A Regression Model for Tool Wear and Breakage Diagnosis

Abstract

Many studies have addressed tool wear diagnosis, breakage detection, and tool vibration removal in machining large workpieces, such as moulds and aircraft parts. However, there have been difficulties in spreading commercialised tools to the field. This is because the optimisation of NC-data is based on machining experience and expertise. In particular, the expertise required to maintain the state of optimisation hinders the spread of commercial tools on actual machining floors. For this reason, NC data-based research has been conducted in CNC machining. In this paper, we propose a machining status diagnosis method using NC data. The machining load generated during machining is stored in synchronisation with the equipment–tool–material, then the correlation with the machining load can be expressed as a regression model, and a tool wear/damage detection method using this is presented. Thus, it is possible to provide auxiliary information for data-based management of individual tools of a CNC part mass production plant. In particular, the proposed method can be used as a standard for tool wear and adaptive control, even in the one-time machining of moulds, aircraft, and mechanical parts, and it can also be said to be a method to predict tool life. Therefore, this method can be considered an NC data-based on-site diagnosis method that can increase machining efficiency through repetitive learning.