Industry 4.0 Adoption Using AI/ML-Driven Metamodels for High-Performance Ductile Iron Sand Casting Design and Manufacturing

IF 2.6 3区 材料科学 Q2 METALLURGY & METALLURGICAL ENGINEERING
Jiten Shah, Brian Began
{"title":"Industry 4.0 Adoption Using AI/ML-Driven Metamodels for High-Performance Ductile Iron Sand Casting Design and Manufacturing","authors":"Jiten Shah, Brian Began","doi":"10.1007/s40962-024-01338-0","DOIUrl":null,"url":null,"abstract":"<p>Data-centric near-real-time intelligent process control for smart manufacturing in an Industry 4.0 era is of tremendous value. Design and manufacturing of high-performance ductile iron sand castings is a multi-variant complex process with much uncertainty involved. As a result, in spite of a well-controlled operation and an experienced workforce, iron foundries in a production environment do face sporadic shrinkage and lots with nonconforming property requirements, resulting in scrap or rework. A framework and methodology consisting of AI (artificial intelligence) and ML (machine learning) tools, coupled with ICME (integrated computational materials engineering) and process simulation tools, will be presented to quantify uncertainty (UQ). Metamodels, both predictive and prescriptive in near real time were developed using such AI/ML techniques using historical production and selective design of experiments (DOE)-generated additional data. The data will be presented including details on successful corrective action production trials. The proposed framework and approach is applicable to solve such complex problems encountered in the foundry and machining operations where there is uncertainty.</p>","PeriodicalId":14231,"journal":{"name":"International Journal of Metalcasting","volume":"139 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Metalcasting","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s40962-024-01338-0","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Data-centric near-real-time intelligent process control for smart manufacturing in an Industry 4.0 era is of tremendous value. Design and manufacturing of high-performance ductile iron sand castings is a multi-variant complex process with much uncertainty involved. As a result, in spite of a well-controlled operation and an experienced workforce, iron foundries in a production environment do face sporadic shrinkage and lots with nonconforming property requirements, resulting in scrap or rework. A framework and methodology consisting of AI (artificial intelligence) and ML (machine learning) tools, coupled with ICME (integrated computational materials engineering) and process simulation tools, will be presented to quantify uncertainty (UQ). Metamodels, both predictive and prescriptive in near real time were developed using such AI/ML techniques using historical production and selective design of experiments (DOE)-generated additional data. The data will be presented including details on successful corrective action production trials. The proposed framework and approach is applicable to solve such complex problems encountered in the foundry and machining operations where there is uncertainty.

Abstract Image

利用人工智能/ML 驱动的元模型进行高性能球墨铸铁砂型的设计和制造,实现工业 4.0
以数据为中心的近实时智能过程控制对于工业 4.0 时代的智能制造具有巨大价值。高性能球墨铸铁砂型铸件的设计和制造是一个多变量的复杂过程,存在很多不确定性。因此,尽管有良好的操作控制和经验丰富的员工队伍,生产环境中的铸铁厂仍会面临零星的缩孔和不符合属性要求的批次,从而导致废品或返工。我们将介绍一种由 AI(人工智能)和 ML(机器学习)工具以及 ICME(集成计算材料工程)和过程模拟工具组成的框架和方法,用于量化不确定性(UQ)。利用历史生产数据和选择性实验设计 (DOE) 生成的额外数据,使用这些 AI/ML 技术开发了近乎实时的预测性和规范性元模型。将介绍这些数据,包括成功的纠正措施生产试验的详细信息。所提出的框架和方法适用于解决铸造和加工操作中遇到的不确定性复杂问题。
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 求助全文
来源期刊
International Journal of Metalcasting
International Journal of Metalcasting 工程技术-冶金工程
CiteScore
4.20
自引率
42.30%
发文量
174
审稿时长
>12 weeks
期刊介绍: The International Journal of Metalcasting is dedicated to leading the transfer of research and technology for the global metalcasting industry. The quarterly publication keeps the latest developments in metalcasting research and technology in front of the scientific leaders in our global industry throughout the year. All papers published in the the journal are approved after a rigorous peer review process. The editorial peer review board represents three international metalcasting groups: academia (metalcasting professors), science and research (personnel from national labs, research and scientific institutions), and industry (leading technical personnel from metalcasting facilities).
×
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