Machine learning approaches towards digital twin development for machining systems

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.