Journal of Mechanical Design最新文献_第3页

Modified structure of deep neural network for training multi-fidelity data with non-common input variables 改进深度神经网络结构，用于训练具有非通用输入变量的多保真数据

Journal of Mechanical Design Pub Date : 2024-02-16 DOI: 10.1115/1.4064782

Hwisang Jo, Byeong-uk Song, Joon-Yong Huh, Seungkyu Lee, Ikjin Lee

{"title":"Modified structure of deep neural network for training multi-fidelity data with non-common input variables","authors":"Hwisang Jo, Byeong-uk Song, Joon-Yong Huh, Seungkyu Lee, Ikjin Lee","doi":"10.1115/1.4064782","DOIUrl":"https://doi.org/10.1115/1.4064782","url":null,"abstract":"\u0000 Multi-fidelity surrogate (MFS) modeling technology, which efficiently constructs surrogate models using low-fidelity (LF) and high-fidelity (HF) data, has been studied to enhance the predictive capability of engineering performances. In addition, several neural network (NN) structures for MFS modeling have been introduced, benefiting from recent developments in deep learning research. However, existing multi-fidelity (MF) NNs have been developed assuming identical sets of input variables for LF and HF data, a condition that is often not met in practical engineering systems. Therefore, this study proposes a new structure of composite NN designed for MF data with different input variables. The proposed network structure includes an input mapping network that connects the LF and HF data's input variables. Even when the physical relationship between these variables is unknown, the input mapping network can be concurrently trained during the process of training the whole network model. Customized loss functions and activation variables are suggested in this study to facilitate forward and backward propagation for the proposed NN structures when training MF data with different inputs. The effectiveness of the proposed method, in terms of prediction accuracy, is demonstrated through mathematical examples and practical engineering problems related to tire performances. The results confirm that the proposed method offers better accuracy than existing surrogate models in most problems. Moreover, the proposed method proves advantageous for surrogate modeling of nonlinear or discrete functions, a characteristic feature of NN-based methods.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"58 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139960792","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}

引用次数: 0

Optimizing Engineered Products for Their Social Impacts On Multiple Stakeholders 优化工程产品对多方利益相关者的社会影响

Journal of Mechanical Design Pub Date : 2024-02-07 DOI: 10.1115/1.4064694

Phillip Stevenson, Christopher A. Mattson, John Salmon, Nile Hatch

引用次数: 0

Optimizing Engineered Products for Their Social Impacts On Multiple Stakeholders 优化工程产品对多方利益相关者的社会影响

Journal of Mechanical Design Pub Date : 2024-02-07 DOI: 10.1115/1.4064694

Phillip Stevenson, Christopher A. Mattson, John Salmon, Nile Hatch

引用次数: 0

ERGO-II: An Improved Bayesian Optimization Technique for Robust Design with Multiple Objectives, Failed Evaluations and Stochastic Parameters ERGO-II：一种改进的贝叶斯优化技术，用于多目标、失败评估和随机参数的稳健设计

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064674

Jolan Wauters

{"title":"ERGO-II: An Improved Bayesian Optimization Technique for Robust Design with Multiple Objectives, Failed Evaluations and Stochastic Parameters","authors":"Jolan Wauters","doi":"10.1115/1.4064674","DOIUrl":"https://doi.org/10.1115/1.4064674","url":null,"abstract":"\u0000 In this work, the Efficient Robust Global Optimization (ERGO) method is revisited with the aim of enhancing and expanding its existing capabilities. The original objective of ERGO was to address the computational challenges associated with optimization-under-uncertainty through the use of Bayesian optimization (BO). ERGO tackles robust optimization problems which are characterized by sensitivity in the objective function due to stochasticity in the design space. It does this by concurrently minimizing the mean and variance of the objective in a multi-objective setting. To handle the computational complexity arising from the uncertainty propagation, ERGO exploits the analytical expression of the surrogate model underlying BO. In this study, ERGO is extended to accommodate multiple objectives, incorporate an improved predictive error estimation approach, investigate the treatment of failed function evaluations, and explore the handling of stochastic parameters next to stochastic design variables. To evaluate the effectiveness of these improvements, the enhanced ERGO scheme is compared with the original method using an analytical test problem with varying dimensionality. Additionally, the novel optimization technique is applied to an aerodynamic design problem to validate its performance.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139862316","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}

引用次数: 0

A Comparative Analysis of Student Perceptions of Recommendations for Engagement in Design Processes 学生对参与设计过程的建议的比较分析

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064671

K. Dugan, Shanna Daly

{"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":"100 s5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139801283","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}

引用次数: 0

DESIGN OF SELF-ORGANIZING SYSTEMS USING MULTI-AGENT REINFORCEMENT LEARNING AND THE COMPROMISE DECISION SUPPORT PROBLEM CONSTRUCT 利用多代理强化学习和折中决策支持问题结构设计自组织系统

