Magnetic-field-assisted laser cladding of SDSS 2507 on SS 420: An experimental investigation and multi-objective optimization

Achieving high hardness in laser-clad layers remains crucial for surface engineering. The present study compares four multi-objective algorithms—Multi-objective Bonobo Optimizer (MOBO), Multi-objective Dragonfly Algorithm (MODA), Multi-objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize processing parameters during single-track laser cladding of Super Duplex Stainless Steel (SDSS) 2507 on SS 420, with/without a steady magnetic field (MF). Magnetostrictive effects were examined to enhance microhardness and reduce thermal stress. The researchers did not investigate such a study earlier. Geometrical features, microstructure, XRD and EDS analysis, microhardness, and thermal stress of the clad layer were evaluated. The comparative analysis indicated that MOBO was the most effective algorithm for this specific optimization challenge, offering the most consistent and accurate approximation of the true Pareto front. Multi-criteria decision-making (MCDM) methods were employed to select the most appropriate solution from the Pareto set generated by MOBO. The optimum processing parameters were 973 W of laser power, 400 mm/min of scanning speed, and 3 mT of magnetic field strength. Experimental validation results were in perfect conformity with the model. The coatings are in acceptable condition, with an average microhardness of 432.25 HV_0.05 and a dilution of 0.49. The maximum average error among experimental validation and model predictions for dilution and microhardness was 4.08 % and 0.59 %, respectively. MF-assisted cladding refined microstructure, enhancing microhardness, minimizing dilution/thermal stress, and preventing plastic deformation. This approach improves dimensional accuracy in SS420 injection moulding dies by reducing surface wear during repetitive contact with mould materials.