Journal Description
Machines
Machines
is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI. The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Mechanical)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.6 (2022);
5-Year Impact Factor:
2.8 (2022)
Latest Articles
Research and Implementation of Pneumatic Amphibious Soft Bionic Robot
Machines 2024, 12(6), 393; https://doi.org/10.3390/machines12060393 - 7 Jun 2024
Abstract
To meet the requirements of amphibious exploration, ocean exploration, and military reconnaissance tasks, a pneumatic amphibious soft bionic robot was developed by taking advantage of the structural characteristics, motion forms, and propulsion mechanisms of the sea lion fore-flippers, inchworms, Carangidae tails, and dolphin
[...] Read more.
To meet the requirements of amphibious exploration, ocean exploration, and military reconnaissance tasks, a pneumatic amphibious soft bionic robot was developed by taking advantage of the structural characteristics, motion forms, and propulsion mechanisms of the sea lion fore-flippers, inchworms, Carangidae tails, and dolphin tails. Using silicone rubber as the main material of the robot, combined with the driving mechanism of the pneumatic soft bionic actuator, and based on the theory of mechanism design, a systematic structural design of the pneumatic amphibious soft bionic robot was carried out from the aspects of flippers, tail, head–neck, and trunk. Then, a numerical simulation algorithm was used to analyze the main executing mechanisms and their coordinated motion performance of the soft bionic robot and to verify the rationality and feasibility of the robot structure design and motion forms. With the use of rapid prototyping technology to complete the construction of the robot prototype body, based on the motion amplitude, frequency, and phase of the bionic prototype, the main execution mechanisms of the robot were controlled through a pneumatic system to carry out experimental testing. The results show that the performance of the robot is consistent with the original design and numerical simulation predictions, and it can achieve certain maneuverability, flexibility, and environmental adaptability. The significance of this work is the development of a pneumatic soft bionic robot suitable for amphibious environments, which provides a new idea for the bionic design and application of pneumatic soft robots.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
Open AccessArticle
Influence of Runner Downstream Structure on the Flow Field in the Runner of Small-Sized Water Turbine
by
Lingdi Tang, Wei Wang, Chenjun Zhang, Zanya Wang and Shouqi Yuan
Machines 2024, 12(6), 392; https://doi.org/10.3390/machines12060392 - 7 Jun 2024
Abstract
Unstable flows in the runner of water turbines, such as reverse flow, vorticity and flow direction transition, are the main factors causing increased losses and decreased efficiency, and changing the geometry structure in the downstream of the runner is an important means of
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Unstable flows in the runner of water turbines, such as reverse flow, vorticity and flow direction transition, are the main factors causing increased losses and decreased efficiency, and changing the geometry structure in the downstream of the runner is an important means of mitigating these instabilities. The different flow fields downstream of runners induced by different locking nut structures are numerically calculated and verified by experimental results. The flow states are evaluated in terms of characteristic quantities such as pressure gradient, swirling flow, reverse flow, and vorticity. The results show a non-negligible effect of the locking nut, which leads to a more uniform pressure distribution, increases the descending speed of the reverse flow rate, and reduces the volume and strength of the vortex. The small locking nut significantly weakens the pressure gradient, reduces the top reverse flow zone, and decreases the vortex volume at the blade flow passage outlet and the size of the downstream disturbance vortex. The extended lock nut reduces the growth rate of the vortex generation rate and the size of the partial vortex, but increases the range of the high-pressure zone, causing the bottom reverse flow and increasing the vortex.
Full article
(This article belongs to the Section Turbomachinery)
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Show Figures
Figure 1
Figure 1
<p>Water turbine structure.</p> Full article ">Figure 2
<p>Downstream structures of the runner. (<b>a</b>) NLN; (<b>b</b>) SLN; (<b>c</b>) ELN.</p> Full article ">Figure 3
<p>Hexahedral mesh of the small-sized water turbine. (<b>a</b>) Water turbine; (<b>b</b>) runner blade; (<b>c</b>) draft tube (ELN).</p> Full article ">Figure 4
<p>Water turbine performance test bench.</p> Full article ">Figure 5
<p>Comparison of numerical predictions of water turbine performance with test results.</p> Full article ">Figure 6
<p>Comparison of numerical hydraulic performance under different locking nuts.</p> Full article ">Figure 7
<p>Blade downstream division diagram.</p> Full article ">Figure 8
<p>Head loss of blade downstream.</p> Full article ">Figure 9
<p>Radial flow field of blade downstream. (<b>a</b>) The blade downstream region with different radial surfaces; (<b>b</b>) position of the blade downstream in relation to the nozzle (fixed position for analysis).</p> Full article ">Figure 10
<p>Radial pressure distributions for NLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 11
<p>Radial pressure distributions for SLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 12
<p>Radial pressure distributions for ELN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 13
<p>Radial streamline of swirling flow for NLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 14
<p>Radial streamline of swirling flow for SLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 15
<p>Radial streamline of swirling flow for ELN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 16
<p>Average pressure variation along the flow direction of the blade downstream. (L is the length from inlet to outlet in the runner in the streamwise direction when the span is 0.5, and <span class="html-italic">l</span> is the distance from the runner inlet in L, <span class="html-italic">l</span>/L = 0 means at the runner inlet, <span class="html-italic">l</span>/L = 1 means at the runner outlet).</p> Full article ">Figure 17
<p>Pressure distribution of the circumferential flow field along the flow direction for NLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 18
<p>Pressure distribution of the circumferential flow field along the flow direction for SLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 19
<p>Pressure distribution of the circumferential flow field along the flow direction for ELN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 20
<p>Mainstream velocity of blade downstream (<span class="html-italic">U</span><sub>1</sub> is the runner inlet circumferential velocity).</p> Full article ">Figure 21
<p>Reverse flow velocity of blade downstream.</p> Full article ">Figure 22
<p>Reverse flow rate of blade downstream.</p> Full article ">Figure 23
<p>Radial velocity distribution of the circumferential flow field along the flow direction for NLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 24
<p>Radial velocity distribution of the circumferential flow field along the flow direction for SLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 25
<p>Radial velocity distribution of the circumferential flow field along the flow direction for ELN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 26
<p>Vortex intensity of the blade downstream.</p> Full article ">Figure 27
<p>Vortex generate rate of the blade downstream.</p> Full article ">Figure 28
<p>Vortex distribution of blade downstream. (<b>a</b>) NLN; (<b>b</b>) SLN; (<b>c</b>) ELN.</p> Full article ">Figure 29
<p>Vortex intensity of the circumferential flow field along the flow direction for NLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 30
<p>Vortex intensity of the circumferential flow field along the flow direction for SLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 31
<p>Vortex intensity of the circumferential flow field along the flow direction for ELN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">
<p>Water turbine structure.</p> Full article ">Figure 2
<p>Downstream structures of the runner. (<b>a</b>) NLN; (<b>b</b>) SLN; (<b>c</b>) ELN.</p> Full article ">Figure 3
<p>Hexahedral mesh of the small-sized water turbine. (<b>a</b>) Water turbine; (<b>b</b>) runner blade; (<b>c</b>) draft tube (ELN).</p> Full article ">Figure 4
<p>Water turbine performance test bench.</p> Full article ">Figure 5
<p>Comparison of numerical predictions of water turbine performance with test results.</p> Full article ">Figure 6
<p>Comparison of numerical hydraulic performance under different locking nuts.</p> Full article ">Figure 7
<p>Blade downstream division diagram.</p> Full article ">Figure 8
<p>Head loss of blade downstream.</p> Full article ">Figure 9
<p>Radial flow field of blade downstream. (<b>a</b>) The blade downstream region with different radial surfaces; (<b>b</b>) position of the blade downstream in relation to the nozzle (fixed position for analysis).</p> Full article ">Figure 10
<p>Radial pressure distributions for NLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 11
<p>Radial pressure distributions for SLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 12
<p>Radial pressure distributions for ELN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 13
<p>Radial streamline of swirling flow for NLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 14
<p>Radial streamline of swirling flow for SLN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 15
<p>Radial streamline of swirling flow for ELN. (<b>a</b>) Span = 0.1; (<b>b</b>) span = 0.5; (<b>c</b>) span = 0.9.</p> Full article ">Figure 16
<p>Average pressure variation along the flow direction of the blade downstream. (L is the length from inlet to outlet in the runner in the streamwise direction when the span is 0.5, and <span class="html-italic">l</span> is the distance from the runner inlet in L, <span class="html-italic">l</span>/L = 0 means at the runner inlet, <span class="html-italic">l</span>/L = 1 means at the runner outlet).</p> Full article ">Figure 17
<p>Pressure distribution of the circumferential flow field along the flow direction for NLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 18
<p>Pressure distribution of the circumferential flow field along the flow direction for SLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 19
<p>Pressure distribution of the circumferential flow field along the flow direction for ELN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 20
<p>Mainstream velocity of blade downstream (<span class="html-italic">U</span><sub>1</sub> is the runner inlet circumferential velocity).</p> Full article ">Figure 21
<p>Reverse flow velocity of blade downstream.</p> Full article ">Figure 22
<p>Reverse flow rate of blade downstream.</p> Full article ">Figure 23
<p>Radial velocity distribution of the circumferential flow field along the flow direction for NLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 24
<p>Radial velocity distribution of the circumferential flow field along the flow direction for SLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 25
<p>Radial velocity distribution of the circumferential flow field along the flow direction for ELN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 26
<p>Vortex intensity of the blade downstream.</p> Full article ">Figure 27
<p>Vortex generate rate of the blade downstream.</p> Full article ">Figure 28
<p>Vortex distribution of blade downstream. (<b>a</b>) NLN; (<b>b</b>) SLN; (<b>c</b>) ELN.</p> Full article ">Figure 29
<p>Vortex intensity of the circumferential flow field along the flow direction for NLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 30
<p>Vortex intensity of the circumferential flow field along the flow direction for SLN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">Figure 31
<p>Vortex intensity of the circumferential flow field along the flow direction for ELN. (<b>a</b>) S1 (<span class="html-italic">l</span>/L = 0); (<b>b</b>) S2 (<span class="html-italic">l</span>/L = 0.5).</p> Full article ">
Open AccessArticle
Digital Simulation of Coupled Dynamic Characteristics of Open Rotor and Dynamic Balancing Test Research
by
Yixiang Guo, Lifang Chen, Yuda Long and Xu Zhang
Machines 2024, 12(6), 391; https://doi.org/10.3390/machines12060391 - 5 Jun 2024
Abstract
An aero engine, as the core power equipment of the aircraft, enables safe and stable operation with a very high reliability index, and is an important guarantee in flight. The open rotor turbine engines (contra-rotating propeller) have stood out as a research hotspot
[...] Read more.
