To reduce the wear, an optimization method of hypoid gears with the objective of minimizing the pinion sliding ratio is proposed. Firstly, the sliding ratio model of the hypoid gear is established on the basis of the spatial gear meshing theory. Furthermore, the influence of design parameters on the sliding ratio and the relative sliding velocity is discussed, and the analysis results show that the parameters, especially the spiral angle and the pressure angle, have the most significant influence on the sliding ratio of the pinion. Additionally, the optimization model of hypoid gears is established with the objective of minimizing the sum of the absolute values of the sliding ratio for the 34 meshing points on the two tooth surfaces of the pinion, through comparison before and after optimization, it is found that the maximum drops of the sliding ratio for the pinion drive and coast side are 68.6% and 29.58% respectively. Finally, the results of the operating temperature test demonstrate that the temperature of the optimized gear pair is significantly reduced, and that the proposed method is effective.
{"title":"Optimization method of design parameters of hypoid gears with low sliding ratio","authors":"Y. Zhang, Zhiyong Wang, Hong-zhi Yan","doi":"10.1115/1.4062880","DOIUrl":"https://doi.org/10.1115/1.4062880","url":null,"abstract":"\u0000 To reduce the wear, an optimization method of hypoid gears with the objective of minimizing the pinion sliding ratio is proposed. Firstly, the sliding ratio model of the hypoid gear is established on the basis of the spatial gear meshing theory. Furthermore, the influence of design parameters on the sliding ratio and the relative sliding velocity is discussed, and the analysis results show that the parameters, especially the spiral angle and the pressure angle, have the most significant influence on the sliding ratio of the pinion. Additionally, the optimization model of hypoid gears is established with the objective of minimizing the sum of the absolute values of the sliding ratio for the 34 meshing points on the two tooth surfaces of the pinion, through comparison before and after optimization, it is found that the maximum drops of the sliding ratio for the pinion drive and coast side are 68.6% and 29.58% respectively. Finally, the results of the operating temperature test demonstrate that the temperature of the optimized gear pair is significantly reduced, and that the proposed method is effective.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"19 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74580737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work introduces the Proficient Simulated Annealing Design Agent Model (PSADA), a cognitively inspired, agent-based model of engineering configuration design. PSADA models different proficiency agents using move selection heuristics and problem space search strategies, both of which are identified and extracted from prior human subject studies. The model is validated with two design problems. Agents are compared to human designers and show the accurate simulation of the behaviors of the different proficiency designers. These behavior differences lead to significantly different performance levels, matching the human performance levels with just one exception. These validated heterogeneous agents are placed into teams and confirmed previous findings that the most proficient member of a configuration design team has the largest impact (positive or negative) on team performance. The PSADA model is introduced as a scalable platform to further explore configuration design proficiency's role in design team performance and organizational behavior.
{"title":"A COMPUTATIONAL MODEL OF HUMAN PROFICIENCY IN ENGINEERING CONFIGURATION DESIGN","authors":"Ethan Brownell, J. Cagan, K. Kotovsky","doi":"10.1115/1.4062861","DOIUrl":"https://doi.org/10.1115/1.4062861","url":null,"abstract":"\u0000 This work introduces the Proficient Simulated Annealing Design Agent Model (PSADA), a cognitively inspired, agent-based model of engineering configuration design. PSADA models different proficiency agents using move selection heuristics and problem space search strategies, both of which are identified and extracted from prior human subject studies. The model is validated with two design problems. Agents are compared to human designers and show the accurate simulation of the behaviors of the different proficiency designers. These behavior differences lead to significantly different performance levels, matching the human performance levels with just one exception. These validated heterogeneous agents are placed into teams and confirmed previous findings that the most proficient member of a configuration design team has the largest impact (positive or negative) on team performance. The PSADA model is introduced as a scalable platform to further explore configuration design proficiency's role in design team performance and organizational behavior.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"1 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82405456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Life-cycle reliability analysis can effectively estimate and present the changes in the state of safety for structures under dynamic uncertainties during their lifecycle. The first-crossing approach is an efficient way to evaluate time-variant reliability-based on the probabilistic characteristics of the first-crossing time point (FCTP). However, the FCTP model has a number of critical challenges, such as computational accuracy. This paper proposes an adaptive first-crossing approach for the time-varying reliability of structures over their whole lifecycle, which can provide a tool for cycle-life reliability analysis and design. The response surface of FCTP regarding input variables is first estimated by performing support vector regression. Furthermore, the adaptive learning algorithm for training support vector regression is developed by integrating the uniform design and the central moments of the surrogate model. Then, the convergence condition, which combines the raw moments and entropy of the first-crossing probability distribution function (PDF), is constructed to build the optimal first-crossing surrogate model. Finally, the first-crossing PDF is solved using the adaptive kernel density estimation to obtain the time-variant reliability trend during the whole lifecycle. Examples are demonstrated to specify the proposed method in applications.
