The development of product platform is an effective strategy to respond to dynamic market demands, decrease lead-time and delay products differentiation. However, the traditional product platform configuration method can not satisfy the sustainability requirements for modern products. To solve this problem, a sustainable product multi-platform (SPMP) model for assembly/ disassembly technology is proposed in this paper. The proposed SPMP model measures the energy consumption of module instances during the installation based on the platform-based assembly index (PAI) and platform-based disassembly index (PDI), and provides a multi-platform solution for the assembly of product family. To demonstrate the effectiveness of the proposed method, two product family cases are discussed. Simplified case shows that multi-objective particle swarm optimisation (MOPSO) algorithm has stronger optimisation ability than linear programming method in reducing product processing cost. The hair dryer family case demonstrates that the proposed method reduces the energy consumption during assembly by linking sustainability to product design.
{"title":"A Sustainable Product Multi-platform Planning Model for Assembly and Disassembly Process","authors":"Guang-yu Zou, Zhongkai Li, Chao He","doi":"10.1115/1.4064675","DOIUrl":"https://doi.org/10.1115/1.4064675","url":null,"abstract":"\u0000 The development of product platform is an effective strategy to respond to dynamic market demands, decrease lead-time and delay products differentiation. However, the traditional product platform configuration method can not satisfy the sustainability requirements for modern products. To solve this problem, a sustainable product multi-platform (SPMP) model for assembly/ disassembly technology is proposed in this paper. The proposed SPMP model measures the energy consumption of module instances during the installation based on the platform-based assembly index (PAI) and platform-based disassembly index (PDI), and provides a multi-platform solution for the assembly of product family. To demonstrate the effectiveness of the proposed method, two product family cases are discussed. Simplified case shows that multi-objective particle swarm optimisation (MOPSO) algorithm has stronger optimisation ability than linear programming method in reducing product processing cost. The hair dryer family case demonstrates that the proposed method reduces the energy consumption during assembly by linking sustainability to product design.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"378 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amin Heyrani Nobari, Justin Rey, S. Kodali, Matthew Jones, Faez Ahmed
In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine learning-based scheme that leverages Graph Neural Networks (GNN) for handling 3D geometries, specifically for surface classification. Our model demonstrates superior performance against two state-of-the-art models, PointNet++ and PointMLP, in terms of surface classification accuracy, beating both models. Central to our contribution is the novel incorporation of conformal predictions, a method that offers robust uncertainty quantification and handling with marginal statistical guarantees. Unlike traditional approaches, conformal predictions enable our model to ensure precision, especially in challenging scenarios where mistakes can be highly costly. This robustness proves invaluable in design applications, and as a case in point, we showcase its utility in automating the Computational Fluid Dynamics (CFD) meshing process for aircraft models based on expert guidance. Our results reveal that our automatically generated mesh, guided by the proposed rules by experts enabled through the segmentation model, is not only efficient but matches the quality of expert-generated meshes, leading to accurate simulations. For the community's benefit, we have made our code and data available at https://github.com/ahnobari/AutoSurf Upon paper acceptance.
{"title":"MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations","authors":"Amin Heyrani Nobari, Justin Rey, S. Kodali, Matthew Jones, Faez Ahmed","doi":"10.1115/1.4064673","DOIUrl":"https://doi.org/10.1115/1.4064673","url":null,"abstract":"\u0000 In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine learning-based scheme that leverages Graph Neural Networks (GNN) for handling 3D geometries, specifically for surface classification. Our model demonstrates superior performance against two state-of-the-art models, PointNet++ and PointMLP, in terms of surface classification accuracy, beating both models. Central to our contribution is the novel incorporation of conformal predictions, a method that offers robust uncertainty quantification and handling with marginal statistical guarantees. Unlike traditional approaches, conformal predictions enable our model to ensure precision, especially in challenging scenarios where mistakes can be highly costly. This robustness proves invaluable in design applications, and as a case in point, we showcase its utility in automating the Computational Fluid Dynamics (CFD) meshing process for aircraft models based on expert guidance. Our results reveal that our automatically generated mesh, guided by the proposed rules by experts enabled through the segmentation model, is not only efficient but matches the quality of expert-generated meshes, leading to accurate simulations. For the community's benefit, we have made our code and data available at https://github.com/ahnobari/AutoSurf Upon paper acceptance.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139858937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Engineering designers are tasked with increasingly complex problems necessitating the use and development of various supports for navigating complexity. Prescriptive design process models are one such tool. However, little research has explored how engineering designers perceive these models' recommendations for engagement in design work. In this initial exploratory study, we analyzed data from 18 individual semi-structured interviews with mechanical engineering students to identify participant perceptions. As many design process model visualizations lack explicit attention to some social and contextual dimensions, we sought to compare perceptions among two drawn from engineering texts and one that was developed with the intent to emphasize social dimensions. We identified five salient areas of participant perceptions of the design process models. Perceptions of the process models related to what designers should do (starting and moving through a design process, gathering information, prototyping, and evaluating or testing) and what they should consider (aspects of focus). Our collection of participant perceptions across the three process models suggests different design process models make perceptions of certain recommendations more salient than others. However, participant perceptions also varied for the same process model. We suggest several implications for design education and training based on participant perceptions of these three process models, particularly the importance of leveraging multiple design process models. The comprehensive descriptions of participant perceptions across five areas of design work provided through our initial study provide a foundation for further investigations bridging designers' perceptions to intent to behavior and, ultimately, design outcomes.
