Chao Yang, Peijiao Li, Yang Wang, Wei Ye, Tianze Sun, Fengli Huang, Hui Zhang
{"title":"Multi-objective optimization design of parallel manipulators using a neural network and principal component analysis","authors":"Chao Yang, Peijiao Li, Yang Wang, Wei Ye, Tianze Sun, Fengli Huang, Hui Zhang","doi":"10.5194/ms-14-361-2023","DOIUrl":null,"url":null,"abstract":"Abstract. In this work, a multi-objective optimization design method is proposed based on principal component analysis (PCA) and a neural network to obtain a mechanism's optimal comprehensive performance. First,\nmulti-objective optimization mathematical modeling, including design\nparameters, objective functions, and constraint functions, is established.\nSecond, the sample data are obtained through the design of the experiment\n(DOE) and are then standardized to eliminate the adverse effects of a\nnon-uniform dimension of objective functions. Third, the first k principal components are established for p performance indices (k<p) using the\nvariance-based PCA method, and then the factor analysis method is employed\nto define its physical meaning. Fourth, the overall comprehensive\nperformance evaluation index is established by objectively determining\nweight factors. Finally, the computational cost of the modeling is improved\nby combining the neural network and a particle swarm optimization (PSO)\nalgorithm. Dimensional synthesis of a Sprint (3RPS) parallel manipulator (PM) is taken as a case study to implement the proposed method, and the\noptimization results are verified by a comprehensive performance comparison of robots before and after optimization.\n","PeriodicalId":18413,"journal":{"name":"Mechanical Sciences","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanical Sciences","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5194/ms-14-361-2023","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. In this work, a multi-objective optimization design method is proposed based on principal component analysis (PCA) and a neural network to obtain a mechanism's optimal comprehensive performance. First,
multi-objective optimization mathematical modeling, including design
parameters, objective functions, and constraint functions, is established.
Second, the sample data are obtained through the design of the experiment
(DOE) and are then standardized to eliminate the adverse effects of a
non-uniform dimension of objective functions. Third, the first k principal components are established for p performance indices (k
期刊介绍:
The journal Mechanical Sciences (MS) is an international forum for the dissemination of original contributions in the field of theoretical and applied mechanics. Its main ambition is to provide a platform for young researchers to build up a portfolio of high-quality peer-reviewed journal articles. To this end we employ an open-access publication model with moderate page charges, aiming for fast publication and great citation opportunities. A large board of reputable editors makes this possible. The journal will also publish special issues dealing with the current state of the art and future research directions in mechanical sciences. While in-depth research articles are preferred, review articles and short communications will also be considered. We intend and believe to provide a means of publication which complements established journals in the field.