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064672

Mingfei Jiang, Z. Ming, Chuanhao Li, J. Allen, F. Mistree

{"title":"DESIGN OF SELF-ORGANIZING SYSTEMS USING MULTI-AGENT REINFORCEMENT LEARNING AND THE COMPROMISE DECISION SUPPORT PROBLEM CONSTRUCT","authors":"Mingfei Jiang, Z. Ming, Chuanhao Li, J. Allen, F. Mistree","doi":"10.1115/1.4064672","DOIUrl":"https://doi.org/10.1115/1.4064672","url":null,"abstract":"\u0000 In this paper, we address the following question: How can multi-robot self-organizing systems be designed so that they show the desired behaviors and are able to perform tasks specified by designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of the system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. In this paper we present a framework that consists of two stages, namely, preliminary design and design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to explore the design space and identify satisfactory solutions considering several performance indicators. Between the two stages, surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in preliminary design. A multi-robot box-pushing problem is used as an example to test the efficacy of the framework. The framework is general and can be extended to design other self-organizing systems. Our focus in this paper is in describing the framework.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"12 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139859327","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}

引用次数: 0

MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations MeshPointNet：使用图形神经网络和基于网格表征的共形预测进行三维表面分类

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064673

Amin Heyrani Nobari, Justin Rey, S. Kodali, Matthew Jones, Faez Ahmed

{"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":"14 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799077","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}

引用次数: 0

ERGO-II: An Improved Bayesian Optimization Technique for Robust Design with Multiple Objectives, Failed Evaluations and Stochastic Parameters ERGO-II：一种改进的贝叶斯优化技术，用于多目标、失败评估和随机参数的稳健设计

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064674

Jolan Wauters

{"title":"ERGO-II: An Improved Bayesian Optimization Technique for Robust Design with Multiple Objectives, Failed Evaluations and Stochastic Parameters","authors":"Jolan Wauters","doi":"10.1115/1.4064674","DOIUrl":"https://doi.org/10.1115/1.4064674","url":null,"abstract":"\u0000 In this work, the Efficient Robust Global Optimization (ERGO) method is revisited with the aim of enhancing and expanding its existing capabilities. The original objective of ERGO was to address the computational challenges associated with optimization-under-uncertainty through the use of Bayesian optimization (BO). ERGO tackles robust optimization problems which are characterized by sensitivity in the objective function due to stochasticity in the design space. It does this by concurrently minimizing the mean and variance of the objective in a multi-objective setting. To handle the computational complexity arising from the uncertainty propagation, ERGO exploits the analytical expression of the surrogate model underlying BO. In this study, ERGO is extended to accommodate multiple objectives, incorporate an improved predictive error estimation approach, investigate the treatment of failed function evaluations, and explore the handling of stochastic parameters next to stochastic design variables. To evaluate the effectiveness of these improvements, the enhanced ERGO scheme is compared with the original method using an analytical test problem with varying dimensionality. Additionally, the novel optimization technique is applied to an aerodynamic design problem to validate its performance.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139802380","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}

引用次数: 0

DESIGN OF SELF-ORGANIZING SYSTEMS USING MULTI-AGENT REINFORCEMENT LEARNING AND THE COMPROMISE DECISION SUPPORT PROBLEM CONSTRUCT 利用多代理强化学习和折中决策支持问题结构设计自组织系统

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064672

Mingfei Jiang, Z. Ming, Chuanhao Li, J. Allen, F. Mistree

{"title":"DESIGN OF SELF-ORGANIZING SYSTEMS USING MULTI-AGENT REINFORCEMENT LEARNING AND THE COMPROMISE DECISION SUPPORT PROBLEM CONSTRUCT","authors":"Mingfei Jiang, Z. Ming, Chuanhao Li, J. Allen, F. Mistree","doi":"10.1115/1.4064672","DOIUrl":"https://doi.org/10.1115/1.4064672","url":null,"abstract":"\u0000 In this paper, we address the following question: How can multi-robot self-organizing systems be designed so that they show the desired behaviors and are able to perform tasks specified by designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of the system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. In this paper we present a framework that consists of two stages, namely, preliminary design and design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to explore the design space and identify satisfactory solutions considering several performance indicators. Between the two stages, surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in preliminary design. A multi-robot box-pushing problem is used as an example to test the efficacy of the framework. The framework is general and can be extended to design other self-organizing systems. Our focus in this paper is in describing the framework.","PeriodicalId":506672,"journal":{"name":"Journal of Mechanical Design","volume":"279 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139799380","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}

引用次数: 0

A Sustainable Product Multi-platform Planning Model for Assembly and Disassembly Process 针对组装和拆卸过程的可持续产品多平台规划模型

Journal of Mechanical Design Pub Date : 2024-02-06 DOI: 10.1115/1.4064675

Guang-yu Zou, Zhongkai Li, Chao He

引用次数: 0