Enhancing CFRP machining performance: A hybrid fuzzy AHP-Grey relational analysis for parameter optimization

Abstract

Carbon fiber-reinforced polymers (CFRPs) have become critical materials in aerospace and automotive industries due to their outstanding strength-to-weight ratio. However, their abrasive characteristics have made machining particularly challenging. In this study, a novel hybrid Fuzzy AHP-GRA method has been introduced to optimize CFRP turning parameters—namely, cutting speed, depth of cut, and feed rate. Experiments have been conducted using an L9 orthogonal array with PVD-TiAlN-coated carbide inserts and a water-miscible coolant. The results have indicated that cutting speed (100 m/min) and depth of cut (0.1 mm) have been the most significant factors affecting tool wear and surface finish. While higher cutting speeds have enhanced material removal rates, they have also increased tool wear due to elevated temperatures. Similarly, greater depths of cut have intensified cutting forces and led to more frequent tool chipping. The optimal parameter setting determined by grey relational analysis (100 m/min, 0.1 mm depth of cut, 0.1 mm/rev feed rate) has reduced average flank wear by 2.77% and slightly improved average crater wear by 1.19%, while also yielding superior taper wall finish and cylindrical surface quality (improvements of 16.09% and 9.26%, respectively) compared to the controlled parameter setting (100 m/min, 0.1 mm, 0.3 mm/rev). This data-driven approach has effectively balanced productivity and tool longevity, thereby addressing key industrial challenges in CFRP machining