生物医学用铬镍铁合金625线材放电加工人工智能模型的建立

IF 2.5 Q2 ENGINEERING, INDUSTRIAL
Pasupuleti Thejasree, Natarajan Manikandan, Neeraj Sunheriya, Jayant Giri, Rajkumar Chadge, T. Sathish, Ajay Kumar, Muhammad Imam Ammarullah
{"title":"生物医学用铬镍铁合金625线材放电加工人工智能模型的建立","authors":"Pasupuleti Thejasree,&nbsp;Natarajan Manikandan,&nbsp;Neeraj Sunheriya,&nbsp;Jayant Giri,&nbsp;Rajkumar Chadge,&nbsp;T. Sathish,&nbsp;Ajay Kumar,&nbsp;Muhammad Imam Ammarullah","doi":"10.1049/cim2.70015","DOIUrl":null,"url":null,"abstract":"<p>Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro-Fuzzy Inference System for forecasting WEDM parameters using grey-based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision-making in the production of high-quality biomedical devices and components.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 4","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70015","citationCount":"0","resultStr":"{\"title\":\"Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications\",\"authors\":\"Pasupuleti Thejasree,&nbsp;Natarajan Manikandan,&nbsp;Neeraj Sunheriya,&nbsp;Jayant Giri,&nbsp;Rajkumar Chadge,&nbsp;T. Sathish,&nbsp;Ajay Kumar,&nbsp;Muhammad Imam Ammarullah\",\"doi\":\"10.1049/cim2.70015\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro-Fuzzy Inference System for forecasting WEDM parameters using grey-based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision-making in the production of high-quality biomedical devices and components.</p>\",\"PeriodicalId\":33286,\"journal\":{\"name\":\"IET Collaborative Intelligent Manufacturing\",\"volume\":\"6 4\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70015\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Collaborative Intelligent Manufacturing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cim2.70015\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.70015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0

摘要

高温合金,特别是镍合金,如Inconel 625,由于其卓越的机械性能和耐腐蚀性,越来越多地用于生物医学工程,用于制造植入物和手术器械等关键部件。然而,由于这些材料的高强度和导热性,传统的加工方法经常与这些材料作斗争。本研究探讨了线切割加工(WEDM)作为一种先进的方法在生物医学领域加工Inconel 625的应用。作者开发了一种自适应神经模糊推理系统,用于利用灰色数据预测电火花线切割参数。模型的变量输入通过方差分析(ANOVA)和田口设计进行分析,旨在优化与生物医学应用相关的过程性能属性。预测数据与实验数据的对比研究表明,该模型具有较高的精度,有效地提高了加工精度。结果表明,该智能系统支持高质量生物医学设备和部件的生产决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications

Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications

Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro-Fuzzy Inference System for forecasting WEDM parameters using grey-based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision-making in the production of high-quality biomedical devices and components.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信