Cutting Power Model for Material Identification during Helical Milling of Aerospace Stacks

ABSTRACT: Smart factories increasingly rely on real-time data to optimize manufacturing, yet machining operations, particularly in aerospace stack drilling, still face challenges such as low productivity and accelerated tool wear. While advanced CNC machines already capture rich process data, its full potential for real-time decision-making remains underexplored. This work introduces a novel approach that leverages machine learning (ML) to identify material layers and optimize cutting conditions during drilling (helical milling) of aluminum–titanium stacks. Unlike prior methods that require additional sensors or complex instrumentation, our approach uniquely utilizes only spindle power signals from the CNC machine. Data maps consisting of cutting coefficients are used to train ML models to reliably predict material transitions across multiple layers under a range of cutting conditions. The results demonstrate appropriate material identification in comparison to experiments, enabling significant improvements in the hole-making of aerospace stacks. This study contributes a scalable, sensor-free, and non-intrusive framework for smart machining, establishing a practical pathway for process optimization in aerospace manufacturing without disrupting existing shop-floor setups.