AI Based cutting tool condition monitoring system for turning operation

Abstract

The study of cutting tool wear, the determination of cutting forces, variations in surface roughness, and other outcomes in machining processes have garnered significant attention from contemporary researchers. These variations in machining responses can greatly impact dimensional accuracy and productivity. Moreover, excessive wear can result in catastrophic consequences, including tool breakage. Hence, this article delves into the current trends in monitoring tool conditions during various machining operations. To achieve this, the article explores the effective utilization of novel sensors and artificial intelligence (AI) methods in a comprehensive review. Sensor systems employed for tracking tool wear encompass dynamometers, accelerometers, acoustic emission sensors, current and power sensors, image sensors, and others. These systems contribute to solving the challenges related to automating and modeling technological parameters in primary cutting processes like turning, milling, drilling, and grinding. The contemporary AI methods under consideration include neural networks, image recognition, fuzzy logic, adaptive neuro-fuzzy inference systems, Bayesian networks, support vector machines, ensembles, decision and regression trees, k-nearest neighbours, artificial neural networks, Markov models, singular spectrum analysis, and genetic algorithms. The discussion also encompasses the key advantages, drawbacks, and potential future applications of various AI techniques in tool wear monitoring.