High-speed micro-milling of nano-graphene-enhanced CFRP: Machine learning and sensitivity analysis

This study develops an integrated predictive and interpretative framework for the high-speed micro-milling of graphene-reinforced carbon fiber polymer (G-CFRP) composites, with emphasis on machining performance and process-parameter interactions. A response surface methodology (RSM)–based experimental design was employed to examine the effects of spindle speed, feed rate, depth of cut, and tool diameter on cutting force, surface roughness, and machining temperature. Predictive modeling was conducted using a boosted regression approach to capture the nonlinear relationships between machining variables and material response. The ABR models achieved test-set R 2 values of 0.953, 0.895, and 0.853 for cutting force, temperature, and surface roughness, respectively. Temperature exhibited the lowest prediction error with an MAPE of 3.13%, whereas higher percentage errors were obtained for cutting force and surface roughness. To enhance the physical interpretability of the data-driven models, a global sensitivity analysis based on the extended Fourier amplitude sensitivity test (EFAST) was performed. The results demonstrated that feed rate and spindle speed predominantly govern surface quality. In contrast, depth of cut plays a critical role in controlling cutting force and thermal rise during micro-milling of G-CFRP composites. Furthermore, a multi-objective optimization using the Derringer–Suich desirability function identified optimal machining conditions (spindle speed of 18,000 r/min, feed rate of 50 mm·min −1 , depth of cut of 0.10 mm, and tool diameter of 0.60 mm), achieving an overall desirability index of 0.939. The proposed modeling–sensitivity–optimization framework provides a robust and interpretable tool for optimizing micro-machining parameters of graphene-enhanced polymer composites. It offers a transferable methodology for performance prediction and process design in advanced polymer-based composite machining.