{"title":"A Comparative Analysis of Student Perceptions of Recommendations for Engagement in Design Processes","authors":"K. Dugan, Shanna Daly","doi":"10.1115/1.4064671","DOIUrl":"https://doi.org/10.1115/1.4064671","url":null,"abstract":"\u0000 Engineering designers are tasked with increasingly complex problems necessitating the use and development of various supports for navigating complexity. Prescriptive design process models are one such tool. However, little research has explored how engineering designers perceive these models' recommendations for engagement in design work. In this initial exploratory study, we analyzed data from 18 individual semi-structured interviews with mechanical engineering students to identify participant perceptions. As many design process model visualizations lack explicit attention to some social and contextual dimensions, we sought to compare perceptions among two drawn from engineering texts and one that was developed with the intent to emphasize social dimensions. We identified five salient areas of participant perceptions of the design process models. Perceptions of the process models related to what designers should do (starting and moving through a design process, gathering information, prototyping, and evaluating or testing) and what they should consider (aspects of focus). Our collection of participant perceptions across the three process models suggests different design process models make perceptions of certain recommendations more salient than others. However, participant perceptions also varied for the same process model. We suggest several implications for design education and training based on participant perceptions of these three process models, particularly the importance of leveraging multiple design process models. The comprehensive descriptions of participant perceptions across five areas of design work provided through our initial study provide a foundation for further investigations bridging designers' perceptions to intent to behavior and, ultimately, design outcomes.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"70 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139860875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the engineering design process, design fixation significantly constrains the diversity of design solutions. Numerous studies have aimed to mitigate design fixation, yet determining its occurrence in real-time remains a challenge. This research seeks to systematically identify the emergence of fixation through the behavior of novice designers in the early stages of the design process. We conducted a laboratory study, involving 50 novice designers possessing engineering drafting skills. Their design processes were monitored via video cameras, with both their design solutions and physical behaviors recorded. Subsequently, expert evaluators categorized design solutions into three types: Fixation, Low-quality, and Innovative. We manually recorded the names and durations of 31 different physical behaviors observed in the videos, which were then coded and filtered. From this, four fixation behaviors were identified using variance analysis (ANOVA): Touch Mouth (TM), Touch Head (TH), Rest Head in Hands (RH), and Hold Face in Hands (HF). Our findings suggest that continuous interaction between the hand and head, mouth, or face can be indicative of a fixation state. Finally, we developed a Behavior-Fixation model based on the Support Vector Machine (SVM) for stage fixation judgment tasks, achieving an accuracy rate of 85.6%. This machine learning model outperforms manual assessment in speed and accuracy. Overall, our study offers promising prospects for assisting designers in recognizing and avoiding design fixation. These findings, coupled with our proposed computational techniques, provide valuable insights for the development of automated and intelligent design innovation systems.
