Hybrid Fuzzy-Based Optimization for Minimizing Delamination in GFRP Machining

Glass Fiber Reinforced Polymer (GFRP) composites are widely used in aerospace, automotive, and structural applications due to their high strength-to-weight ratio and corrosion resistance. However, machining these heterogeneous and anisotropic materials often leads to delamination, adversely affecting structural integrity and service performance. This paper presents a hybrid fuzzy-based optimization approach designed to minimize delamination during GFRP machining by integrating fuzzy logic inference with a multi-objective optimization framework. The proposed method captures the nonlinear relationships between machining parameters—such as cutting speed, feed rate, drill diameter, and tool geometry—and delamination factors. Experimental data were used to develop fuzzy rule sets, while the optimization module systematically identified optimal parameter combinations that balance machining quality and productivity. Results demonstrate that the hybrid fuzzy system effectively reduces delamination compared to conventional optimization techniques, providing a robust and intelligent decision-support tool for machining GFRP composites. The study highlights the potential of combining fuzzy systems with optimization algorithms to address challenges inherent in the machining of advanced composite materials.

GFRP Machining, Delamination Minimization, Fuzzy Logic, Hybrid Optimization, Composite Materials, Machining Parameters, Intelligent Modeling, Drilling of Composites

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