{"title":"Adaptive First-Crossing Approach for Life-Cycle Reliability Analysis","authors":"Shuijuan Yu, Peng Guo, X. Wu","doi":"10.1115/1.4062732","DOIUrl":"https://doi.org/10.1115/1.4062732","url":null,"abstract":"\u0000 Life-cycle reliability analysis can effectively estimate and present the changes in the state of safety for structures under dynamic uncertainties during their lifecycle. The first-crossing approach is an efficient way to evaluate time-variant reliability-based on the probabilistic characteristics of the first-crossing time point (FCTP). However, the FCTP model has a number of critical challenges, such as computational accuracy. This paper proposes an adaptive first-crossing approach for the time-varying reliability of structures over their whole lifecycle, which can provide a tool for cycle-life reliability analysis and design. The response surface of FCTP regarding input variables is first estimated by performing support vector regression. Furthermore, the adaptive learning algorithm for training support vector regression is developed by integrating the uniform design and the central moments of the surrogate model. Then, the convergence condition, which combines the raw moments and entropy of the first-crossing probability distribution function (PDF), is constructed to build the optimal first-crossing surrogate model. Finally, the first-crossing PDF is solved using the adaptive kernel density estimation to obtain the time-variant reliability trend during the whole lifecycle. Examples are demonstrated to specify the proposed method in applications.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"59 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73790953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G-Lattices (proposed by Armanfar and Gunpinar) are a group of novel periodic and strut-based lattice structures for additive manufacturing. It has been demonstrated that these structures have superior mechanical properties under compression compared to conventional lattice structures. This paper introduces an extension of G-Lattices (i.e., reinforced G-Lattices) that also have better mechanical performance under inclined (compression) loading conditions. For different inclined loads, separate reinforced G-Lattices are first optimized, and a G-Lattice library is formed. For a part under loading, displacement vectors in each unit cell (cubic domains within inner region of the part) are then extracted. Based on these vectors, (pre-optimized) reinforced G-Lattices are selected from the G-Lattice library and utilized (as infills) in the unit cells. This process is called G-Puzzling. As a proof of concept, parts under three different inclined loading conditions are infilled using reinforced G-Lattices and investigated based on stiffness-over-volume ratios. According to these experiments, the resulting parts, on average, exhibit more than %30 better mechanical performance compared to FBCCZ (a conventional lattice structure).
{"title":"G-Puzzle: Infilling 3D Models with Reinforced G-Lattices","authors":"Arash Armanfar, E. Ustundag, Erkan Gunpinar","doi":"10.1115/1.4062832","DOIUrl":"https://doi.org/10.1115/1.4062832","url":null,"abstract":"\u0000 G-Lattices (proposed by Armanfar and Gunpinar) are a group of novel periodic and strut-based lattice structures for additive manufacturing. It has been demonstrated that these structures have superior mechanical properties under compression compared to conventional lattice structures. This paper introduces an extension of G-Lattices (i.e., reinforced G-Lattices) that also have better mechanical performance under inclined (compression) loading conditions. For different inclined loads, separate reinforced G-Lattices are first optimized, and a G-Lattice library is formed. For a part under loading, displacement vectors in each unit cell (cubic domains within inner region of the part) are then extracted. Based on these vectors, (pre-optimized) reinforced G-Lattices are selected from the G-Lattice library and utilized (as infills) in the unit cells. This process is called G-Puzzling. As a proof of concept, parts under three different inclined loading conditions are infilled using reinforced G-Lattices and investigated based on stiffness-over-volume ratios. According to these experiments, the resulting parts, on average, exhibit more than %30 better mechanical performance compared to FBCCZ (a conventional lattice structure).","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"7 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89765483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hongrui Chen, Aditya Joglekar, Kate S. Whitefoot, Levent Burak Kara
Abstract Without an explicit formulation to minimize support structures, topology optimization may create complex shapes that require an intensive use of support material when additively manufactured. We propose a neural network-based approach to topology optimization that aims to reduce the use of support structures in additive manufacturing. Our approach uses a network architecture that allows the simultaneous determination of an optimized: (1) part segmentation, (2) the topology of each part, and (3) the build direction of each part that collectively minimize the amount of support structure. Through training, the network learns a material density and segment classification in the continuous 3D space. Given a problem domain with prescribed load and displacement boundary conditions, the neural network takes as input 3D coordinates of the voxelized domain as training samples and outputs a continuous density field. Since the neural network for topology optimization learns the density distribution field, analytical solutions to the density gradient can be obtained from the input–output relationship of the neural network. We demonstrate our approach on several compliance minimization problems with volume fraction constraints, where support volume minimization is added as an additional criterion to the objective function. We show that simultaneous optimization of part segmentation along with the topology and print angle optimization further reduces the support structure, compared to a combined print angle and topology optimization without segmentation.