{"title":"Mapping Novice Designer Behavior to Design Fixation in the Early-Stage Design Process","authors":"Miao Jia, Shuo Jiang, Jin Qi, Jie Hu","doi":"10.1115/1.4064649","DOIUrl":"https://doi.org/10.1115/1.4064649","url":null,"abstract":"\u0000 In the engineering design process, design fixation significantly constrains the diversity of design solutions. Numerous studies have aimed to mitigate design fixation, yet determining its occurrence in real-time remains a challenge. This research seeks to systematically identify the emergence of fixation through the behavior of novice designers in the early stages of the design process. We conducted a laboratory study, involving 50 novice designers possessing engineering drafting skills. Their design processes were monitored via video cameras, with both their design solutions and physical behaviors recorded. Subsequently, expert evaluators categorized design solutions into three types: Fixation, Low-quality, and Innovative. We manually recorded the names and durations of 31 different physical behaviors observed in the videos, which were then coded and filtered. From this, four fixation behaviors were identified using variance analysis (ANOVA): Touch Mouth (TM), Touch Head (TH), Rest Head in Hands (RH), and Hold Face in Hands (HF). Our findings suggest that continuous interaction between the hand and head, mouth, or face can be indicative of a fixation state. Finally, we developed a Behavior-Fixation model based on the Support Vector Machine (SVM) for stage fixation judgment tasks, achieving an accuracy rate of 85.6%. This machine learning model outperforms manual assessment in speed and accuracy. Overall, our study offers promising prospects for assisting designers in recognizing and avoiding design fixation. These findings, coupled with our proposed computational techniques, provide valuable insights for the development of automated and intelligent design innovation systems.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"112 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139830277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the engineering design process, design fixation significantly constrains the diversity of design solutions. Numerous studies have aimed to mitigate design fixation, yet determining its occurrence in real-time remains a challenge. This research seeks to systematically identify the emergence of fixation through the behavior of novice designers in the early stages of the design process. We conducted a laboratory study, involving 50 novice designers possessing engineering drafting skills. Their design processes were monitored via video cameras, with both their design solutions and physical behaviors recorded. Subsequently, expert evaluators categorized design solutions into three types: Fixation, Low-quality, and Innovative. We manually recorded the names and durations of 31 different physical behaviors observed in the videos, which were then coded and filtered. From this, four fixation behaviors were identified using variance analysis (ANOVA): Touch Mouth (TM), Touch Head (TH), Rest Head in Hands (RH), and Hold Face in Hands (HF). Our findings suggest that continuous interaction between the hand and head, mouth, or face can be indicative of a fixation state. Finally, we developed a Behavior-Fixation model based on the Support Vector Machine (SVM) for stage fixation judgment tasks, achieving an accuracy rate of 85.6%. This machine learning model outperforms manual assessment in speed and accuracy. Overall, our study offers promising prospects for assisting designers in recognizing and avoiding design fixation. These findings, coupled with our proposed computational techniques, provide valuable insights for the development of automated and intelligent design innovation systems.
{"title":"Mapping Novice Designer Behavior to Design Fixation in the Early-Stage Design Process","authors":"Miao Jia, Shuo Jiang, Jin Qi, Jie Hu","doi":"10.1115/1.4064649","DOIUrl":"https://doi.org/10.1115/1.4064649","url":null,"abstract":"\u0000 In the engineering design process, design fixation significantly constrains the diversity of design solutions. Numerous studies have aimed to mitigate design fixation, yet determining its occurrence in real-time remains a challenge. This research seeks to systematically identify the emergence of fixation through the behavior of novice designers in the early stages of the design process. We conducted a laboratory study, involving 50 novice designers possessing engineering drafting skills. Their design processes were monitored via video cameras, with both their design solutions and physical behaviors recorded. Subsequently, expert evaluators categorized design solutions into three types: Fixation, Low-quality, and Innovative. We manually recorded the names and durations of 31 different physical behaviors observed in the videos, which were then coded and filtered. From this, four fixation behaviors were identified using variance analysis (ANOVA): Touch Mouth (TM), Touch Head (TH), Rest Head in Hands (RH), and Hold Face in Hands (HF). Our findings suggest that continuous interaction between the hand and head, mouth, or face can be indicative of a fixation state. Finally, we developed a Behavior-Fixation model based on the Support Vector Machine (SVM) for stage fixation judgment tasks, achieving an accuracy rate of 85.6%. This machine learning model outperforms manual assessment in speed and accuracy. Overall, our study offers promising prospects for assisting designers in recognizing and avoiding design fixation. These findings, coupled with our proposed computational techniques, provide valuable insights for the development of automated and intelligent design innovation systems.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"28 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139890294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Connor Moffatt, Jae Sung Huh, Sangook Jun, Il Yong Kim
The packing optimization of three-dimensional components into a design space is a challenging and time-intensive task. Of particular concern is the thermal performance of the system, as tightly packed components typically exhibit poor heat dissipation performance which can result in overheating and system failure. As temperature modelling can be quite complex, there is growing demand in industry for software tools that aid designers in the packing process whilst considering heat transfer. This work outlines a novel multi-objective algorithm that considers temperature and thermal effects directly within the packing optimization process itself using thermal optimization objectives. In addition, the algorithm can consider functional objectives such as a desired center of mass position and minimizing rotational inertia. The algorithm packs components from initial to optimal positions within a design domain using a set of dynamic acceleration fields. There are multiple accelerations, each designed to improve the objective values for the systems (for example, minimize temperature variance). Component temperatures are calculated using thermal finite element analyses modelling conduction and natural convection. Forced convection is approximated via computational fluid dynamics simulations. Numerical results for two academic and one real-world case studies are presented to demonstrate the efficacy of the presented algorithm.