An aero engine, as the core power equipment of the aircraft, enables safe and stable operation with a very high reliability index, and is an important guarantee in flight. The open rotor turbine engines (contra-rotating propeller) have stood out as a research hotspot for aviation power equipment in recent years due to their outstanding advantages of low fuel consumption, high airspeed, and strong propulsion efficiency. Aiming at the problems of vibration exceeding the standard generated by imbalance during the operation of the dual-rotor system of aircraft development, the difficulty of identifying the coupled vibration under the micro-differential speed condition, and the complexity of the dynamic characteristic law, a kind of numerical simulation of the dynamics based on the finite element technology is proposed, together with an experimental research method for the fast and accurate identification of the coupled vibration of the dual-rotor system. Based on the existing open rotor engine structure design to build a simulation test bed, establish a double rotor finite element simulation digital twin model, and analyze and calculate the typical working conditions of the dynamic characteristics of parameters. The advanced algorithm of double rotor coupling vibration signal identification is utilized to carry out decoupling and dynamic balancing experimental tests, comparing the simulation results with the measured data to verify the accuracy of the technical means. The results of the study show that the vibration suppression rate of the finite element calculation simulation test carried out for the simulated double rotor is 98%, and the average vibration reduction ratio of the actual field test at 850 rpm, 1000 rpm, and 3000 rpm is over 50%, which achieves a good dynamic balancing effect, and has the merit of practical engineering application.
Full article
(This article belongs to the Section Electrical Machines and Drives)
Open AccessArticle
Optimization of the Factory Layout and Production Flow Using Production-Simulation-Based Reinforcement Learning
by
Hyekyung Choi, Seokhwan Yu, DongHyun Lee, Sang Do Noh, Sanghoon Ji, Horim Kim, Hyunsik Yoon, Minsu Kwon and Jagyu Han
Machines 2024, 12(6), 390; https://doi.org/10.3390/machines12060390 - 5 Jun 2024
Abstract
Poor layout designs in manufacturing facilities severely reduce production efficiency and increase short- and long-term costs. Analyzing and deriving efficient layouts for novel line designs or improvements to existing lines considering both the layout design and logistics flow is crucial. In this study,
[...] Read more.
Poor layout designs in manufacturing facilities severely reduce production efficiency and increase short- and long-term costs. Analyzing and deriving efficient layouts for novel line designs or improvements to existing lines considering both the layout design and logistics flow is crucial. In this study, we performed production simulation in the design phase for factory layout optimization and used reinforcement learning to derive the optimal factory layout. To facilitate factory-wide layout design, we considered the facility layout, logistics movement paths, and the use of automated guided vehicles (AGVs). The reinforcement-learning process for optimizing each component of the layout was implemented in a multilayer manner, and the optimization results were applied to the design production simulation for verification. Moreover, a flexible simulation system was developed. Users can efficiently review and execute alternative scenarios by considering both facility and logistics layouts in the workspace. By emphasizing the redesign and reuse of the simulation model, we achieved layout optimization through an automated process and propose a flexible simulation system that can adapt to various environments through a multilayered modular approach. By adjusting weights and considering various conditions, throughput increased by 0.3%, logistics movement distance was reduced by 3.8%, and the number of AGVs required was reduced by 11%.
Full article
(This article belongs to the Special Issue Digital Twin-Driven Smart Production, Logistics, and Supply Chains)
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Figure 1
Figure 1
<p>Production-simulation-based factory layout optimization framework.</p> Full article ">Figure 2
<p>Production-simulation-based factory layout analyses sub-framework.</p> Full article ">Figure 3
<p>System optimization UML class diagram.</p> Full article ">Figure 4
<p>Production-simulation-based factory layout optimization processor.</p> Full article ">Figure 5
<p>Factory layout optimization simulation model.</p> Full article ">Figure 6
<p>Simulation output: facility and AGV utilization reports.</p> Full article ">Figure 7
<p>Initial factory layout diagram.</p> Full article ">Figure 8
<p>Case 1 factory layout diagram.</p> Full article ">Figure 9
<p>Case 2 factory layout diagram.</p> Full article ">Figure 10
<p>Case 3 factory layout diagram.</p> Full article ">
<p>Production-simulation-based factory layout optimization framework.</p> Full article ">Figure 2
<p>Production-simulation-based factory layout analyses sub-framework.</p> Full article ">Figure 3
<p>System optimization UML class diagram.</p> Full article ">Figure 4
<p>Production-simulation-based factory layout optimization processor.</p> Full article ">Figure 5
<p>Factory layout optimization simulation model.</p> Full article ">Figure 6
<p>Simulation output: facility and AGV utilization reports.</p> Full article ">Figure 7
<p>Initial factory layout diagram.</p> Full article ">Figure 8
<p>Case 1 factory layout diagram.</p> Full article ">Figure 9
<p>Case 2 factory layout diagram.</p> Full article ">Figure 10
<p>Case 3 factory layout diagram.</p> Full article ">
Open AccessArticle
Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors
by
Dafeng Tang, Yuanbo Xu and Xiaojun Liu
Machines 2024, 12(6), 389; https://doi.org/10.3390/machines12060389 - 5 Jun 2024
Abstract
The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an
[...] Read more.
The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
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Show Figures
Figure 1
Figure 1
<p>Sub-components. (<b>a</b>) Cyclic fault impulses, (<b>b</b>) period interference, (<b>c</b>) random impulse noise, and (<b>d</b>) background noise.</p> Full article ">Figure 2
<p>Signal mixture. (<b>a</b>) Waveform and (<b>b</b>) envelope spectrum.</p> Full article ">Figure 3
<p>ILoG-enhanced signal. (<b>a</b>) Time-domain signal and (<b>b</b>) envelope spectrum of the enhanced signal.</p> Full article ">Figure 4
<p>MLoG-enhanced signal. (<b>a</b>) Time-domain signal and (<b>b</b>) envelope spectrum of the MLoG-enhanced signal.</p> Full article ">Figure 5
<p>MED-enhanced signal. (<b>a</b>) Time-domain signal and (<b>b</b>) envelope spectrum of the ILoG-enhanced signal.</p> Full article ">Figure 6
<p>MFS-LT test rig.</p> Full article ">Figure 7
<p>Inner fault signal with additional impulse noise. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 8
<p>ILoG-enhanced signal. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 9
<p>CYCBD-enhanced signal. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 10
<p>Vibrating screen test bench.</p> Full article ">Figure 11
<p>Outer ring fault signal. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 12
<p>ILoG-enhanced signal. (<b>a</b>) Waveform and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 13
<p>α-stable-filtered signal.</p> Full article ">Figure 14
<p>Decomposed signals.</p> Full article ">Figure 15
<p>Final enhanced signal. (<b>a</b>) Waveform and (<b>b</b>) its envelope spectrum.</p> Full article ">
<p>Sub-components. (<b>a</b>) Cyclic fault impulses, (<b>b</b>) period interference, (<b>c</b>) random impulse noise, and (<b>d</b>) background noise.</p> Full article ">Figure 2
<p>Signal mixture. (<b>a</b>) Waveform and (<b>b</b>) envelope spectrum.</p> Full article ">Figure 3
<p>ILoG-enhanced signal. (<b>a</b>) Time-domain signal and (<b>b</b>) envelope spectrum of the enhanced signal.</p> Full article ">Figure 4
<p>MLoG-enhanced signal. (<b>a</b>) Time-domain signal and (<b>b</b>) envelope spectrum of the MLoG-enhanced signal.</p> Full article ">Figure 5
<p>MED-enhanced signal. (<b>a</b>) Time-domain signal and (<b>b</b>) envelope spectrum of the ILoG-enhanced signal.</p> Full article ">Figure 6
<p>MFS-LT test rig.</p> Full article ">Figure 7
<p>Inner fault signal with additional impulse noise. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 8
<p>ILoG-enhanced signal. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 9
<p>CYCBD-enhanced signal. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 10
<p>Vibrating screen test bench.</p> Full article ">Figure 11
<p>Outer ring fault signal. (<b>a</b>) Time domain and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 12
<p>ILoG-enhanced signal. (<b>a</b>) Waveform and (<b>b</b>) its envelope spectrum.</p> Full article ">Figure 13
<p>α-stable-filtered signal.</p> Full article ">Figure 14
<p>Decomposed signals.</p> Full article ">Figure 15
<p>Final enhanced signal. (<b>a</b>) Waveform and (<b>b</b>) its envelope spectrum.</p> Full article ">
Open AccessArticle
Preliminary Testing of a Passive Exoskeleton Prototype Based on McKibben Muscles
by
Maria Paterna, Carlo De Benedictis and Carlo Ferraresi
Machines 2024, 12(6), 388; https://doi.org/10.3390/machines12060388 - 5 Jun 2024
Abstract
Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this
[...] Read more.
Upper-limb exoskeletons for industrial applications can enhance the comfort and productivity of workers by reducing muscle activity and intra-articular forces during overhead work. Current devices typically employ a spring-based mechanism to balance the gravitational torque acting on the shoulder. As an alternative, this paper presents the design of a passive upper-limb exoskeleton based on McKibben artificial muscles. The interaction forces between the exoskeleton and the user, as well as the mechanical resistance of the exoskeleton structure, were investigated to finalize the design of the device prior to its prototyping. Details are provided about the solutions adopted to assemble, wear, and regulate the exoskeleton’s structure. The first version of the device weighing about 5.5 kg was manufactured and tested by two users in a motion analysis laboratory. The results of this study highlight that the exoskeleton can effectively reduce the activation level of shoulder muscles without affecting the lumbar strain.