{"title":"Concurrent Build Direction, Part Segmentation, and Topology Optimization for Additive Manufacturing Using Neural Networks","authors":"Hongrui Chen, Aditya Joglekar, Kate S. Whitefoot, Levent Burak Kara","doi":"10.1115/1.4062663","DOIUrl":"https://doi.org/10.1115/1.4062663","url":null,"abstract":"Abstract Without an explicit formulation to minimize support structures, topology optimization may create complex shapes that require an intensive use of support material when additively manufactured. We propose a neural network-based approach to topology optimization that aims to reduce the use of support structures in additive manufacturing. Our approach uses a network architecture that allows the simultaneous determination of an optimized: (1) part segmentation, (2) the topology of each part, and (3) the build direction of each part that collectively minimize the amount of support structure. Through training, the network learns a material density and segment classification in the continuous 3D space. Given a problem domain with prescribed load and displacement boundary conditions, the neural network takes as input 3D coordinates of the voxelized domain as training samples and outputs a continuous density field. Since the neural network for topology optimization learns the density distribution field, analytical solutions to the density gradient can be obtained from the input–output relationship of the neural network. We demonstrate our approach on several compliance minimization problems with volume fraction constraints, where support volume minimization is added as an additional criterion to the objective function. We show that simultaneous optimization of part segmentation along with the topology and print angle optimization further reduces the support structure, compared to a combined print angle and topology optimization without segmentation.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135903221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The solution to global (a posteriori) multi-objective optimization problems traditionally relies on population-based algorithms, which are very effective in generating a Pareto front. Unfortunately, due to the high number of function evaluations, these methods are of limited use in problems that involve expensive black-box functions. In recent years, multi-objective Bayesian optimization has emerged as a powerful alternative; however, in many applications, these methods fail to generate a diverse and well-spread Pareto front. To address this limitation, our work introduces a novel acquisition function (AF) for multi-objective Bayesian optimization that produces more informative acquisition landscapes. The proposed AF comprises two terms, namely, a distance-based metric and a diversity index. The distance-based metric, referred to as the expected Pareto distance change, promotes the evaluation of high-performing designs and repels low-performing design zones. The diversity term prevents the evaluation of designs that are similar to the ones contained in the current sampling plan. The proposed AF is studied using seven analytical problems and in the design optimization of sandwich composite armors for blast mitigation, which involves expensive finite element simulations. The results show that the proposed AF generates high-quality Pareto sets outperforming well-established methods such as the Euclidean-based expected improvement function. The proposed AF is also compared with respect to a recently proposed multi-objective approach. The difference in their performance is problem dependent.