{"title":"Thermally Driven Multi-Objective Packing Optimization Using Acceleration Fields","authors":"Connor Moffatt, Jae Sung Huh, Sangook Jun, Il Yong Kim","doi":"10.1115/1.4064489","DOIUrl":"https://doi.org/10.1115/1.4064489","url":null,"abstract":"\u0000 The packing optimization of three-dimensional components into a design space is a challenging and time-intensive task. Of particular concern is the thermal performance of the system, as tightly packed components typically exhibit poor heat dissipation performance which can result in overheating and system failure. As temperature modelling can be quite complex, there is growing demand in industry for software tools that aid designers in the packing process whilst considering heat transfer. This work outlines a novel multi-objective algorithm that considers temperature and thermal effects directly within the packing optimization process itself using thermal optimization objectives. In addition, the algorithm can consider functional objectives such as a desired center of mass position and minimizing rotational inertia. The algorithm packs components from initial to optimal positions within a design domain using a set of dynamic acceleration fields. There are multiple accelerations, each designed to improve the objective values for the systems (for example, minimize temperature variance). Component temperatures are calculated using thermal finite element analyses modelling conduction and natural convection. Forced convection is approximated via computational fluid dynamics simulations. Numerical results for two academic and one real-world case studies are presented to demonstrate the efficacy of the presented algorithm.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"3 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139532682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers have studied Stewart platform-based Parallel Kinematic Machines (PKM) extensively for their fine control capabilities, for many applications including medicine, precision engineering machines, aerospace research, electronic chip manufacturing, automobile manufacturing, etc. These applications need micro and nano-level movement control in 3D space for the motions to be precise, complicated, and repeatable; a Stewart platform fulfills these challenges smartly. For this, the PKM must be more accurate than the specified application accuracy level and thus proper calibration for a PKM robot is crucial. Forward kinematics-based calibration for such hexapod machines becomes unnecessarily complex and inverse kinematics complete this task with much ease. To experiment different calibration techniques, various calibration approaches were implemented by using external instruments, constraining one or more motions of the system, and using extra sensors for auto or self-calibration. This survey paid attention to those key methodologies, their outcome, and important details related to inverse kinematic-based PKM calibrations. It was observed during this study that the researchers focused on improving the accuracy of the platform position and orientation considering the errors contributed by one source or multiple sources. The error sources considered are mainly kinematic and structural, in some cases, environmental factors also are reviewed, however, those calibrations are done under no-load conditions. This study aims to review the present state of the art in this field and highlight on the processes and errors considered for the calibration of Stewart platforms.
研究人员对基于 Stewart 平台的并联运动机械 (PKM) 的精细控制能力进行了广泛研究，其应用领域包括医疗、精密工程机械、航空航天研究、电子芯片制造、汽车制造等。这些应用需要在三维空间内进行微米级和纳米级运动控制，以实现精确、复杂和可重复的运动。为此，PKM 的精度必须高于指定的应用精度水平，因此对 PKM 机器人进行适当的校准至关重要。基于正向运动学的校准对于这类六足机器人来说变得过于复杂，而反向运动学则可以轻松完成这项任务。为了尝试不同的校准技术，我们采用了各种校准方法，包括使用外部仪器、限制系统的一个或多个运动，以及使用额外的传感器进行自动或自我校准。本次调查关注了这些关键方法、其结果以及与基于逆运动学的 PKM 校准相关的重要细节。在这项研究中，我们注意到研究人员将重点放在提高平台位置和方向的精确度上，同时考虑到一个或多个来源造成的误差。考虑的误差源主要是运动学和结构学，在某些情况下，也会审查环境因素，但这些校准都是在空载条件下进行的。本研究旨在回顾该领域的技术现状，并重点介绍校准 Stewart 平台时考虑的过程和误差。
{"title":"A LITERATURE REVIEW ON STEWART-GOUGH PLATFORM CALIBRATIONS","authors":"Sourabh Karmakar, Cameron Turner","doi":"10.1115/1.4064487","DOIUrl":"https://doi.org/10.1115/1.4064487","url":null,"abstract":"\u0000 Researchers have studied Stewart platform-based Parallel Kinematic Machines (PKM) extensively for their fine control capabilities, for many applications including medicine, precision engineering machines, aerospace research, electronic chip manufacturing, automobile manufacturing, etc. These applications need micro and nano-level movement control in 3D space for the motions to be precise, complicated, and repeatable; a Stewart platform fulfills these challenges smartly. For this, the PKM must be more accurate than the specified application accuracy level and thus proper calibration for a PKM robot is crucial. Forward kinematics-based calibration for such hexapod machines becomes unnecessarily complex and inverse kinematics complete this task with much ease. To experiment different calibration techniques, various calibration approaches were implemented by using external instruments, constraining one or more motions of the system, and using extra sensors for auto or self-calibration. This survey paid attention to those key methodologies, their outcome, and important details related to inverse kinematic-based PKM calibrations. It was observed during this study that the researchers focused on improving the accuracy of the platform position and orientation considering the errors contributed by one source or multiple sources. The error sources considered are mainly kinematic and structural, in some cases, environmental factors also are reviewed, however, those calibrations are done under no-load conditions. This study aims to review the present state of the art in this field and highlight on the processes and errors considered for the calibration of Stewart platforms.