Full article
(This article belongs to the Special Issue Intelligent Bio-Inspired Robots: New Trends and Future Perspectives)
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Show Figures
Figure 1
Figure 1
<p>Exoskeleton architecture.</p> Full article ">Figure 2
<p>Geometrical parameters of the exoskeleton model.</p> Full article ">Figure 3
<p>A 3D scheme of the forces acting on the (<b>a</b>) exo-arm and (<b>b</b>) back frame.</p> Full article ">Figure 4
<p>FEM analysis boundary condition: (<b>a</b>) exo-arm; (<b>b</b>) back frame.</p> Full article ">Figure 5
<p>(<b>a</b>) Back, (<b>b</b>) front, and (<b>c</b>) right side view of the marker set.</p> Full article ">Figure 6
<p>A representative recording of the acquired signals in (<b>a</b>) static and (<b>b</b>) dynamic tasks.</p> Full article ">Figure 7
<p>Contact pressures vs shoulder flexion angle on the (<b>a</b>) upper arm, (<b>b</b>) chest, and (<b>c</b>) pelvis, by selecting different abduction angles. The black lines represent the pain detection thresholds for each body area.</p> Full article ">Figure 8
<p>Von Mises stresses in stainless steel and aluminum components and maximum normal stress in the fiber direction of the PLA components. All stresses are in MPa.</p> Full article ">Figure 9
<p>Displacements in the mechanical structure: (<b>a</b>) displacement in the anteroposterior direction of the exo-arm; (<b>b</b>) displacement in the cranio-caudal direction of the exo-arm; (<b>c</b>) displacement magnitude of the back frame represented with a scale of 20. All data are in mm.</p> Full article ">Figure 10
<p>Muscles’ ARV percentage variation between FREE and EXO trials of subject 1 (blue bars) and subject 2 (red bars): (<b>a</b>) static task of the left arm; (<b>b</b>) static task of the right arm; (<b>c</b>) dynamic task without tool; (<b>d</b>) dynamic task with tool. Negative values indicate decreased muscular activity in the EXO sessions.</p> Full article ">Figure 11
<p>CoP<sub>rms</sub> percentage variation between FREE and EXO trials of subject 1 (blue bars) and subject 2 (red bars): (<b>a</b>) static task; (<b>b</b>) dynamic task with tool; (<b>c</b>) dynamic task without tool. Positive values indicate increased CoP displacement in EXO sessions.</p> Full article ">
<p>Exoskeleton architecture.</p> Full article ">Figure 2
<p>Geometrical parameters of the exoskeleton model.</p> Full article ">Figure 3
<p>A 3D scheme of the forces acting on the (<b>a</b>) exo-arm and (<b>b</b>) back frame.</p> Full article ">Figure 4
<p>FEM analysis boundary condition: (<b>a</b>) exo-arm; (<b>b</b>) back frame.</p> Full article ">Figure 5
<p>(<b>a</b>) Back, (<b>b</b>) front, and (<b>c</b>) right side view of the marker set.</p> Full article ">Figure 6
<p>A representative recording of the acquired signals in (<b>a</b>) static and (<b>b</b>) dynamic tasks.</p> Full article ">Figure 7
<p>Contact pressures vs shoulder flexion angle on the (<b>a</b>) upper arm, (<b>b</b>) chest, and (<b>c</b>) pelvis, by selecting different abduction angles. The black lines represent the pain detection thresholds for each body area.</p> Full article ">Figure 8
<p>Von Mises stresses in stainless steel and aluminum components and maximum normal stress in the fiber direction of the PLA components. All stresses are in MPa.</p> Full article ">Figure 9
<p>Displacements in the mechanical structure: (<b>a</b>) displacement in the anteroposterior direction of the exo-arm; (<b>b</b>) displacement in the cranio-caudal direction of the exo-arm; (<b>c</b>) displacement magnitude of the back frame represented with a scale of 20. All data are in mm.</p> Full article ">Figure 10
<p>Muscles’ ARV percentage variation between FREE and EXO trials of subject 1 (blue bars) and subject 2 (red bars): (<b>a</b>) static task of the left arm; (<b>b</b>) static task of the right arm; (<b>c</b>) dynamic task without tool; (<b>d</b>) dynamic task with tool. Negative values indicate decreased muscular activity in the EXO sessions.</p> Full article ">Figure 11
<p>CoP<sub>rms</sub> percentage variation between FREE and EXO trials of subject 1 (blue bars) and subject 2 (red bars): (<b>a</b>) static task; (<b>b</b>) dynamic task with tool; (<b>c</b>) dynamic task without tool. Positive values indicate increased CoP displacement in EXO sessions.</p> Full article ">
Open AccessArticle
Research on Collaboration Motion Planning Method for a Dual-Arm Robot Based on Closed-Chain Kinematics
by
Yuantian Qin, Kai Zhang, Kuiquan Meng and Zhehang Yin
Machines 2024, 12(6), 387; https://doi.org/10.3390/machines12060387 - 4 Jun 2024
Abstract
Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established
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Aiming to address challenges in the motion coordination of dual-arm robot engineering applications, a comprehensive set of planning methods is devised. This paper takes a dual-arm system composed of two six-degrees-of-freedom industrial robots as the research object. Initially, a transformation model is established for the characteristic trajectories between the workpiece coordinate system and various other coordinate systems. Subsequently, the position and orientation curves of the working trajectory are discretized to facilitate the controller’s execution. Furthermore, an analysis is conducted of the closed-chain kinematics relationship between two arms of the robot and a pose-calibration method based on a reference coordinate system is introduced. Finally, constraints to the collaborative motion of the dual-arm robot are analyzed, leading to the establishment of a motion collaboration planning methodology. Simulations and experiments demonstrate that the proposed approach enables effective and collaborative task planning for dual-arm robots. Moreover, joint angle and angular velocity curves corresponding to the motion trajectory exhibit smoothness, reducing joint impacts.
Full article
(This article belongs to the Section Automation and Control Systems)
Open AccessArticle
Analysis of Vibration Characteristics and Influencing Factors of Complex Tread Pattern Tires Based on Finite Element Method
by
Mengdi Xu, Yunfei Ge, Xianbin Du and Zhaohong Meng
Machines 2024, 12(6), 386; https://doi.org/10.3390/machines12060386 - 4 Jun 2024
Abstract
The vibration of the tires significantly impacts a vehicle’s ride comfort and noise level; however, the current analysis of tire vibration characteristics often involves excessive simplification in their models, leading to a reduction in model accuracy. To analyze the tire vibrational properties and
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The vibration of the tires significantly impacts a vehicle’s ride comfort and noise level; however, the current analysis of tire vibration characteristics often involves excessive simplification in their models, leading to a reduction in model accuracy. To analyze the tire vibrational properties and the influence of its design and service conditions, a combined modeling technology was developed to construct a three-dimensional (3D) finite element model of a 205/55R16 specification radial tire with intricate tread patterns. The accuracy and reliability of the simulation model was verified through vibration modal tests. Based on the vibration mode theory, the Lanczos method provided by ABAQUS was adopted to analyze the modal characteristics of the tire under free inflation and grounded conditions, and the effects of different inflation pressures, loads, operating conditions, and belt cord angles on the tire vibration characteristics were analyzed. The results indicate that grounding constraints will suppress the low order radial modal frequency of the tire and enhance the lateral modal frequency. The higher the order of the tire vibration mode, the greater the impact of inflation pressure. As the operating conditions change, the modal frequencies of all directions have the same trend of change, and as the ground load increases, the tire is prone to misalignment at lower lateral frequencies. The radial and lateral grounding modes of the tire are slightly affected by the change of the cord angle in the belt layer, but the circumferential grounding frequency decreases as the belt layer angle increases. These research findings offer a crucial foundation for the structural design of complex tread pattern tires, and also serve as a reference for addressing vibration and comfort issues encountered in the tire matching process.
Full article
(This article belongs to the Section Machine Design and Theory)
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Figure 1
<p>Tire modeling process.</p> Full article ">Figure 2
<p>205/55R16 DS tire structure diagram.</p> Full article ">Figure 3
<p>Tire modal test.</p> Full article ">Figure 4
<p>Test and simulation results of the first five orders of free radial vibration patterns.</p> Full article ">Figure 5
<p>Comparison between experimental and simulated radial free modes.</p> Full article ">Figure 6
<p>The first five radial grounded vibration modes.</p> Full article ">Figure 7
<p>First five orders of lateral free vibration and grounded vibration pattern.</p> Full article ">Figure 8
<p>Comparison of modal frequencies for different cases.</p> Full article ">Figure 9
<p>The influence of pressure changes on the frequency of (<b>a</b>) free and (<b>b</b>) grounded mode vibration.</p> Full article ">Figure 10
<p>The influence of load on (<b>a</b>) radial and (<b>b</b>) lateral vibration frequencies.</p> Full article ">Figure 11
<p>The influence of load on circumferential vibration frequencies.</p> Full article ">Figure 12
<p>The influence of operational conditions on (<b>a</b>) radial and (<b>b</b>) lateral vibration frequencies.</p> Full article ">Figure 13
<p>The influence of operational conditions on circumferential vibration frequencies.</p> Full article ">Figure 14
<p>The influence of the belt angle on (<b>a</b>) radial and (<b>b</b>) lateral vibration frequencies.</p> Full article ">Figure 15
<p>The influence of the belt angle on circumferential vibration frequencies.</p> Full article ">
<p>Tire modeling process.</p> Full article ">Figure 2
<p>205/55R16 DS tire structure diagram.</p> Full article ">Figure 3
<p>Tire modal test.</p> Full article ">Figure 4
<p>Test and simulation results of the first five orders of free radial vibration patterns.</p> Full article ">Figure 5
<p>Comparison between experimental and simulated radial free modes.</p> Full article ">Figure 6
<p>The first five radial grounded vibration modes.</p> Full article ">Figure 7
<p>First five orders of lateral free vibration and grounded vibration pattern.</p> Full article ">Figure 8
<p>Comparison of modal frequencies for different cases.</p> Full article ">Figure 9
<p>The influence of pressure changes on the frequency of (<b>a</b>) free and (<b>b</b>) grounded mode vibration.</p> Full article ">Figure 10
<p>The influence of load on (<b>a</b>) radial and (<b>b</b>) lateral vibration frequencies.</p> Full article ">Figure 11
<p>The influence of load on circumferential vibration frequencies.</p> Full article ">Figure 12
<p>The influence of operational conditions on (<b>a</b>) radial and (<b>b</b>) lateral vibration frequencies.</p> Full article ">Figure 13
<p>The influence of operational conditions on circumferential vibration frequencies.</p> Full article ">Figure 14
<p>The influence of the belt angle on (<b>a</b>) radial and (<b>b</b>) lateral vibration frequencies.</p> Full article ">Figure 15
<p>The influence of the belt angle on circumferential vibration frequencies.</p> Full article ">
Open AccessArticle
Development of a Multi-Robot System for Pier Construction
by
Hyo-Gon Kim, Ji-Hyun Park, Jong-Chan Kim, Jeong-Hwan Hwang, Jeong-Woo Park, In-Gyu Park, Hyo-Jun Lee, Kyoungseok Noh, Young-Ho Choi and Jin-Ho Suh
Machines 2024, 12(6), 385; https://doi.org/10.3390/machines12060385 - 4 Jun 2024
Abstract
The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support
[...] Read more.
The construction industry is a challenging field for the application of robots. In particular, bridge construction, which involves many tasks at great heights, makes it difficult to implement robots. To construct a bridge, it is necessary to build numerous piers that can support the bridge deck. Pier construction involves a series of tasks including rebar connection, formwork installation, concrete pouring, formwork dismantling, and formwork reinstallation. These activities require working at heights, presenting a significant risk of falls. If bridge construction could be performed remotely using robots instead of relying on human labor, it would greatly contribute to the safety of bridge construction. This paper proposes a multi-robot system capable of remote operation and automation for rebar structure connection, concrete pouring, and concrete vibrating tasks in pier construction. The proposed multi-robot system for pier construction is composed of three robot systems. Each robot system consists of a robot arm mounted on a mobile robot that can move along rails. And to apply the proposed system to a construction site, it is essential to implement a compliance control algorithm that adapts to external forces. In this paper, we propose an admittance control that takes into account the weight of the tool for the compliance control of the proposed robot, which performs tasks by switching between various construction tools of different weights. Furthermore, we propose a synchronization control method for the multi-robot system to connect reinforcing structures. We validated the proposed algorithm through simulation. Furthermore, we developed a prototype of the proposed system to verify the feasibility of the suggested hardware design and control.
Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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<p>Design model of the multi-robot system for pier construction.</p> Full article ">Figure 2
<p>Conceptual diagram of installing the multi-robot system for pier construction on the automatic climbing formwork system.</p> Full article ">Figure 3
<p>Rail drive mechanism of the mobile platform.</p> Full article ">Figure 4
<p>Position sensing mechanism of the mobile platform.</p> Full article ">Figure 5
<p>Block diagram of admittance control for a commercial manipulator.</p> Full article ">Figure 6
<p>Diagram of frames related to admittance control.</p> Full article ">Figure 7
<p>Coordinate systems related to synchronization control.</p> Full article ">Figure 8
<p>Simulation environment implemented using RecurDyn and MATLAB.</p> Full article ">Figure 9
<p>The simulation environment implemented with the RecurDyn simulator.</p> Full article ">Figure 10
<p>Simulation results; (<b>a</b>) rebar mesh position, (<b>b</b>) force received by the robot from the reinforcement rebar mesh.</p> Full article ">Figure 11
<p>Prototype of the mobile manipulation system for pier construction.</p> Full article ">Figure 12
<p>Rail constraint of the prototype of the mobile manipulation system.</p> Full article ">Figure 13
<p>Graphs of the position during movement.</p> Full article ">Figure 14
<p>Graphs of the velocity during movement.</p> Full article ">Figure 15
<p>Admittance control test (pitch 90°, 10 kg load).</p> Full article ">Figure 16
<p>Admittance control test (pitch 45°, 10 kg load).</p> Full article ">Figure 17
<p>Admittance control test (arbitrary external force).</p> Full article ">Figure 18
<p>Tool change automation test scene.</p> Full article ">Figure 19
<p>Scene of synchronization control test including admittance control.</p> Full article ">
<p>Design model of the multi-robot system for pier construction.</p> Full article ">Figure 2
<p>Conceptual diagram of installing the multi-robot system for pier construction on the automatic climbing formwork system.</p> Full article ">Figure 3
<p>Rail drive mechanism of the mobile platform.</p> Full article ">Figure 4
<p>Position sensing mechanism of the mobile platform.</p> Full article ">Figure 5
<p>Block diagram of admittance control for a commercial manipulator.</p> Full article ">Figure 6
<p>Diagram of frames related to admittance control.</p> Full article ">Figure 7
<p>Coordinate systems related to synchronization control.</p> Full article ">Figure 8
<p>Simulation environment implemented using RecurDyn and MATLAB.</p> Full article ">Figure 9
<p>The simulation environment implemented with the RecurDyn simulator.</p> Full article ">Figure 10
<p>Simulation results; (<b>a</b>) rebar mesh position, (<b>b</b>) force received by the robot from the reinforcement rebar mesh.</p> Full article ">Figure 11
<p>Prototype of the mobile manipulation system for pier construction.</p> Full article ">Figure 12
<p>Rail constraint of the prototype of the mobile manipulation system.</p> Full article ">Figure 13
<p>Graphs of the position during movement.</p> Full article ">Figure 14
<p>Graphs of the velocity during movement.</p> Full article ">Figure 15
<p>Admittance control test (pitch 90°, 10 kg load).</p> Full article ">Figure 16
<p>Admittance control test (pitch 45°, 10 kg load).</p> Full article ">Figure 17
<p>Admittance control test (arbitrary external force).</p> Full article ">Figure 18
<p>Tool change automation test scene.</p> Full article ">Figure 19
<p>Scene of synchronization control test including admittance control.</p> Full article ">
Open AccessArticle
A Thorough Procedure to Design Surface-Mounted Permanent Magnet Synchronous Generators
by
Gustavo Garbelini de Menezes, Narco Afonso Ravazzoli Maciejewski, Elissa Soares de Carvalho and Thiago de Paula Machado Bazzo
Machines 2024, 12(6), 384; https://doi.org/10.3390/machines12060384 - 4 Jun 2024
Abstract
This paper sets forth a thorough procedure to design surface-mounted permanent magnet synchronous generators. Since synchronous generators generate the majority of electrical energy, their relevance in society nowadays is substantial. As a consequence, the methodology to design these electrical machines also holds great
[...] Read more.
This paper sets forth a thorough procedure to design surface-mounted permanent magnet synchronous generators. Since synchronous generators generate the majority of electrical energy, their relevance in society nowadays is substantial. As a consequence, the methodology to design these electrical machines also holds great importance. However, even though a considerable amount of works addresses the matter, it is difficult to find a complete and thoroughly explained design procedure. The proposed method is based on analytical equations to fully consider PM generator fundamentals with a few simplifications, which implies in a considerable number of design equations and parameters. Differently from most papers on the design of PM synchronous generators, a significant level of detail and explanation is presented, all design choices are discussed, and the suggested ranges for the design parameters are shown. This results in a straightforward procedure that allows non-experienced designers to easily replicate the results and effectively enhance the comprehension of permanent magnet synchronous machines, and provides a guideline for researchers from other fields who may need to understand and perform a synchronous generator design. To show the effectiveness of the proposed design procedure, a PM generator is designed, and the results are compared with a finite element simulation, showing good accuracy.
Full article
(This article belongs to the Special Issue Research in Design and Analysis of Permanent Magnet Synchronous Machines)
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Figure 1
Figure 1
<p>Main geometric parameters of the generator: (<b>a</b>) diameters and air gap, (<b>b</b>) main angles and stator yoke, (<b>c</b>) PM pole dimensions, and (<b>d</b>) slot dimensions.</p> Full article ">Figure 1 Cont.
<p>Main geometric parameters of the generator: (<b>a</b>) diameters and air gap, (<b>b</b>) main angles and stator yoke, (<b>c</b>) PM pole dimensions, and (<b>d</b>) slot dimensions.</p> Full article ">Figure 2
<p>Flow chart of the proposed PMSG design procedure. The numbers alongside each block correspond to the equation numbers in <a href="#sec2dot3-machines-12-00384" class="html-sec">Section 2.3</a>.</p> Full article ">Figure 3
<p>Phasor diagram for non-salient pole PMSGs.</p> Full article ">Figure 4
<p>Demagnetization curve of the N42SH at 120 °C.</p> Full article ">Figure 5
<p>Designed permanent magnet generator.</p> Full article ">Figure 6
<p>Finite element solution of the magnetic field and flux density distribution under no-load operation.</p> Full article ">Figure 7
<p>Finite element solution of the magnetic field and flux density distribution under full-load operation.</p> Full article ">Figure 8
<p>Full-load phase-to-phase voltages.</p> Full article ">Figure 9
<p>Full-load armature currents.</p> Full article ">
<p>Main geometric parameters of the generator: (<b>a</b>) diameters and air gap, (<b>b</b>) main angles and stator yoke, (<b>c</b>) PM pole dimensions, and (<b>d</b>) slot dimensions.</p> Full article ">Figure 1 Cont.
<p>Main geometric parameters of the generator: (<b>a</b>) diameters and air gap, (<b>b</b>) main angles and stator yoke, (<b>c</b>) PM pole dimensions, and (<b>d</b>) slot dimensions.</p> Full article ">Figure 2
<p>Flow chart of the proposed PMSG design procedure. The numbers alongside each block correspond to the equation numbers in <a href="#sec2dot3-machines-12-00384" class="html-sec">Section 2.3</a>.</p> Full article ">Figure 3
<p>Phasor diagram for non-salient pole PMSGs.</p> Full article ">Figure 4
<p>Demagnetization curve of the N42SH at 120 °C.</p> Full article ">Figure 5
<p>Designed permanent magnet generator.</p> Full article ">Figure 6
<p>Finite element solution of the magnetic field and flux density distribution under no-load operation.</p> Full article ">Figure 7
<p>Finite element solution of the magnetic field and flux density distribution under full-load operation.</p> Full article ">Figure 8
<p>Full-load phase-to-phase voltages.</p> Full article ">Figure 9
<p>Full-load armature currents.</p> Full article ">
Open AccessArticle
An Improved Fault Diagnosis Method for Rolling Bearings Based on 1D_CNN Considering Noise and Working Condition Interference
by
Kai Huang, Linbo Zhu, Zhijun Ren, Tantao Lin, Li Zeng, Jin Wan and Yongsheng Zhu
Machines 2024, 12(6), 383; https://doi.org/10.3390/machines12060383 - 3 Jun 2024
Abstract
Rolling bearings are prone to failure due to the complexity and serious operational environment of rotating equipment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has become an effective tool to ensure the reliable operation of rolling bearings. However, interference caused by
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Rolling bearings are prone to failure due to the complexity and serious operational environment of rotating equipment. Intelligent fault diagnosis based on convolutional neural networks (CNNs) has become an effective tool to ensure the reliable operation of rolling bearings. However, interference caused by environmental noise and variable working conditions can affect the data. To solve this problem, we propose an improved fault diagnosis method called deep convolutional neural network based on multi-scale features and mutual information (MMDCNN). In our approach, a multi-scale convolutional layer is placed at the front end of a 1D_CNN to maximize the retention of the multi-scale initial features. Meanwhile, the key fault features are further enhanced adaptively by introducing a self-attention mechanism. Then, the composite loss function is constructed by maximizing mutual information as an auxiliary loss based on cross-entropy loss; thus, the proposed method can extract robust fault features with high generalization performance. To demonstrate the superiority of MMDCNN, we compared the performance of our scheme with several existing deep learning models on two datasets. The results show that the proposed model successfully achieves bearing fault diagnosis with interference from noise and variable working conditions, possessing a powerful fault feature extraction capability.
Full article
(This article belongs to the Section Machines Testing and Maintenance)
Open AccessArticle
Establishment and Analysis of Load Spectrum for Bogie Frame of High-Speed Train at 400 km/h Speed Level
by
Guidong Tao, Zhiming Liu, Chengxiang Ji and Guangxue Yang
Machines 2024, 12(6), 382; https://doi.org/10.3390/machines12060382 - 3 Jun 2024
Abstract
The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs,
[...] Read more.
The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, especially when the design and operational speeds exceed 400 km/h, the applicability of current international standards is uncertain. The load spectrum serves as the foundation for structural reliability design and fatigue evaluation. In this paper, the measured loads of the bogie frame of a CR400AF high-speed train on the Beijing–Shanghai high-speed railway are obtained, and the time-domain characteristic of the measured loads is analyzed under different operating conditions. Then, through the Weibull distribution of three parameters, the Weibull parameters at the 450 km/h speed level are fitted, and the maximum load and cumulative frequency under the speed level are derived. Finally, the load spectrum of the bogie frame at the 450 km/h speed level is established, which provides a more realistic load condition for accurately evaluating the fatigue strength of bogie frames at higher speed levels.
