A multi-objective cross-scale optimization approach for high-performance Inconel 625 laser cladding on ductile iron

Ductile iron, critical for industrial components, suffers from wear and interfacial degradation. Laser cladding with Inconel 625 offers repair potential via metallurgical bonding, yet existing parameter optimizations inadequately balance mechanical, microstructural, and interfacial synergies. This study presents a multi-objective optimization of In625 coatings on QT-600 ductile iron using Taguchi-grey relational analysis (GRA). Specifically, the mapping relationship between process parameters (laser power (P), powder feed rate (R), and scanning speed (V)) and optimization objectives (microhardness, porosity, elastic modulus, dilution rate, and bonding strength) is evaluated through L16 orthogonal experiments. Variance and signal-to-noise ratio analyses revealed P as statistically significant for microhardness, porosity, and elastic modulus, while V predominantly influenced dilution rate and bonding strength. GRA consolidated multi-response objectives into a single grey relational grade (GRG), yielding optimal parameters (P = 2400 W, R = 0.9 r/min, V = 8 mm/s). Validation experiments showed a 52.5 % increase in microhardness, 96.1 % lower porosity, 11.2-fold higher dilution rate, and 59.4 % improved bonding strength, with a slight elastic modulus reduction. Experimental GRG (0.683) closely matched predictions (0.680), confirming model reliability. Optimized parameters enhanced hardness, elasticity, and bonding strength by reducing porosity, refining grain structure, promoting Fe–Ni solid solutions and continuous ledeburite, and generating the dual-shell ledeburite-martensite structures in the the partially melted zone.