Advanced Multi-Objective Optimization for Laser Cladding of H13 Die Steel with CFOA

Abstract

The quality of laser cladding is strongly influenced by process parameters, which interact in complex and often nonlinear ways. The existing literature primarily focuses on the influence of process parameters on surface properties. However, limited research has explored the relationship between process parameters, surface properties, and their optimization. To bridge this gap, this study introduces a novel parameter modeling and optimization approach using the Catch Fish Optimization Algorithm (CFOA). Key process parameters, including laser power, scanning speed, and powder feeding rate, were systematically analyzed for their effects on the surface quality of H13 die steel. An orthogonal experimental design was employed to develop a regression model capable of accurately predicting cladding quality metrics, such as dilution rate, microhardness, and aspect ratio. To address the multi-objective nature of the optimization problem, the analytic hierarchy process (AHP) was used to transform it into a single-objective framework. The proposed approach identified an optimal parameter combination: laser power of 1628.19 W, scanning speed of 9.9 mm/s, and powder feeding rate of 14.73 g/min. Experimental validation demonstrated significant improvements in cladding performance, with enhancements of 19.71% in dilution rate, 3.37% in microhardness, and 28.66% in aspect ratio. The CFOA also shows global search capabilities and precision compared to conventional methods, making it a robust tool for complex optimization tasks. This study presents an innovative methodology for optimizing laser cladding processes, providing effective H13 die steel repair solutions. It also emphasizes the versatility of metaheuristic algorithms for advancing manufacturing process optimization.