Full article
(This article belongs to the Section Vehicle Engineering)
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Figure 1
<p>Schematic diagram of the load series of a power bogie frame.</p> Full article ">Figure 2
<p>Bouncing load series and roll load series of the bogie frame.</p> Full article ">Figure 3
<p>Bending cell with axial load compensation.</p> Full article ">Figure 4
<p>Bogie frame static calibration test.</p> Full article ">Figure 5
<p>Beijing–Shanghai high-speed railway route map.</p> Full article ">Figure 6
<p>Time history of the vertical load between two stations.</p> Full article ">Figure 7
<p>Time history of the transverse load between two stations.</p> Full article ">Figure 8
<p>Load spectra of different speed classes under straight line conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 9
<p>Load spectra of different velocity classes under curve conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 10
<p>Load spectra of different speed classes under acceleration conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 11
<p>Load spectra of different speed classes under deceleration conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 12
<p>Equivalent load under different operation conditions. (<b>a</b>) Constant speed operation on a straight line. (<b>b</b>) Constant speed operation on a curved line. (<b>c</b>) Acceleration operation. (<b>d</b>) Deceleration operation.</p> Full article ">Figure 13
<p>Relationship of the Weibull fitting parameters and speed of the bouncing load spectrum. (<b>a</b>) Constant speed operation on a straight line. (<b>b</b>) Constant speed operation on a curved line. (<b>c</b>) Acceleration operation. (<b>d</b>) Deceleration operation.</p> Full article ">Figure 14
<p>Cumulative load spectrum at a 450 km/h speed. (<b>a</b>) Constant speed operation on a straight line. (<b>b</b>) Constant speed operation on a curved line. (<b>c</b>) Acceleration operation. (<b>d</b>) Deceleration operation.</p> Full article ">
<p>Schematic diagram of the load series of a power bogie frame.</p> Full article ">Figure 2
<p>Bouncing load series and roll load series of the bogie frame.</p> Full article ">Figure 3
<p>Bending cell with axial load compensation.</p> Full article ">Figure 4
<p>Bogie frame static calibration test.</p> Full article ">Figure 5
<p>Beijing–Shanghai high-speed railway route map.</p> Full article ">Figure 6
<p>Time history of the vertical load between two stations.</p> Full article ">Figure 7
<p>Time history of the transverse load between two stations.</p> Full article ">Figure 8
<p>Load spectra of different speed classes under straight line conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 9
<p>Load spectra of different velocity classes under curve conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 10
<p>Load spectra of different speed classes under acceleration conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 11
<p>Load spectra of different speed classes under deceleration conditions. (<b>a</b>) Bouncing load spectrum. (<b>b</b>) Roll load spectrum. (<b>c</b>) Transverse load spectrum.</p> Full article ">Figure 12
<p>Equivalent load under different operation conditions. (<b>a</b>) Constant speed operation on a straight line. (<b>b</b>) Constant speed operation on a curved line. (<b>c</b>) Acceleration operation. (<b>d</b>) Deceleration operation.</p> Full article ">Figure 13
<p>Relationship of the Weibull fitting parameters and speed of the bouncing load spectrum. (<b>a</b>) Constant speed operation on a straight line. (<b>b</b>) Constant speed operation on a curved line. (<b>c</b>) Acceleration operation. (<b>d</b>) Deceleration operation.</p> Full article ">Figure 14
<p>Cumulative load spectrum at a 450 km/h speed. (<b>a</b>) Constant speed operation on a straight line. (<b>b</b>) Constant speed operation on a curved line. (<b>c</b>) Acceleration operation. (<b>d</b>) Deceleration operation.</p> Full article ">
Open AccessArticle
Flexspline Pitch Deviation Rapid Measurement Method Using Offset Point Laser Sensors
by
Xiaoyi Wang, Kunlei Zheng, Longyuan Xiao, Chengxiang Zhao, Mingkang Liu, Dongjie Zhu, Tianyang Yao and Zhaoyao Shi
Machines 2024, 12(6), 381; https://doi.org/10.3390/machines12060381 - 3 Jun 2024
Abstract
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Flexsplines in harmonic gear reducers are usually characterized by a large number of teeth, small modulus, and poor stiffness, which makes them difficult to measure using conventional gear measuring centers. In order to efficiently evaluate the quality of flexsplines in harmonic gear reducers,
[...] Read more.
Flexsplines in harmonic gear reducers are usually characterized by a large number of teeth, small modulus, and poor stiffness, which makes them difficult to measure using conventional gear measuring centers. In order to efficiently evaluate the quality of flexsplines in harmonic gear reducers, a rapid measurement method for flexspline pitch using offset point laser sensors (PLS) is proposed. This paper investigates the principle of measuring the tooth flank of the flexspline under the offset of the PLS, establishes a model for collecting and analyzing gear surface data, builds an experimental system, calibrates the six pose parameters of the sensor using the geometric features of the flexspline’s outer circular surface, and completes the reconstruction of the left and right gear surfaces of the flexspline based on the measured data. In the experiment, the gear surface obtained by the proposed method is largely consistent with that measured by the video imaging method, and the repeatability of both single pitch deviation and cumulative pitch deviation is within ±3 µm.
Full article
Figure 1
Figure 1
<p>Mapping relationship between coordinates.</p> Full article ">Figure 2
<p>Calibration of pitch angle <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Calibration of the yaw angle <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>z</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Calibration of sensor offset distances.</p> Full article ">Figure 5
<p>Simulated gear tooth flanks.</p> Full article ">Figure 6
<p>Virtual measurement.</p> Full article ">Figure 7
<p>Virtual measurement data of sensors. (<b>a</b>) Virtual measurement data for the left teeth flanks. (<b>b</b>) Virtual measurement data for the right teeth flanks.</p> Full article ">Figure 8
<p>Reconstructed teeth flanks and theoretical teeth flanks. (<b>a</b>) Reconstructed and theoretical teeth flanks. (<b>b</b>) Theoretical teeth flanks.</p> Full article ">Figure 9
<p>Experimental setup. (<b>a</b>) Experimental setup diagram. (<b>b</b>) Partial schematic diagram.</p> Full article ">Figure 10
<p>Data of tooth flank measured by PLS.</p> Full article ">Figure 11
<p>Description of the measurement area. (<b>a</b>) Measuring area of the flexspline gear surface. (<b>b</b>) Measuring position of the flexspline gear surface.</p> Full article ">Figure 12
<p>Reconstruction of teeth flank data. (<b>a</b>) reconstructed teeth flanks of flexspline. (<b>b</b>) partially reconstructed teeth flanks of flexspline.</p> Full article ">Figure 13
<p>Comparison of reconstruction of teeth flank data with teeth flank data obtained by video measuring method. (<b>a</b>) Overall comparison of video measuring method with reconstructed teeth flanks. (<b>b</b>) Partial comparison of the video measuring method with the reconstructed teeth flanks. (<b>c</b>) Partially reconstructed teeth flanks.</p> Full article ">Figure 14
<p>Single pitch deviations and total cumulative pitch deviations of 5 time measurements. (<b>a</b>) single pitch deviations. (<b>b</b>) total cumulative pitch deviations.</p> Full article ">Figure 15
<p>The range of individual single pitch deviation in the repeated measurements.</p> Full article ">
<p>Mapping relationship between coordinates.</p> Full article ">Figure 2
<p>Calibration of pitch angle <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>x</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Calibration of the yaw angle <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>z</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Calibration of sensor offset distances.</p> Full article ">Figure 5
<p>Simulated gear tooth flanks.</p> Full article ">Figure 6
<p>Virtual measurement.</p> Full article ">Figure 7
<p>Virtual measurement data of sensors. (<b>a</b>) Virtual measurement data for the left teeth flanks. (<b>b</b>) Virtual measurement data for the right teeth flanks.</p> Full article ">Figure 8
<p>Reconstructed teeth flanks and theoretical teeth flanks. (<b>a</b>) Reconstructed and theoretical teeth flanks. (<b>b</b>) Theoretical teeth flanks.</p> Full article ">Figure 9
<p>Experimental setup. (<b>a</b>) Experimental setup diagram. (<b>b</b>) Partial schematic diagram.</p> Full article ">Figure 10
<p>Data of tooth flank measured by PLS.</p> Full article ">Figure 11
<p>Description of the measurement area. (<b>a</b>) Measuring area of the flexspline gear surface. (<b>b</b>) Measuring position of the flexspline gear surface.</p> Full article ">Figure 12
<p>Reconstruction of teeth flank data. (<b>a</b>) reconstructed teeth flanks of flexspline. (<b>b</b>) partially reconstructed teeth flanks of flexspline.</p> Full article ">Figure 13
<p>Comparison of reconstruction of teeth flank data with teeth flank data obtained by video measuring method. (<b>a</b>) Overall comparison of video measuring method with reconstructed teeth flanks. (<b>b</b>) Partial comparison of the video measuring method with the reconstructed teeth flanks. (<b>c</b>) Partially reconstructed teeth flanks.</p> Full article ">Figure 14
<p>Single pitch deviations and total cumulative pitch deviations of 5 time measurements. (<b>a</b>) single pitch deviations. (<b>b</b>) total cumulative pitch deviations.</p> Full article ">Figure 15
<p>The range of individual single pitch deviation in the repeated measurements.</p> Full article ">
Open AccessReview
A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems
by
Syeda Sitara Wishal Fatima and Afshin Rahimi
Machines 2024, 12(6), 380; https://doi.org/10.3390/machines12060380 - 1 Jun 2024
Abstract
Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries.
[...] Read more.
Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.
Full article
(This article belongs to the Special Issue Smart Manufacturing and Industrial Automation)
Open AccessArticle
Developmental and Experimental Study on a Double-Excitation Ultrasonic Elliptical Vibration-Assisted Cutting Device
by
Gaofeng Hu, Wendong Xin, Min Zhang, Junti Lu, Yanjie Lu, Shengming Zhou and Kai Zheng
Machines 2024, 12(6), 379; https://doi.org/10.3390/machines12060379 - 1 Jun 2024
Abstract
Ultrasonic elliptical vibration-assisted cutting (UEVC) has been successfully applied in the precision and ultra-precision machining of hard and brittle materials due to its advantages of a low cutting force and minimal tool wear. This study developed a novel double-excitation ultrasonic elliptic vibration-assisted cutting
[...] Read more.
Ultrasonic elliptical vibration-assisted cutting (UEVC) has been successfully applied in the precision and ultra-precision machining of hard and brittle materials due to its advantages of a low cutting force and minimal tool wear. This study developed a novel double-excitation ultrasonic elliptic vibration-assisted cutting (D-UEVC) device by coupling ultrasonic vibrations in orthogonal dual paths. A two-degree-of-freedom vibration system of the D-UEVC was modeled, form which the elliptical trajectory of the end under different phase angle φ values was derived. The initial dimensions of the D-UEVC device were obtained through theoretical calculations. Subsequently, with the aid of finite element analysis methods, structural dynamic analysis of the device was conducted to obtain the elliptical vibration trajectory under different phase differences of the excitation source. In order to verify the cutting trajectory and cutting performance of the D-UEVC device, a prototype of the device was developed, and a series of vibration performance tests as well as the Inconel 718 cutting experiment were conducted. The experimental results illustrated that the D-UEVC device can achieve the elliptical vibration trajectory at the tool tip with a resonant frequency of 36.5 KHz. The adjustable elliptical vibration trajectories covered a range of ±4 μm in the axial and radial directions. Compared with the surface roughness Ra = 0.36 μm under the conventional cutting, the surface roughness of Inconel 718 under D-UEVC was Ra = 0.215 μm. Thus, the surface quality can be significant improved by utilizing the D-UEVC device.
Full article
(This article belongs to the Section Advanced Manufacturing)
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Figure 1
<p>Structure of D-UEVC.</p> Full article ">Figure 2
<p>Model of two-degree-of-freedom vibration system.</p> Full article ">Figure 3
<p>Elliptical trajectory under different phase angle φ values.</p> Full article ">Figure 4
<p>Longitudinal vibration transducers.</p> Full article ">Figure 5
<p>Finite element model of the D-UEVC device.</p> Full article ">Figure 6
<p>(<b>a</b>) Fourth-order vibration frequency. (<b>b</b>) Vector plot of vibration mode displacement.</p> Full article ">Figure 7
<p>The elliptical trajectory under different phase angle φ values.</p> Full article ">Figure 8
<p>Prototype of the D-UEVC device.</p> Full article ">Figure 9
<p>Impedance characteristics testing of D-UEVC.</p> Full article ">Figure 10
<p>The impedance curve of both branches.</p> Full article ">Figure 11
<p>The vibration trajectory testing system: (<b>a</b>) the schematic diagram of the elliptical trajectory testing system, (<b>b</b>) the experimental site.</p> Full article ">Figure 11 Cont.
