Digital twin-based anomaly detection for real-time tool condition monitoring in machining

Abstract

Real-time tool condition monitoring (TCM) has been emerging as a key technology for smart manufacturing. TCM can improve the dimensional accuracy of products, minimize machine tool downtime, and eliminate scraps and re-work costs. Digital twins offer new opportunities for real-time monitoring of machining processes, which can, in principle, take into account changes in machining processes and operating environments, help understand mechanisms of cutting tool wear, and improve the anomaly detection accuracy and fault diagnosis results. The present study exploits these potential advantages of digital twins and proposes a new digital twin-based anomaly detection framework for real-time TCM in machining. The framework of the digital twin consists of three parts: the physical product, the virtual product and data flow connections. Within this framework of the digital twin, the “physical product” represents the machining processes. The “virtual product” includes a real-time data-driven model representing the dynamic relationship between vibration data measured from machining processes as well as the model frequency features (MFFs)-based diagnostics for cutting tool anomaly detection. The “data flow connections” involve real-time measured vibration data and machine tool numerical controller (NC) signals providing real-time information on machine tool dynamics and various machining processes. The novelty is associated with an innovative integration of real-time data-driven modeling, MFFs extraction, and MFFs and machine tool NC signal-based tool wear diagnostics. This, for the first time, enables the concept of digital twins to be potentially applied to the TCM for complicated dynamic machining processes which, as far as we are aware of, has never been achieved before. Comprehensive field studies have demonstrated the effectiveness of the proposed digital twin-based TCM framework and its potential industrial applications.