Optimizing the Parameters of Carbon Fiber Reinforced Plastic Composite Drilling Process Using Signal-to-noise Ratio-based Grey Wolf Optimization Algorithm

This study aims to develop an optimization scheme that contributes to the production of carbon fiber-reinforced plastics using the grey wolf optimization approach. Different from other optimization schemes such as the Taguchi method, which takes some time to compute and use, this grey wolf optimization approach introduced a fast convergence scheme to reduce computation time thereby making its implementation in the factory very interesting. Data used for the analysis was obtained from a doctoral thesis via an experimental approach. Four responses were considered in this work, namely the torque, delamination at entry and exit, eccentricity and thrust force. A spreadsheet was used to implement the computational procedure of the grey wolf optimization algorithm. In using the wolves, at the initial level, the starting point was a zero where hunting had not begun and the prey had just entered the park, which is within the territory of the grey wolves. With this in mind, real life is mimicked and such data gathered would aid precise decision-making. The results revealed the feasibility of the approach and convergence was obtained at the tenth iteration with the best fitness value at 9020785071. It is expected that the findings from this work will be useful as a method for planning in production planning and policy development for the carbon fiber-reinforced plastic industry. This study is a noteworthy contribution to the production development of CFRPs where the grey wolf algorithm is used to analyze the problem. In addition, evidence of the responses determining the quality of drilled products is provided.