<p>The vibration trajectory testing system: (<b>a</b>) the schematic diagram of the elliptical trajectory testing system, (<b>b</b>) the experimental site.</p> Full article ">Figure 12
<p>End effector vibration trajectory under different phases.</p> Full article ">Figure 12 Cont.
<p>End effector vibration trajectory under different phases.</p> Full article ">Figure 13
<p>Elliptical trajectories at different voltages (φ = 90°).</p> Full article ">Figure 14
<p>The cutting experiment of D-UEVC.</p> Full article ">Figure 15
<p>Surface micro-morphology and roughness of different cutting methods: (<b>a</b>,<b>c</b>) CC surface micro-morphology; (<b>b</b>,<b>d</b>) UEVC surface micro-morphology (cutting speed = 4 m/min).</p> Full article ">Figure 16
<p>The effect of the cutting parameters on surface roughness: (<b>a</b>) effect of cutting speed on the surface roughness, (<b>b</b>) effect of amplitude on the surface roughness (speed: 5 m/min).</p> Full article ">
<p>Structure of D-UEVC.</p> Full article ">Figure 2
<p>Model of two-degree-of-freedom vibration system.</p> Full article ">Figure 3
<p>Elliptical trajectory under different phase angle φ values.</p> Full article ">Figure 4
<p>Longitudinal vibration transducers.</p> Full article ">Figure 5
<p>Finite element model of the D-UEVC device.</p> Full article ">Figure 6
<p>(<b>a</b>) Fourth-order vibration frequency. (<b>b</b>) Vector plot of vibration mode displacement.</p> Full article ">Figure 7
<p>The elliptical trajectory under different phase angle φ values.</p> Full article ">Figure 8
<p>Prototype of the D-UEVC device.</p> Full article ">Figure 9
<p>Impedance characteristics testing of D-UEVC.</p> Full article ">Figure 10
<p>The impedance curve of both branches.</p> Full article ">Figure 11
<p>The vibration trajectory testing system: (<b>a</b>) the schematic diagram of the elliptical trajectory testing system, (<b>b</b>) the experimental site.</p> Full article ">Figure 11 Cont.
<p>The vibration trajectory testing system: (<b>a</b>) the schematic diagram of the elliptical trajectory testing system, (<b>b</b>) the experimental site.</p> Full article ">Figure 12
<p>End effector vibration trajectory under different phases.</p> Full article ">Figure 12 Cont.
<p>End effector vibration trajectory under different phases.</p> Full article ">Figure 13
<p>Elliptical trajectories at different voltages (φ = 90°).</p> Full article ">Figure 14
<p>The cutting experiment of D-UEVC.</p> Full article ">Figure 15
<p>Surface micro-morphology and roughness of different cutting methods: (<b>a</b>,<b>c</b>) CC surface micro-morphology; (<b>b</b>,<b>d</b>) UEVC surface micro-morphology (cutting speed = 4 m/min).</p> Full article ">Figure 16
<p>The effect of the cutting parameters on surface roughness: (<b>a</b>) effect of cutting speed on the surface roughness, (<b>b</b>) effect of amplitude on the surface roughness (speed: 5 m/min).</p> Full article ">
Open AccessArticle
Kinematic Modeling and Performance Analysis of a 5-DoF Robot for Welding Applications
by
Selvaraj Karupusamy, Sundaram Maruthachalam and Balaji Veerasamy
Machines 2024, 12(6), 378; https://doi.org/10.3390/machines12060378 - 1 Jun 2024
Abstract
Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated
[...] Read more.
Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated robots are popular due to their diverse applications, this research proposes a novel 5-DoF robot design for industrial automation, featuring a combination of three prismatic and two revolute (2R) joints, and analyzes its workspace. The proposed techno-economically efficient design offers control over the robot manipulator to achieve any reachable position and orientation within its workspace, replacing traditional 6-DoF robots. The kinematic model integrates both parallel and serial manipulator principles, combining a Cartesian mechanism with rotational mechanisms. Simulations demonstrate the end effector’s flexibility for tasks like welding, additive manufacturing, and material inspections, achieving the desired position and orientation. The research encompasses the design of linear and rotational actuators, kinematic modeling, Human–Machine Interface (HMI) development, and welding application integration. The developed robot demonstrates a superior performance and user-friendliness in welding. The experimental work validates the design’s optimized joint trajectories, efficient power usage, singularity avoidance, easy access in application areas, and reduced costs due to fewer actuators.
Full article
(This article belongs to the Section Automation and Control Systems)
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Figure 1
<p>The kinematic representation of 5 DoF robot.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic representation of the 2R-Wrist in 3D plane, (<b>b</b>) schematic representation of the 2R-Wrist in XY plane, and (<b>c</b>) schematic representation of the tilting frame.</p> Full article ">Figure 3
<p>The simulation of 5-DoF robot trajectory using forward and Inverse Kinematics algorithms.</p> Full article ">Figure 4
<p>The manipulator’s CAD Modeling, (<b>a</b>) Y-axis prismatic joint, (<b>b</b>) X-axis prismatic joint, (<b>c</b>) Z-axis prismatic joint, (<b>d</b>) R-axis revolute joint, and (<b>e</b>) T-axis revolute Joint.</p> Full article ">Figure 5
<p>The Robot Control Panel and its Architecture for Welding Applications.</p> Full article ">Figure 6
<p>The 5-DoF robot HMI for welding applications.</p> Full article ">Figure 7
<p>The visual depiction illustrates the manipulation of the tool along a circular trajectory path.</p> Full article ">Figure 8
<p>The trajectory control flow chart of the 5-DoF robot.</p> Full article ">Figure 9
<p>The 5-DoF robot for welding applications.</p> Full article ">Figure 10
<p>The 5-DoF robot functional flow chart.</p> Full article ">Figure 11
<p>The 5-DoF robot’s axis trajectory displacement with time in a rectangular motion.</p> Full article ">Figure 12
<p>The 6-DoF-articulated robot axis rotational position with time in rectangular motion.</p> Full article ">Figure 13
<p>The 5-DoF robot’s rectangle and circular trajectory path welding.</p> Full article ">Figure 14
<p>(<b>a</b>) Schematic representation of circular and (<b>b</b>) rectangular path joint motion.</p> Full article ">Figure 15
<p>The 5-DoF robot joint position for circular trajectory.</p> Full article ">Figure 16
<p>The 6-DoF robot joint position for circular trajectory.</p> Full article ">Figure 17
<p>Power utilization experimental setup of (<b>a</b>) the 5-DoF robot and (<b>b</b>) the 6-DoF-Articulated robot.</p> Full article ">Figure 18
<p>(<b>a</b>) The 5-DoF Robot: each joint’s power concerning velocity; (<b>b</b>) the 6-DoF-Articulated Robot: each joint’s power concerning velocity; and (<b>c</b>) power utilization comparison between the 6-DoF and 5-DoF robot.</p> Full article ">
<p>The kinematic representation of 5 DoF robot.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic representation of the 2R-Wrist in 3D plane, (<b>b</b>) schematic representation of the 2R-Wrist in XY plane, and (<b>c</b>) schematic representation of the tilting frame.</p> Full article ">Figure 3
<p>The simulation of 5-DoF robot trajectory using forward and Inverse Kinematics algorithms.</p> Full article ">Figure 4
<p>The manipulator’s CAD Modeling, (<b>a</b>) Y-axis prismatic joint, (<b>b</b>) X-axis prismatic joint, (<b>c</b>) Z-axis prismatic joint, (<b>d</b>) R-axis revolute joint, and (<b>e</b>) T-axis revolute Joint.</p> Full article ">Figure 5
<p>The Robot Control Panel and its Architecture for Welding Applications.</p> Full article ">Figure 6
<p>The 5-DoF robot HMI for welding applications.</p> Full article ">Figure 7
<p>The visual depiction illustrates the manipulation of the tool along a circular trajectory path.</p> Full article ">Figure 8
<p>The trajectory control flow chart of the 5-DoF robot.</p> Full article ">Figure 9
<p>The 5-DoF robot for welding applications.</p> Full article ">Figure 10
<p>The 5-DoF robot functional flow chart.</p> Full article ">Figure 11
<p>The 5-DoF robot’s axis trajectory displacement with time in a rectangular motion.</p> Full article ">Figure 12
<p>The 6-DoF-articulated robot axis rotational position with time in rectangular motion.</p> Full article ">Figure 13
<p>The 5-DoF robot’s rectangle and circular trajectory path welding.</p> Full article ">Figure 14
<p>(<b>a</b>) Schematic representation of circular and (<b>b</b>) rectangular path joint motion.</p> Full article ">Figure 15
<p>The 5-DoF robot joint position for circular trajectory.</p> Full article ">Figure 16
<p>The 6-DoF robot joint position for circular trajectory.</p> Full article ">Figure 17
<p>Power utilization experimental setup of (<b>a</b>) the 5-DoF robot and (<b>b</b>) the 6-DoF-Articulated robot.</p> Full article ">Figure 18
<p>(<b>a</b>) The 5-DoF Robot: each joint’s power concerning velocity; (<b>b</b>) the 6-DoF-Articulated Robot: each joint’s power concerning velocity; and (<b>c</b>) power utilization comparison between the 6-DoF and 5-DoF robot.</p> Full article ">
Open AccessArticle
Analytical Modeling of Eddy Current Losses and Thermal Analysis of Non-Uniform-Air-Gap Combined-Pole Permanent Magnet Motors for Electric Vehicles
by
Shilun Ma, Jianwei Ma, Keqi Chen and Changwei Li
Machines 2024, 12(6), 377; https://doi.org/10.3390/machines12060377 - 31 May 2024
Abstract
In order to solve the problem of large eddy current losses and high temperature rises caused by a large number of permanent magnets, a new type of combined-magnetic-pole permanent magnet motor is proposed in this paper. The sinusoidally distributed subdomain model of a
[...] Read more.
In order to solve the problem of large eddy current losses and high temperature rises caused by a large number of permanent magnets, a new type of combined-magnetic-pole permanent magnet motor is proposed in this paper. The sinusoidally distributed subdomain model of a non-uniform-air-gap rotor was established using the Laplace equation, and the analytical expression of eddy current losses in the rotor in a uniform air gap and non-uniform air gap was derived. The effect of the rotor’s eccentricity on eddy current losses was obtained. According to the characteristics of the distributed winding of the non-uniform-air-gap combined-pole permanent magnet motor, an equivalent treatment was performed to obtain the equivalent thermal conductivity value; to establish an equivalent thermal network model of the motor; determine the temperature of each component of the motor; and verify the correctness of the thermal network model through magnetothermal bidirectional coupling. Finally, an experimental platform was set up to carry out temperature rise experiments on the two prototypes. The experimental results show that a non-uniform-air-gap rotor structure can effectively reduce a rotor’s eddy current losses and motor temperature rise, as well as verify the accuracy of the analytical model’s calculation results.
