Digital twin based online material defect detection for CNC-milled workpieces

Reliable lot size one capable online quality monitoring for CNC machined parts remains elusive. To address this challenge, the proposed approach aims to bridge the current gap in research by developing a cost-effective and reference-independent monitoring concept for material defect detection in CNC-machined parts. This paper presents a novel digital twin-based method, utilising machining vibrations and a g-code-based encoding of the cutting process. The objective is to detect material defects, such as blowholes, without the need for individual workpiece references. The proposed method aims to reduce barriers to entry, minimise waste, and enhance machine productivity by enabling automated early online quality control. To develop and validate the model, a dataset combining machining vibration with technological context data such as chip-shape is generated. The feasibility and potential of the approach is demonstrated in a job shop setting on a 3-axis CNC mill.