{"title":"Multi-objective Bayesian Optimization Supported by an Expected Pareto Distance Change","authors":"H. Valladares, A. Tovar","doi":"10.1115/1.4062789","DOIUrl":"https://doi.org/10.1115/1.4062789","url":null,"abstract":"\u0000 The solution to global (a posteriori) multi-objective optimization problems traditionally relies on population-based algorithms, which are very effective in generating a Pareto front. Unfortunately, due to the high number of function evaluations, these methods are of limited use in problems that involve expensive black-box functions. In recent years, multi-objective Bayesian optimization has emerged as a powerful alternative; however, in many applications, these methods fail to generate a diverse and well-spread Pareto front. To address this limitation, our work introduces a novel acquisition function (AF) for multi-objective Bayesian optimization that produces more informative acquisition landscapes. The proposed AF comprises two terms, namely, a distance-based metric and a diversity index. The distance-based metric, referred to as the expected Pareto distance change, promotes the evaluation of high-performing designs and repels low-performing design zones. The diversity term prevents the evaluation of designs that are similar to the ones contained in the current sampling plan. The proposed AF is studied using seven analytical problems and in the design optimization of sandwich composite armors for blast mitigation, which involves expensive finite element simulations. The results show that the proposed AF generates high-quality Pareto sets outperforming well-established methods such as the Euclidean-based expected improvement function. The proposed AF is also compared with respect to a recently proposed multi-objective approach. The difference in their performance is problem dependent.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"91 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73907137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate analysis of rare failure events with an affordable computational cost is often challenging in many engineering applications, particularly for problems with high dimensional system inputs. The extremely low probabilities occurrences often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. The general method of determining confidence intervals of an estimator using the central limit theorem faces the critical obstacle of low computational efficiency. This is a side-effect of the widely used Monte Carlo method, which often requires a large number of simulation samples to derive a reasonably narrow confidence interval. In this paper a new probability analysis approach is developed which can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems and complex engineering systems. The asymptotic behavior of the developed estimator is proven theoretically without imposing strong assumptions. An asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be obtained efficiently using the developed approach with the true values consistently located within the estimation bounds.
{"title":"Sequential Sampling Based Asymptotic Probability Estimation for High Dimensional Rare Events","authors":"Yanwen Xu, Pingfeng Wang","doi":"10.1115/1.4062790","DOIUrl":"https://doi.org/10.1115/1.4062790","url":null,"abstract":"\u0000 Accurate analysis of rare failure events with an affordable computational cost is often challenging in many engineering applications, particularly for problems with high dimensional system inputs. The extremely low probabilities occurrences often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. The general method of determining confidence intervals of an estimator using the central limit theorem faces the critical obstacle of low computational efficiency. This is a side-effect of the widely used Monte Carlo method, which often requires a large number of simulation samples to derive a reasonably narrow confidence interval. In this paper a new probability analysis approach is developed which can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems and complex engineering systems. The asymptotic behavior of the developed estimator is proven theoretically without imposing strong assumptions. An asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be obtained efficiently using the developed approach with the true values consistently located within the estimation bounds.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"79 7 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85405386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We are pleased to announce the two (2) winners for the Journal of Mechanical Design 2022 Editors’ Choice Paper Award:In the Category of Design Methods:Eamon Whalen and Caitlin Mueller (September 21, 2021). “Toward Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses.” ASME. J. Mech. Des. February 2022; 144(2): 021704. https://doi.org/10.1115/1.4052298In the Category of Machine Design:Abdullah Aamir Hayat, Lim Yi, Manivannan Kalimuthu, M. R. Elara, and Kristin L. Wood (February 15, 2022). “Reconfigurable Robotic System Design With Application to Cleaning and Maintenance.” ASME. J. Mech. Des. June 2022; 144(6): 063305. https://doi.org/10.1115/1.4053631In addition, one (1) paper was awarded an Honorable Mention in the Category of Machine Design:Merel van Diepen and Kristina Shea (June 13, 2022). “Co-Design of the Morphology and Actuation of Soft Robots for Locomotion.” ASME. J. Mech. Des. August 2022; 144(8): 083305. https://doi.org/10.1115/1.4054522The selection of these papers was based on the recommendations of the Associate and Guest Editors and guided by the following criteria: (i) fundamental value of the contribution, (ii) expectation of archival value (e.g., expected number of citations), (iii) practical relevance to mechanical design, and (iv) quality of presentation. Nominated papers were considered in two category tracks by two separate ad hoc committees: one for design methods and one for machine design. The paper by Whalen et al. was awarded in the category of design methods and the papers by Hayat et al. and van Diepen et al. were awarded in the category of machine design.Plaques will be awarded to each of the authors of the Editors’ Choice Award, and certificates will be awarded to the authors of the paper with an Honorable Mention. We would like to congratulate all the award recipients and look forward to continuing to work with the entire ASME community of editors, authors, reviewers, and staff to bring the Journal of Mechanical Design to the next level of excellence.