Full article
(This article belongs to the Section Vehicle Engineering)
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Figure 1
<p>Structure diagram of an ITRPMM. 1. Stator core; 2. armature winding; 3. semicircular radial magnetic field permanent magnet (SRPM); 4. radial magnetic field rectangular permanent magnet (RRPM); 5. rotor core; 6. tangential magnetic field rectangular permanent magnet (TRPM).</p> Full article ">Figure 2
<p>Rotor structure with a non-uniform air gap.</p> Full article ">Figure 3
<p>Distribution of the air gap when the magnetic field is sinusoidal.</p> Full article ">Figure 4
<p>Eddy current loss model of a silicon steel sheet.</p> Full article ">Figure 5
<p>Influence of rotor eccentricity on eddy current losses. (<b>a</b>) Relationship between eddy current losses and rotor eccentricity. (<b>b</b>) Finite element simulation results with and without rotor eccentricity.</p> Full article ">Figure 6
<p>Equivalent thermal network model of the motor.</p> Full article ">Figure 7
<p>Equivalent model of armature winding. (<b>a</b>) Initial model of stator winding. (<b>b</b>) Equivalent model of stator winding.</p> Full article ">Figure 8
<p>Flowchart of magnetothermal bidirectional coupling.</p> Full article ">Figure 9
<p>Temperature rise of each part of the motor with a uniform air gap structure at a rated load for 120 min of operation. (<b>a</b>) Temperature rise of the rotor core. (<b>b</b>) Temperature rise of the permanent magnet. (<b>c</b>) Temperature rise of the stator core. (<b>d</b>) Temperature rise of the armature winding. (<b>e</b>) Temperature rise of the motor housing. (<b>f</b>) Temperature rise of the shaft.</p> Full article ">Figure 10
<p>Temperature rise of each part of the motor with a non-uniform air gap structure at a rated load for 120 min of operation. (<b>a</b>) Temperature rise of the rotor core. (<b>b</b>) Temperature rise of the permanent magnet. (<b>c</b>) Temperature rise of the stator core. (<b>d</b>) Temperature rise of the armature winding. (<b>e</b>) Temperature rise of the motor housing. (<b>f</b>) Temperature rise of the shaft.</p> Full article ">Figure 11
<p>Prototype and temperature rise experiment platform. (<b>a</b>) Uniform air gap rotor structure. (<b>b</b>) Non-uniform air gap rotor gap rotor structure. (<b>c</b>) Stator and armature winding. (<b>d</b>) Diagram of the test platform.</p> Full article ">Figure 12
<p>Mechanical characteristic curves of the ITRPMM.</p> Full article ">Figure 13
<p>Thermal imaging camera.</p> Full article ">Figure 14
<p>Steady-state temperature cloud.</p> Full article ">Figure 15
<p>Maximum temperature rise contrast curves of the armature winding.</p> Full article ">Figure 16
<p>Experimental platform of the no-load back EMF.</p> Full article ">Figure 17
<p>Measured waveform of the no-load back EMF of the prototype at rated speed. (<b>a</b>) No-load back EMF waveform with a uniform air gap. (<b>b</b>) No-load back EMF waveform with a non-uniform air gap.</p> Full article ">Figure 18
<p>Harmonic amplitude of the no-load induced electromotive force.</p> Full article ">
<p>Structure diagram of an ITRPMM. 1. Stator core; 2. armature winding; 3. semicircular radial magnetic field permanent magnet (SRPM); 4. radial magnetic field rectangular permanent magnet (RRPM); 5. rotor core; 6. tangential magnetic field rectangular permanent magnet (TRPM).</p> Full article ">Figure 2
<p>Rotor structure with a non-uniform air gap.</p> Full article ">Figure 3
<p>Distribution of the air gap when the magnetic field is sinusoidal.</p> Full article ">Figure 4
<p>Eddy current loss model of a silicon steel sheet.</p> Full article ">Figure 5
<p>Influence of rotor eccentricity on eddy current losses. (<b>a</b>) Relationship between eddy current losses and rotor eccentricity. (<b>b</b>) Finite element simulation results with and without rotor eccentricity.</p> Full article ">Figure 6
<p>Equivalent thermal network model of the motor.</p> Full article ">Figure 7
<p>Equivalent model of armature winding. (<b>a</b>) Initial model of stator winding. (<b>b</b>) Equivalent model of stator winding.</p> Full article ">Figure 8
<p>Flowchart of magnetothermal bidirectional coupling.</p> Full article ">Figure 9
<p>Temperature rise of each part of the motor with a uniform air gap structure at a rated load for 120 min of operation. (<b>a</b>) Temperature rise of the rotor core. (<b>b</b>) Temperature rise of the permanent magnet. (<b>c</b>) Temperature rise of the stator core. (<b>d</b>) Temperature rise of the armature winding. (<b>e</b>) Temperature rise of the motor housing. (<b>f</b>) Temperature rise of the shaft.</p> Full article ">Figure 10
<p>Temperature rise of each part of the motor with a non-uniform air gap structure at a rated load for 120 min of operation. (<b>a</b>) Temperature rise of the rotor core. (<b>b</b>) Temperature rise of the permanent magnet. (<b>c</b>) Temperature rise of the stator core. (<b>d</b>) Temperature rise of the armature winding. (<b>e</b>) Temperature rise of the motor housing. (<b>f</b>) Temperature rise of the shaft.</p> Full article ">Figure 11
<p>Prototype and temperature rise experiment platform. (<b>a</b>) Uniform air gap rotor structure. (<b>b</b>) Non-uniform air gap rotor gap rotor structure. (<b>c</b>) Stator and armature winding. (<b>d</b>) Diagram of the test platform.</p> Full article ">Figure 12
<p>Mechanical characteristic curves of the ITRPMM.</p> Full article ">Figure 13
<p>Thermal imaging camera.</p> Full article ">Figure 14
<p>Steady-state temperature cloud.</p> Full article ">Figure 15
<p>Maximum temperature rise contrast curves of the armature winding.</p> Full article ">Figure 16
<p>Experimental platform of the no-load back EMF.</p> Full article ">Figure 17
<p>Measured waveform of the no-load back EMF of the prototype at rated speed. (<b>a</b>) No-load back EMF waveform with a uniform air gap. (<b>b</b>) No-load back EMF waveform with a non-uniform air gap.</p> Full article ">Figure 18
<p>Harmonic amplitude of the no-load induced electromotive force.</p> Full article ">
Open AccessReview
Nonlinear Passive Observer for Motion Estimation in Multi-Axis Precision Motion Control
by
Hector Gutierrez and Dengfeng Li
Machines 2024, 12(6), 376; https://doi.org/10.3390/machines12060376 - 30 May 2024
Abstract
A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-axis high-precision motion control stage is presented. The proposed nonlinear estimation strategy is developed based on a Lyapunov stability analysis, which proves that the NPO is stable. Three test cases
[...] Read more.
A nonlinear passive observer (NPO) for estimating the time-varying velocity vector of a multi-axis high-precision motion control stage is presented. The proposed nonlinear estimation strategy is developed based on a Lyapunov stability analysis, which proves that the NPO is stable. Three test cases are used to investigate the performance of the proposed observer. Experimental results are given to demonstrate the performance of the proposed NPO in accurately estimating time-varying velocity during alignment, reciprocating motion, and multi-axis motion in high-precision motion control applications.
Full article
(This article belongs to the Special Issue Advances in Applied Mechatronics, Volume II)
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Figure 1
<p>Multi-axis high-precision motion control stage, (<b>a</b>) stationary and moving parts, (<b>b</b>) moving part showing permanent magnet stacks, actuator target areas, and sensor target areas.</p> Full article ">Figure 2
<p>Multi-axis high-precision motion control stage, (<b>a</b>) moving part and base showing actuator and sensor locations, (<b>b</b>) implementation showing axes of motion and axes of rotation.</p> Full article ">Figure 3
<p>Implementation and testing of the proposed approach. (<b>a</b>) The real-time control platform based on TI-6701 DSP; (<b>b</b>) the low-pass filter for implementation of a nonlinear passive observer (NPO).</p> Full article ">Figure 4
<p>Frequency response of the <span class="html-italic">H</span>(<span class="html-italic">z</span>) digital low-pass filter (<b>a</b>). Frequency response over the entire design range; (<b>b</b>) frequency response in the application’s pass band.</p> Full article ">Figure 5
<p>Step response to 500 nm command on x-axis, (<b>a</b>) position response, and (<b>b</b>) velocity response and velocity estimation on x-axis.</p> Full article ">Figure 6
<p>Step response to 250 nm command on y-axis, (<b>a</b>) position response, and (<b>b</b>) velocity response and velocity estimation on y-axis.</p> Full article ">Figure 7
<p>Step response to 4 μrad command on θ-axis, (<b>a</b>) angular position response (yaw), and (<b>b</b>) velocity response and velocity estimation on yaw-axis.</p> Full article ">Figure 8
<p>(<b>a</b>) Reciprocating motion on x-axis; (<b>b</b>) synchronized circular trajectory in XY plane.</p> Full article ">Figure 9
<p>Estimator performance during reciprocating motion on x-axis; (<b>a</b>) estimation of positive velocity; (<b>b</b>) estimation of negative velocity.</p> Full article ">Figure 10
<p>Estimator performance in synchronized trajectory in the XY plane, (<b>a</b>) velocity estimation on the x-axis during circular motion; (<b>b</b>) velocity estimation on the y-axis during circular motion.</p> Full article ">
<p>Multi-axis high-precision motion control stage, (<b>a</b>) stationary and moving parts, (<b>b</b>) moving part showing permanent magnet stacks, actuator target areas, and sensor target areas.</p> Full article ">Figure 2
<p>Multi-axis high-precision motion control stage, (<b>a</b>) moving part and base showing actuator and sensor locations, (<b>b</b>) implementation showing axes of motion and axes of rotation.</p> Full article ">Figure 3
<p>Implementation and testing of the proposed approach. (<b>a</b>) The real-time control platform based on TI-6701 DSP; (<b>b</b>) the low-pass filter for implementation of a nonlinear passive observer (NPO).</p> Full article ">Figure 4
<p>Frequency response of the <span class="html-italic">H</span>(<span class="html-italic">z</span>) digital low-pass filter (<b>a</b>). Frequency response over the entire design range; (<b>b</b>) frequency response in the application’s pass band.</p> Full article ">Figure 5
<p>Step response to 500 nm command on x-axis, (<b>a</b>) position response, and (<b>b</b>) velocity response and velocity estimation on x-axis.</p> Full article ">Figure 6
<p>Step response to 250 nm command on y-axis, (<b>a</b>) position response, and (<b>b</b>) velocity response and velocity estimation on y-axis.</p> Full article ">Figure 7
<p>Step response to 4 μrad command on θ-axis, (<b>a</b>) angular position response (yaw), and (<b>b</b>) velocity response and velocity estimation on yaw-axis.</p> Full article ">Figure 8
<p>(<b>a</b>) Reciprocating motion on x-axis; (<b>b</b>) synchronized circular trajectory in XY plane.</p> Full article ">Figure 9
<p>Estimator performance during reciprocating motion on x-axis; (<b>a</b>) estimation of positive velocity; (<b>b</b>) estimation of negative velocity.</p> Full article ">Figure 10
<p>Estimator performance in synchronized trajectory in the XY plane, (<b>a</b>) velocity estimation on the x-axis during circular motion; (<b>b</b>) velocity estimation on the y-axis during circular motion.</p> Full article ">
Open AccessArticle
Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle
by
Yelin He, Jian Wu, Fuxing Xu, Xin Liu, Shuai Wang and Guanjie Cui
Machines 2024, 12(6), 375; https://doi.org/10.3390/machines12060375 - 29 May 2024
Abstract
Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change.
