Predicting surface roughness in machining aluminum alloys taking into account material properties

IF 3.7 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

International Journal of Computer Integrated Manufacturing Pub Date : 2024-07-05 DOI:10.1080/0951192x.2024.2372252

Van-Hai Nguyen, Tien-Thinh Le

引用次数: 0

Abstract

This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse ...

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根据材料特性预测加工铝合金时的表面粗糙度

本研究探讨了在不预先知道最佳切削参数的情况下，使用机器学习模型预测铣削多级铝合金的表面粗糙度（Ra）。一个多样化的...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Computer Integrated Manufacturing 工程技术-工程：制造

CiteScore

9.00

自引率

9.80%

发文量

审稿时长

10 months

期刊介绍： International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing. Terms such as industry 4.0, intelligent manufacturing, digital manufacturing and cyber-physical manufacturing systems are now used to identify the area of knowledge that IJCIM has supported and shaped in its history of more than 30 years. IJCIM continues to grow and has become a key forum for academics and industrial researchers to exchange information and ideas. In response to this interest, IJCIM is now published monthly, enabling the editors to target topical special issues; topics as diverse as digital twins, transdisciplinary engineering, cloud manufacturing, deep learning for manufacturing, service-oriented architectures, dematerialized manufacturing systems, wireless manufacturing and digital enterprise technologies to name a few.