我们很高兴地宣布，机械设计杂志2022年编辑选择论文奖的两(2)名获奖者:在设计方法类别:Eamon Whalen和Caitlin Mueller(2021年9月21日)。迈向可重用代理模型:基于图的桁架迁移学习。ASME。j .机械工程。2022年2月9日;144(2): 021704。https://doi.org/10.1115/1.4052298In机械设计类:Abdullah Aamir Hayat, Lim Yi, Manivannan Kalimuthu, M. R. Elara和Kristin L. Wood(2022年2月15日)。可重构机器人系统设计及其在清洁和维护中的应用ASME。j .机械工程。2022年6月6日;144(6): 063305。https://doi.org/10.1115/1.4053631In此外，一(1)篇论文在机器设计类别中获得荣誉奖:Merel van Diepen和Kristina Shea(2022年6月13日)。软体机器人运动形态与驱动的协同设计ASME。j .机械工程。2022年8月8日;144(8): 083305。https://doi.org/10.1115/1.4054522The这些论文的选择是基于副编辑和客座编辑的建议，并以以下标准为指导:(i)贡献的基本价值，(ii)档案价值的预期(例如，引用的预期数量)，(iii)与机械设计的实际相关性，以及(iv)展示质量。提名论文由两个独立的特设委员会分两个类别进行审议:一个是设计方法，一个是机器设计。Whalen等人的论文获得了设计方法类的奖项，Hayat等人和van Diepen等人的论文获得了机器设计类的奖项。每位获得编辑选择奖的作者将获得奖牌，论文的作者将获得荣誉奖的证书。我们要祝贺所有获奖者，并期待继续与整个ASME社区的编辑、作者、审稿人和工作人员合作，将《机械设计杂志》推向一个新的卓越水平。
{"title":"Announcing the Journal of Mechanical Design 2022 Editors’ Choice Paper Awards and Honorable Mention","authors":"Carolyn Seepersad, Qiaode Jeffrey Ge","doi":"10.1115/1.4062670","DOIUrl":"https://doi.org/10.1115/1.4062670","url":null,"abstract":"We are pleased to announce the two (2) winners for the Journal of Mechanical Design 2022 Editors’ Choice Paper Award:In the Category of Design Methods:Eamon Whalen and Caitlin Mueller (September 21, 2021). “Toward Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses.” ASME. J. Mech. Des. February 2022; 144(2): 021704. https://doi.org/10.1115/1.4052298In the Category of Machine Design:Abdullah Aamir Hayat, Lim Yi, Manivannan Kalimuthu, M. R. Elara, and Kristin L. Wood (February 15, 2022). “Reconfigurable Robotic System Design With Application to Cleaning and Maintenance.” ASME. J. Mech. Des. June 2022; 144(6): 063305. https://doi.org/10.1115/1.4053631In addition, one (1) paper was awarded an Honorable Mention in the Category of Machine Design:Merel van Diepen and Kristina Shea (June 13, 2022). “Co-Design of the Morphology and Actuation of Soft Robots for Locomotion.” ASME. J. Mech. Des. August 2022; 144(8): 083305. https://doi.org/10.1115/1.4054522The selection of these papers was based on the recommendations of the Associate and Guest Editors and guided by the following criteria: (i) fundamental value of the contribution, (ii) expectation of archival value (e.g., expected number of citations), (iii) practical relevance to mechanical design, and (iv) quality of presentation. Nominated papers were considered in two category tracks by two separate ad hoc committees: one for design methods and one for machine design. The paper by Whalen et al. was awarded in the category of design methods and the papers by Hayat et al. and van Diepen et al. were awarded in the category of machine design.Plaques will be awarded to each of the authors of the Editors’ Choice Award, and certificates will be awarded to the authors of the paper with an Honorable Mention. We would like to congratulate all the award recipients and look forward to continuing to work with the entire ASME community of editors, authors, reviewers, and staff to bring the Journal of Mechanical Design to the next level of excellence.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135711067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qi Zhang, Yizhong Wu, Ping Qiao, Li Lu, Zhehao Xia
Abstract When solving the black-box dynamic optimization problem (BDOP) in the sophisticated dynamic system, the finite difference technique requires significant computational efforts on numerous expensive system simulations to provide approximate numerical Jacobian information for the gradient-based optimizer. To save computational budget, this work introduces a BDOP solving framework based on the right-hand side (RHS) function surrogate model (RHSFSM), in which the RHS derivative functions of the state equation are approximated by the surrogate models, and the Jacobian information is provided by inexpensive estimations of RHSFSM rather than the original time-consuming system evaluations. Meanwhile, the sampling strategies applicable to the construction of RHSFSM are classified into three categories: direct, indirect, and hybrid sampling strategy, and the properties of these strategies are analyzed and compared. Furthermore, to assist the RHSFSM-based BDOP solving framework search for the optimum efficiently, a novel dynamic hybrid sampling strategy is proposed to update RHSFSM sequentially. Finally, two dynamic optimization examples and a co-design example of a horizontal axis wind turbine illustrate that the RHSFSM-based BDOP solving framework integrated with the proposed dynamic hybrid sampling strategy not only solves the BDOP efficiently but also achieves the optimal solution robustly and reliably compared to other sampling strategies.
