{"title":"Design-LIME: An Interpretable Visualization Method for Electric Motor Design Based on Deep Learning","authors":"Kazuhisa Iwata;Hidenori Sasaki","doi":"10.1109/ACCESS.2025.3563351","DOIUrl":null,"url":null,"abstract":"A novel visualization method for interpreting the resultant design from topology optimization (TO) is proposed. We employ a pre-trained deep learning (DL) model to predict the degree of influence of transitions from air to magnetic materials, and build an interpretable linear model to display the visualization result. The proposed method, Design-LIME, is applied for visualizing the impact of effective regions on the torque characteristics of interior permanent magnet synchronous motors (IPMSMs). Compared to conventional visualization methods based on explainable artificial intelligence (XAI), Design-LIME presents accurate and simple visualization results. Furthermore, a novel multistep TO method is proposed. The proposed TO utilizes Design-LIME to efficiently address the electromagnetic and mechanical characteristics of IPMSMs by extracting the effective region of the IPMSM characteristics. The proposed TO method improves search performance by 18.7% when compared with the conventional single-step optimization method. The proposed method enables more efficient motor designs with improved electromagnetic and mechanical performance. The proposed method contributes to the streamlining of the design process not only for motors but also for various electrical devices.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"73697-73708"},"PeriodicalIF":3.4000,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973077","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10973077/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
A novel visualization method for interpreting the resultant design from topology optimization (TO) is proposed. We employ a pre-trained deep learning (DL) model to predict the degree of influence of transitions from air to magnetic materials, and build an interpretable linear model to display the visualization result. The proposed method, Design-LIME, is applied for visualizing the impact of effective regions on the torque characteristics of interior permanent magnet synchronous motors (IPMSMs). Compared to conventional visualization methods based on explainable artificial intelligence (XAI), Design-LIME presents accurate and simple visualization results. Furthermore, a novel multistep TO method is proposed. The proposed TO utilizes Design-LIME to efficiently address the electromagnetic and mechanical characteristics of IPMSMs by extracting the effective region of the IPMSM characteristics. The proposed TO method improves search performance by 18.7% when compared with the conventional single-step optimization method. The proposed method enables more efficient motor designs with improved electromagnetic and mechanical performance. The proposed method contributes to the streamlining of the design process not only for motors but also for various electrical devices.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.