Indirect In-process Tool Wear Monitoring Through Digital Twin Integration

Tool wear monitoring remains critical in modern manufacturing, particularly for small-batch production where conventional monitoring approaches fail. Current methods rely on operator inspection, camera systems or fixed spindle power thresholds applicable only to identical workpieces. This paper presents an in-process tool wear estimation concept based on tangential edge cutting force coefficient ( K te ) identification, which quantifies edge friction and serves as an indirect wear indicator. The approach utilizes CNC controller data in combination with online process digital twin simulation, eliminating the need for external sensors. The controller data are supplemented by material removal rate (MRR) and cutter-workpiece engagement (CWE) signals calculated by the digital twin and subsequently used to update the K te estimation. The method requires a change in the ratio of MRR to CWE to update the K te estimate. This requirement is satisfied in many standard situations, such as in toolpath corners. The method therefore does not require machining interruption, unlike many standard methods. Experimental results using a 10 mm monolithic end mill demonstrate significant correlation between the estimated K te and tool wear progression, with K te increasing by tens of percent throughout tool life. The presented approach demonstrates the feasibility of non-intrusive tool wear monitoring in small-batch production environments.