{"title":"A Right-Hand Side Function Surrogate Model-Based Method for the Black-Box Dynamic Optimization Problem","authors":"Qi Zhang, Yizhong Wu, Ping Qiao, Li Lu, Zhehao Xia","doi":"10.1115/1.4062641","DOIUrl":"https://doi.org/10.1115/1.4062641","url":null,"abstract":"Abstract When solving the black-box dynamic optimization problem (BDOP) in the sophisticated dynamic system, the finite difference technique requires significant computational efforts on numerous expensive system simulations to provide approximate numerical Jacobian information for the gradient-based optimizer. To save computational budget, this work introduces a BDOP solving framework based on the right-hand side (RHS) function surrogate model (RHSFSM), in which the RHS derivative functions of the state equation are approximated by the surrogate models, and the Jacobian information is provided by inexpensive estimations of RHSFSM rather than the original time-consuming system evaluations. Meanwhile, the sampling strategies applicable to the construction of RHSFSM are classified into three categories: direct, indirect, and hybrid sampling strategy, and the properties of these strategies are analyzed and compared. Furthermore, to assist the RHSFSM-based BDOP solving framework search for the optimum efficiently, a novel dynamic hybrid sampling strategy is proposed to update RHSFSM sequentially. Finally, two dynamic optimization examples and a co-design example of a horizontal axis wind turbine illustrate that the RHSFSM-based BDOP solving framework integrated with the proposed dynamic hybrid sampling strategy not only solves the BDOP efficiently but also achieves the optimal solution robustly and reliably compared to other sampling strategies.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135672903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Researchers and practitioners alike agree that for companies to survive and thrive they must develop and support radical innovation. However, these ideas are complex and risky, and not all succeed. Because of this, decision makers are often left to make hard decisions in terms of which ideas can move on and which are abandoned. The goal of this article was to provide evidence on the impact of individuals’ preferences for creativity on the effectiveness of their decision making for radical ideas using principles from signal detection theory (SDT). To do this, we used data from a previous study of 2252 idea evaluations by engineering students and classified these decisions based on SDT to see if we could predict the likelihood of occurrence of hit (correct identification), miss (type 1 error), false alarm (type II error), and correct rejection. The results showed that lower levels of risk tolerance resulted in an increased likelihood that a hit occurred. On the other hand, higher levels of motivation resulted in an increased likelihood of a type I error occurring, or that an individual would more likely neglect a good idea that had a high chance of future success. Finally, increased risk tolerance resulted in an increased likelihood that type II error occurred, or that an individual would expend resources on an idea with limited likelihood of success. The results serve as empirical evidence on decision making in radically innovative tasks and provide a methodology for studying decision making in innovative design.
{"title":"Hit, Miss, or Error? Predicting Errors in Design Decision Making for Radically Innovative Ideas Using Individual Attributes","authors":"Aoran Peng, Scarlett Miller","doi":"10.1115/1.4062605","DOIUrl":"https://doi.org/10.1115/1.4062605","url":null,"abstract":"Abstract Researchers and practitioners alike agree that for companies to survive and thrive they must develop and support radical innovation. However, these ideas are complex and risky, and not all succeed. Because of this, decision makers are often left to make hard decisions in terms of which ideas can move on and which are abandoned. The goal of this article was to provide evidence on the impact of individuals’ preferences for creativity on the effectiveness of their decision making for radical ideas using principles from signal detection theory (SDT). To do this, we used data from a previous study of 2252 idea evaluations by engineering students and classified these decisions based on SDT to see if we could predict the likelihood of occurrence of hit (correct identification), miss (type 1 error), false alarm (type II error), and correct rejection. The results showed that lower levels of risk tolerance resulted in an increased likelihood that a hit occurred. On the other hand, higher levels of motivation resulted in an increased likelihood of a type I error occurring, or that an individual would more likely neglect a good idea that had a high chance of future success. Finally, increased risk tolerance resulted in an increased likelihood that type II error occurred, or that an individual would expend resources on an idea with limited likelihood of success. The results serve as empirical evidence on decision making in radically innovative tasks and provide a methodology for studying decision making in innovative design.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":"162 3-4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135050784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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