Study on the Process and Multi-Objective Optimization of Laser Cleaning Anodic Oxide Film on 2024 Aluminum Alloy

<title>Abstract</title> <p>The process parameters of laser cleaning the anodic oxide film on 2024 aluminum alloy surface are numerous and have a wide adjustable range. The selection of its optimal parameters to achieve a dual improvement in cleaning quality and efficiency has become a critical technical bottleneck restricting engineering applications. To address this problem, this study designed a Box-Behnken design (BBD) response surface experiment with laser power, travel speed, and scanning speed as input variables, and post-cleaning surface roughness, oxygen content, and self-corrosion potential as response indicators. Response surface models and a BP neural network model optimized based on the MOEA/D-DE hybrid algorithm were established, and the prediction accuracy and generalization ability of the two models were compared and analyzed. In addition, this study proposed an improved multi-objective firefly algorithm (IMO-FA) integrating Latin hypercube initialization, adaptive variable step-size detection, and a strict crowding distance selection mechanism to solve the problems of traditional algorithms being prone to local clustering, premature convergence, uneven Pareto front distribution, and boundary shrinkage. Subsequently, the entropy weight-TOPSIS method was used to determine the optimal process parameter combination from the optimal Pareto solution set: average power 184.28 W, scanning speed 2447.10 mm/s, and travel speed 2.86 mm/s. The results show that compared with the optimal specimen in the BBD experiment, the surface roughness of the optimized specimen was reduced by 5.63%, the oxygen content was reduced by 21.43%, and the self-corrosion potential was increased by 2.12%. The optimized process parameters are beneficial to the improvement of cleaning surface quality.</p>