Exploring the edge of the edge: Utilization of available CNC machine data for material defect detection

Moving towards sustainable production, zero-defect manufacturing plays an important role. Achieving this, the identification of material defects during machining is a decisive factor. This paper introduces innovation through a theoretical model for the smallest detectable material defect in machining, solely based on machine data from the existing numerical controller, eliminating the need for external sensors. The verified model correlates the material defect size with spindle torque changes (affected by tool-, material-, machine-, and machining parameters) and demonstrates the identification of 0.2mm defects compared to 0.7mm in the literature as a remarkable contribution to zero-defect manufacturing.