Exploring the Potential of Transfer Learning for Chatter Detection

Abstract

Chatter detection and avoidance are indispensable for many industries that rely on the machining process. The physics-based analytical models and recently successful machine learning methods can provide solutions using data from a unique setting. When the primary conditions of machining alter, new data needs to be collected, and analysis/training should be revised. Unfortunately, data collection is time-consuming and expensive for all machine learning applications. Therefore, broader applications of these methods are usually hindered at high production rate machining shops. Transfer learning aims to attenuate this critical barrier of machine learning implementations by transferring knowledge generated from a source domain to a different but related domain. As the concept has immense potential as an accelerator for machine learning applications, it has many prospects in Industry 4.0 framework. This article provides an introduction to transfer learning and briefly overviews its categorizations. Afterward, its potential for chatter detection is explored, and potential strategies are exemplified. Recent studies in the literature within the strategies are briefly presented as well. (C) 2022 The Authors. Published by Elsevier B.V.