A hybrid optimization method for determining laser cladding process parameters to control geometric morphology in CoCrFeNiMn high-entropy alloy

For precise control of laser-cladded CoCrFeNiMn high-entropy alloy coatings, a multi-algorithm hybrid optimization-based method for determining process parameters was developed. Using Latin hypercube experimental design, single-track cladding experiments on 316L stainless steel substrates systematically revealed three-dimensional response relationships among laser power, scanning speed, and powder feeding rate relative to coating dilution rate, aspect ratio, and wetting angle. A hybrid prediction model has been developed that integrates the Natural Residual Balance Optimizer (NRBO) with Deep Belief Networks (DBN), significantly enhancing the accuracy of complex nonlinear mappings between process parameters and geometric characteristics. Subsequently, an Adaptive Reference Vector Multi-objective Evolutionary Algorithm (ARMOEA) was applied to generate Pareto fronts aimed at optimizing the dilution rate and aspect ratio, while minimizing the wetting angle. An integrated evaluation method based on objective weighting was then utilized for global optimization of multidimensional solution sets. The geometric characteristics of the optimized coating were validated: The measured values using the optimal process parameters (P = 1265.95 W, V = 8.28 mm/s, F = 0.41 r/min) exhibited a deviation from the values predicted by the optimization model that was controlled within 6.01 %. This demonstrates the effectiveness of the proposed method. Compared to empirical parameter sets, this approach achieved a 44.2 % reduction in dilution rate, a 2.28 % improvement in aspect ratio, and a decrease in wetting angle to 49.5°. This methodology enabled quantitative optimization of morphological features in high-entropy alloy claddings through intelligent algorithmic collaboration, providing a data-driven decision paradigm for the development of laser additive manufacturing processes.