Application of Machine Learning in the Machining Processes

Abstract

Machining is a subtractive manufacturing method that removes chip from a work item. Machining can be broadly divided into two kinds depending on the cutting tool and energy sources: (i) conventional machining and (ii) non-conventional machining. Turning and milling are two common conventional machining processes whereas electrical discharge machining (EDM), ultrasonic machining (USM), laser beam machining (LBM), etc. are non-conventional machining processes. Improving productivity necessitates the selection of process parameters, cutting tools, and machines with attention. However, over the previous few decades, such parameters have been chosen via a standard approach. The efficiency of the machining process can be improved if the industry adopts intelligent machining techniques that can offer self-optimization and adaptation to unforeseen situations. Machine learning (ML) algorithms have been used to diagnose and forecast the health of machine tools, optimize process parameters, and anticipate the quality of the end products manufactured by both conventional and non-conventional machining processes, all of which contribute to increased productivity. This chapter explores several ML processes and how they are used in various machining processes.