Abstract
This research aims to improve the accuracy of cutting fiber-reinforced polymers (FRPs) utilizing CO2 laser processing techniques, with a particular focus on carbon-glass fiber-reinforced hybrid composites (CGFRP) using epoxy resin. Establishing CO(2) laser machining as a dependable and effective process for creating superior CGFRP components is the main goal. This research intends to optimize laser machining parameters to enhance surface quality and machining efficiency for these composites by a thorough parametric analysis. In order to model and improve the correlations between important machining parameters, the research combines regression models, multi-objective gray relational analysis (MOGRA), and Artificial Neural Networks (ANNs). When linked together, these methods enable efficient multi-objective optimization, which enhances laser cutting operations for CGFRP materials in terms of accuracy and economy. Using the Taguchi L27 orthogonal array, one can methodically investigate how different parameters affect CGFRP cutting. GRA is used in optimization to find the best parameter combinations and highlight important parameters. After determining that LPW3CSD1FOL1GPR1 and LPW3CSD2FOL1GPR2 were the ultimate ideal settings, the initial machining parameters were set at LPW3CSD1FOL1GPR1. Predictions and trials confirm that these adjusted parameters result in a 6.125% improvement in grade. Also, ANN structured approach enhances predictive accuracy and provides valuable insights for optimizing machining processes. Accordingly, a strong framework for enhancing hybrid composite laser machining is provided by this research. The research aims to develop a robust framework for optimizing CO2 laser cutting of CGFRP composites, ultimately leading to more efficient, cost-effective manufacturing solutions for high-performance applications in aerospace, automotive, and marine sectors.