MOEDO-Based Multi-Objective Optimization of Laser Remelting for Surface Roughness, Microhardness, and Energy Consumption

In the context of low-carbon and energy-efficient manufacturing, this study investigated laser remelting of 35CrMo/Fe45 cladding layers to improve surface quality and microhardness while reducing energy consumption. Laser power, scanning speed, and overlap ratio were selected as the main process parameters, while surface roughness, microhardness, and electrical energy consumption during laser-on processing were taken as the optimization objectives. Based on the experimental data obtained using an enhanced Latin hypercube sampling design, a surrogate model was constructed. The multi-objective exponential distribution optimizer (MOEDO) was then used to generate a Pareto-optimal solution set, from which CRITIC-TOPSIS selected the optimal process parameters. Relative to the as-clad condition, the surface roughness of the cladding layer decreased from 8.56 μm to 0.789 μm, while the microhardness increased from 536 HV0.5 to 625.3 HV0.5. Compared with the reference condition, the optimized process reduced surface roughness by 39.6%, increased microhardness by 31.5%, and reduced electrical energy consumption during laser-on processing by 55.7%. The proposed approach provides a practical basis for balancing surface-quality improvement, hardness enhancement, and energy saving in energy-efficient laser remelting of cladding layers.