[...] Read more.
Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system’s overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance.
Full article
(This article belongs to the Special Issue New Trends in Robotics and Automation)
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Show Figures
Figure 1
Figure 1
<p>Schematic of vehicle-road system model based on yaw angle.</p> Full article ">Figure 2
<p>Schematic of vehicle-road system model based on heading angle.</p> Full article ">Figure 3
<p>Membership functions of vehicle speed.</p> Full article ">Figure 4
<p>Vehicle speed changes on a serpentine road.</p> Full article ">Figure 5
<p>Serpentine road curvature.</p> Full article ">Figure 6
<p>The simulation results of path tracking on a serpentine road are compared.</p> Full article ">Figure 7
<p>The simulation results of the state vector on a serpentine road are compared. (<b>a</b>) Results of lateral velocity simulations are compared; (<b>b</b>) results of yaw velocity and heading velocity simulations are compared; (<b>c</b>) results of lateral deviation simulations are compared; (<b>d</b>) results of the yaw angle error and heading angle error simulations are compared.</p> Full article ">Figure 8
<p>The simulation results of the front-wheel steering angle on a serpentine road are compared.</p> Full article ">Figure 9
<p>Vehicle speed changes on a double lane change.</p> Full article ">Figure 10
<p>Double lane change curvature.</p> Full article ">Figure 11
<p>The simulation results of path tracking on a double lane change are compared.</p> Full article ">Figure 12
<p>The simulation results of the state vector on a double lane change are compared. (<b>a</b>) Results of lateral velocity simulations are compared; (<b>b</b>) results of yaw velocity and heading velocity simulations are compared; (<b>c</b>) results of lateral deviation simulations are compared; (<b>d</b>) results of the yaw angle error and heading angle error simulations are compared.</p> Full article ">Figure 13
<p>The simulation results of the front-wheel steering angle on a double lane change are compared.</p> Full article ">Figure 14
<p>Structure of the control scheme based on the model of the yaw angle or heading angle.</p> Full article ">Figure 15
<p>Hardware-in-the-loop (HIL) test rig.</p> Full article ">Figure 16
<p>The experimental results of path tracking on a serpentine road are compared.</p> Full article ">Figure 17
<p>The experimental results of the state vector on a serpentine road are compared. (<b>a</b>) Results of lateral velocity experimental are compared; (<b>b</b>) results of yaw velocity and heading velocity experimental are compared; (<b>c</b>), results of lateral deviation experimental are compared; (<b>d</b>) results of the yaw angle error and heading angle error experimental are compared.</p> Full article ">Figure 18
<p>The experimental results of front-wheel steering angle on a serpentine road are compared.</p> Full article ">
<p>Schematic of vehicle-road system model based on yaw angle.</p> Full article ">Figure 2
<p>Schematic of vehicle-road system model based on heading angle.</p> Full article ">Figure 3
<p>Membership functions of vehicle speed.</p> Full article ">Figure 4
<p>Vehicle speed changes on a serpentine road.</p> Full article ">Figure 5
<p>Serpentine road curvature.</p> Full article ">Figure 6
<p>The simulation results of path tracking on a serpentine road are compared.</p> Full article ">Figure 7
<p>The simulation results of the state vector on a serpentine road are compared. (<b>a</b>) Results of lateral velocity simulations are compared; (<b>b</b>) results of yaw velocity and heading velocity simulations are compared; (<b>c</b>) results of lateral deviation simulations are compared; (<b>d</b>) results of the yaw angle error and heading angle error simulations are compared.</p> Full article ">Figure 8
<p>The simulation results of the front-wheel steering angle on a serpentine road are compared.</p> Full article ">Figure 9
<p>Vehicle speed changes on a double lane change.</p> Full article ">Figure 10
<p>Double lane change curvature.</p> Full article ">Figure 11
<p>The simulation results of path tracking on a double lane change are compared.</p> Full article ">Figure 12
<p>The simulation results of the state vector on a double lane change are compared. (<b>a</b>) Results of lateral velocity simulations are compared; (<b>b</b>) results of yaw velocity and heading velocity simulations are compared; (<b>c</b>) results of lateral deviation simulations are compared; (<b>d</b>) results of the yaw angle error and heading angle error simulations are compared.</p> Full article ">Figure 13
<p>The simulation results of the front-wheel steering angle on a double lane change are compared.</p> Full article ">Figure 14
<p>Structure of the control scheme based on the model of the yaw angle or heading angle.</p> Full article ">Figure 15
<p>Hardware-in-the-loop (HIL) test rig.</p> Full article ">Figure 16
<p>The experimental results of path tracking on a serpentine road are compared.</p> Full article ">Figure 17
<p>The experimental results of the state vector on a serpentine road are compared. (<b>a</b>) Results of lateral velocity experimental are compared; (<b>b</b>) results of yaw velocity and heading velocity experimental are compared; (<b>c</b>), results of lateral deviation experimental are compared; (<b>d</b>) results of the yaw angle error and heading angle error experimental are compared.</p> Full article ">Figure 18
<p>The experimental results of front-wheel steering angle on a serpentine road are compared.</p> Full article ">
Open AccessArticle
Human-Centered Design and Manufacturing of a Pressure-Profile-Based Pad for Better Car Seat Comfort
by
Alessandro Naddeo, Alfonso Morra and Rosaria Califano
Machines 2024, 12(6), 374; https://doi.org/10.3390/machines12060374 - 28 May 2024
Abstract
A car seat’s function is to support, protect, and make passengers and drivers feel comfortable during a trip. A more uniform pressure distribution and a larger contact area usually provide less discomfort. Consequently, the seat pan’s material and geometry play an essential role
[...] Read more.
A car seat’s function is to support, protect, and make passengers and drivers feel comfortable during a trip. A more uniform pressure distribution and a larger contact area usually provide less discomfort. Consequently, the seat pan’s material and geometry play an essential role in the design process. A shaped pad was opportunely designed and realized, starting from the pressure distributions between the buttocks and the seat pan; pressure data were acquired during an initial experiment involving 41 people, representing a wide range of percentiles. The shaped pad was compared with a standard one by building a special seat with an interchangeable internal pad and testing the standard and the new seat; the second experiment involved 52 people that tested both seats. The tests were conducted to assess comfort (33 subjects were asked to be seated for 1 min each) and discomfort (19 subjects were asked to be seated for 15 min each); during the tests, pressure distribution and contact area data were gathered. The results showed that, for both tests, about 80% of the participants, among which 100% of the female sample, preferred the shaped seat pan pad. Even if the material was exactly the same, the shaped pad seemed to be softer, more comfortable, and more suited to the body’s shape than the standard one. The design methodology was demonstrated to be very useful for granting a more uniform pressure distribution and a wider contact area, i.e., higher comfort and less discomfort.
Full article
(This article belongs to the Special Issue Digital Technologies to Support Human Factors Engineering in Manufacturing System Design: Theory and Applications)
►▼
Show Figures
Figure 1
Figure 1
<p>Simplified mock-up.</p> Full article ">Figure 2
<p>Three different postures, from left to right: relaxed posture simulating a passenger, pressing the brake pedal, pressing the accelerator pedal.</p> Full article ">Figure 3
<p>Output script 2: relaxed posture simulating a passenger (posture 1).</p> Full article ">Figure 4
<p>Output script 2: pressing the brake pedal (posture 2).</p> Full article ">Figure 5
<p>Output script 2: pressing the accelerator pedal (posture 3).</p> Full article ">Figure 6
<p>Example of 3D surface obtained by Grasshopper<sup>®</sup>.</p> Full article ">Figure 7
<p>(<b>a</b>) Solidworks<sup>®</sup> model; (<b>b</b>) auxiliary planes.</p> Full article ">Figure 8
<p>Examples of profiles generated by plane 6 (posture 1—relaxed posture simulating a passenger, posture 2—pressing the brake pedal, posture 3—pressing the accelerator pedal).</p> Full article ">Figure 9
<p>(<b>a</b>) Seat pan shape; (<b>b</b>) seat pan pad prototype.</p> Full article ">Figure 10
<p>(<b>a</b>) Printed seat pan pad; (<b>b</b>) assembled seat.</p> Full article ">Figure 11
<p>Setup used for the experiments.</p> Full article ">Figure 12
<p>Body map used in the tests; areas are numbered as indicated in the questionnaires.</p> Full article ">Figure 13
<p>One-minute test, descriptive adjectives.</p> Full article ">Figure 14
<p>Fifteen-minute test, descriptive adjectives.</p> Full article ">Figure 15
<p>Comfort assessment.</p> Full article ">Figure A1
<p>Reference image.</p> Full article ">
<p>Simplified mock-up.</p> Full article ">Figure 2
<p>Three different postures, from left to right: relaxed posture simulating a passenger, pressing the brake pedal, pressing the accelerator pedal.</p> Full article ">Figure 3
<p>Output script 2: relaxed posture simulating a passenger (posture 1).</p> Full article ">Figure 4
<p>Output script 2: pressing the brake pedal (posture 2).</p> Full article ">Figure 5
<p>Output script 2: pressing the accelerator pedal (posture 3).</p> Full article ">Figure 6
<p>Example of 3D surface obtained by Grasshopper<sup>®</sup>.</p> Full article ">Figure 7
<p>(<b>a</b>) Solidworks<sup>®</sup> model; (<b>b</b>) auxiliary planes.</p> Full article ">Figure 8
<p>Examples of profiles generated by plane 6 (posture 1—relaxed posture simulating a passenger, posture 2—pressing the brake pedal, posture 3—pressing the accelerator pedal).</p> Full article ">Figure 9
<p>(<b>a</b>) Seat pan shape; (<b>b</b>) seat pan pad prototype.</p> Full article ">Figure 10
<p>(<b>a</b>) Printed seat pan pad; (<b>b</b>) assembled seat.</p> Full article ">Figure 11
<p>Setup used for the experiments.</p> Full article ">Figure 12
<p>Body map used in the tests; areas are numbered as indicated in the questionnaires.</p> Full article ">Figure 13
<p>One-minute test, descriptive adjectives.</p> Full article ">Figure 14
<p>Fifteen-minute test, descriptive adjectives.</p> Full article ">Figure 15
<p>Comfort assessment.</p> Full article ">Figure A1
<p>Reference image.</p> Full article ">
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