Machine learning approaches towards digital twin development for machining systems

被引：0

作者：

Jarosz K. ^{[1
]}

Özel T. ^{[1
]}

机构：

[1] Department of Industrial and Systems Engineering, Manufacturing and Automation Research Laboratory, Rutgers University, Piscataway, 08854, NJ

来源：

International Journal of Mechatronics and Manufacturing Systems | 2022年 / 15卷 / 2-3期

关键词：

advanced manufacturing system; artificial intelligence; CNC; computer numerical control; digital twin; Industry; 4.0; machine learning; production; virtual machining;

D O I：

10.1504/ijmms.2022.124922

中图分类号：

学科分类号：

摘要：

Machine learning (ML) and artificial intelligence (AI) have experienced an increased degree of applications associated with Industry 4.0. Their effective utilisation is elevated with readily available computational power and computerisation of production processes toward digital twin development. This paper begins with a review of the use of ML and AI Methods in machining applications, using examples from open literature, discussing the future perspectives for further utilisation of ML and AI techniques within the scope of machining, both in terms of research and industrial applications. Examples of computer-aided production (CAP) systems are presented and compared with a discussion on how ML and AI can be applied to improve applicability and performance of already established software solutions. Additionally, a software solution for numerically controlled (NC) toolpath optimisation is shortly presented. Finally, incorporation of machine learning method in a CAE software solution developed by the authors is discussed along with a case study. Copyright © Inderscience Enterprises Ltd.

引用

页码：127 / 148

页数：21

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