Deep Learning Algorithms for Tool Condition Monitoring in Milling: A Review

Abstract

The 4th Industrial Revolution (Industry 4.0) necessitates implementing the prognostics and health management (PHM) practices in manufacturing processes. The traditional machine learning approach has well assisted the PHM practices within the same data distributions. However, when a high noise environment, versatile operating conditions, and cross-domain machining is considered, it still lacks key steps of generalizing unknown tool faults. In an attempt to address PHM practices under such domains, a generic Deep Learning-based scheme is gaining significant attention. In this paper, an inclusive review is presented in order to provide an insight into the application of DL in tool condition monitoring (TCM), particularly in milling. Commonly used DL algorithms and their applications toward TCM are initially discussed and number of illustrative DL models applied for TCM is presented. Later, emergent DL themes & their computational techniques are summarized with an intention to provide framework for domain generalization. Finally, challenges in further exploration and futuristic trends in TCM are discussed.