Optimization of robotic polishing process parameters for mold steel based on artificial intelligence method

IF 17.7 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2024-02-05 DOI:10.1177/09544054231221959

Ri Pan, Xiaofang Cheng, Yinhui Xie, Jun Li, Weilong Huang

{"title":"Optimization of robotic polishing process parameters for mold steel based on artificial intelligence method","authors":"Ri Pan, Xiaofang Cheng, Yinhui Xie, Jun Li, Weilong Huang","doi":"10.1177/09544054231221959","DOIUrl":null,"url":null,"abstract":"Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated. Firstly, based on XGBoost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 μm, and the maximum relative error is <9%. At the same time, when the minimum is set as the optimization objective. With the same length of polishing path, the feed rate is increased from 0.25 mm/s to 0.37 mm/s, and the efficiency is improved to 48%. The NSGA II-TOPSIS algorithm can achieve quantitative control of mold steel surface roughness after robotic polishing to improve polishing efficiency, and provide a basis for reasonable selection of processing parameters, which have certain practical value.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"78 2-3","pages":""},"PeriodicalIF":17.7000,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544054231221959","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated. Firstly, based on XGBoost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 μm, and the maximum relative error is <9%. At the same time, when the minimum is set as the optimization objective. With the same length of polishing path, the feed rate is increased from 0.25 mm/s to 0.37 mm/s, and the efficiency is improved to 48%. The NSGA II-TOPSIS algorithm can achieve quantitative control of mold steel surface roughness after robotic polishing to improve polishing efficiency, and provide a basis for reasonable selection of processing parameters, which have certain practical value.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于人工智能方法的模具钢机器人抛光工艺参数优化

为了实现机器人抛光后工件表面的定量控制，提高抛光效率，研究了一种涉及人工智能算法的两步加工优化方法。首先，基于 XGBoost 算法，提出了取决于关键参数的抛光工件表面预测模型，并通过实验验证了模型的准确性。之后，利用上述模型定量评估了各参数对粗糙度的影响。随后，根据各参数对粗糙度的影响，将粗糙度预测模型与 NSGA II-TOPSIS 算法相结合，提出了以粗糙度为导向的加工参数优化目标。为了验证所提出的加工优化方法，对模具钢样品进行了抛光实验。实验结果表明，预测粗糙度与实验粗糙度之间的最大绝对误差为 0.035 μm，最大相对误差小于 9%。同时，当优化目标设定为最小值时。在抛光路径长度不变的情况下，进给速度从 0.25 mm/s 提高到 0.37 mm/s，效率提高到 48%。NSGA II-TOPSIS 算法可实现机器人抛光后模具钢表面粗糙度的定量控制，提高抛光效率，并为合理选择加工参数提供依据，具有一定的实用价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.

期刊最新文献

Corrigendum to "Do All Isolated Traumatic Subarachnoid Hemorrhages Need to Be Transferred to a Level 1 Trauma Center?" Issue Editorial Masthead Issue Publication Information Photon Avalanching Nanoparticles Conjugated Oligoelectrolytes as Optical Probes