Enhanced Classification of Refractory Coatings in Foundries: A VPCA-Based Machine Learning Approach

IF 2.6 3区 材料科学 Q2 METALLURGY & METALLURGICAL ENGINEERING
Ronit Shetty, Ahmad Al Majali, Lee Wells
{"title":"Enhanced Classification of Refractory Coatings in Foundries: A VPCA-Based Machine Learning Approach","authors":"Ronit Shetty, Ahmad Al Majali, Lee Wells","doi":"10.1007/s40962-024-01427-0","DOIUrl":null,"url":null,"abstract":"<p>This paper introduces a thorough approach for classifying refractory coatings used on chemically bonded sand according to their thickness, which is essential for monitoring mold and core coatings in foundries. The method combines feature extraction through vectorized principal component analysis (VPCA) with classification modeling using a machine learning algorithm. The study examines five different scenarios, which involve the utilization of raw axial, radial, and temperature data, as well as the use of scalar properties. Additionally, the study involves extracting features from the first two approaches and training on the complete dataset. An assessment of performance is carried out, showcasing the strong ability to classify accurately across all levels of coating thickness. In addition, Hotelling's T-squared statistics are used to identify changes in the process, offering valuable information about the structure and distinctiveness of the data classes. This study demonstrates the efficacy of feature extraction methods and machine learning algorithms in accurately categorizing coating thicknesses, providing practical solutions for applications in the foundry industry. This systematic methodology not only improves the comprehensibility and effectiveness of classification models but also offers vital understanding into process monitoring and identification of abnormalities within intricate datasets.</p>","PeriodicalId":14231,"journal":{"name":"International Journal of Metalcasting","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-08-24","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/s40962-024-01427-0","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

This paper introduces a thorough approach for classifying refractory coatings used on chemically bonded sand according to their thickness, which is essential for monitoring mold and core coatings in foundries. The method combines feature extraction through vectorized principal component analysis (VPCA) with classification modeling using a machine learning algorithm. The study examines five different scenarios, which involve the utilization of raw axial, radial, and temperature data, as well as the use of scalar properties. Additionally, the study involves extracting features from the first two approaches and training on the complete dataset. An assessment of performance is carried out, showcasing the strong ability to classify accurately across all levels of coating thickness. In addition, Hotelling's T-squared statistics are used to identify changes in the process, offering valuable information about the structure and distinctiveness of the data classes. This study demonstrates the efficacy of feature extraction methods and machine learning algorithms in accurately categorizing coating thicknesses, providing practical solutions for applications in the foundry industry. This systematic methodology not only improves the comprehensibility and effectiveness of classification models but also offers vital understanding into process monitoring and identification of abnormalities within intricate datasets.

Abstract Image

铸造厂耐火涂层的强化分类:基于 VPCA 的机器学习方法
本文介绍了一种根据厚度对用于化学结合砂的耐火涂层进行分类的全面方法,这对监测铸造厂的模具和型芯涂层至关重要。该方法将矢量化主成分分析(VPCA)的特征提取与机器学习算法的分类建模相结合。研究考察了五种不同的情况,包括利用原始轴向、径向和温度数据,以及使用标量属性。此外,研究还包括从前两种方法中提取特征,并在完整的数据集上进行训练。研究对性能进行了评估,结果表明该方法具有很强的能力,能对各级涂层厚度进行准确分类。此外,Hotelling 的 T 平方统计用于识别过程中的变化,为数据类别的结构和独特性提供了有价值的信息。这项研究证明了特征提取方法和机器学习算法在准确划分涂层厚度方面的功效,为铸造行业的应用提供了实用的解决方案。这种系统化的方法不仅提高了分类模型的可理解性和有效性,而且对复杂数据集中的过程监控和异常识别提供了重要的理解。
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