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"31 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139532310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Man Bok Hong, Dukchan Yoon, Jaehyun Park, Keehoon Kim
This paper presents a wrist exoskeleton called the KULEX (KIST Upper-Limb EXoskeleton)-wrist for activities of daily living assistance of the elderly and the disabled. A novel linkage-based structure of the rotational mechanism with three degrees of freedom is proposed. The proposed wrist mechanism is composed of two PUS (Prismatic-Universal-Spherical) serial chains and one RRR (Revolute-Revolute-Revolute) spherical chain. Besides, a combination of a planar slider-crank and spherical four-bar linkages was employed as a driving mechanism for power transmission. Kinematic analysis was conducted to understand its working principle. Then, the dimensions of all the linkages were synthesized to meet the structural design suitable for the wearable exoskeleton and the transmission quality. In addition, motion twists and wrenches were geometrically derived. Finally, a prototype of the KULEX-wrist was designed, and then its performance of mechanical stiffness, motion capability, and power assistance was verified.
{"title":"KULEX-Wrist: Design and Analysis of Linkage-Driven Exoskeleton for Wrist Assistance","authors":"Man Bok Hong, Dukchan Yoon, Jaehyun Park, Keehoon Kim","doi":"10.1115/1.4064491","DOIUrl":"https://doi.org/10.1115/1.4064491","url":null,"abstract":"\u0000 This paper presents a wrist exoskeleton called the KULEX (KIST Upper-Limb EXoskeleton)-wrist for activities of daily living assistance of the elderly and the disabled. A novel linkage-based structure of the rotational mechanism with three degrees of freedom is proposed. The proposed wrist mechanism is composed of two PUS (Prismatic-Universal-Spherical) serial chains and one RRR (Revolute-Revolute-Revolute) spherical chain. Besides, a combination of a planar slider-crank and spherical four-bar linkages was employed as a driving mechanism for power transmission. Kinematic analysis was conducted to understand its working principle. Then, the dimensions of all the linkages were synthesized to meet the structural design suitable for the wearable exoskeleton and the transmission quality. In addition, motion twists and wrenches were geometrically derived. Finally, a prototype of the KULEX-wrist was designed, and then its performance of mechanical stiffness, motion capability, and power assistance was verified.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"12 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanwen Xu, Hao Wu, Zheng Liu, Pingfeng Wang, Yumeng Li
The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. Firstly, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Secondly, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems.
{"title":"Multi-Task Learning for Design under Uncertainty with Multi-Fidelity Partially Observed Information","authors":"Yanwen Xu, Hao Wu, Zheng Liu, Pingfeng Wang, Yumeng Li","doi":"10.1115/1.4064492","DOIUrl":"https://doi.org/10.1115/1.4064492","url":null,"abstract":"\u0000 The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. Firstly, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Secondly, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"47 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sangjoon Lee, Haris Moazam Sheikh, Dahyun Daniel Lim, Grace X Gu, Philip S. Marcus
A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing Design-by-Morphing (DbM), Large Eddy Simulation (LES), and Bayesian Optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of 3 optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8 %. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity of computational design before materialization.
{"title":"Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing with Large Eddy Simulation","authors":"Sangjoon Lee, Haris Moazam Sheikh, Dahyun Daniel Lim, Grace X Gu, Philip S. Marcus","doi":"10.1115/1.4064413","DOIUrl":"https://doi.org/10.1115/1.4064413","url":null,"abstract":"\u0000 A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing Design-by-Morphing (DbM), Large Eddy Simulation (LES), and Bayesian Optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of 3 optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8 %. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity of computational design before materialization.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"51 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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