Crack prediction and optimization of laser-clad IN718 coatings via GA-PSO-BP

Laser additive manufacturing of IN718 superalloy claddings is frequently hampered by severe hot cracking and inadequate geometrical uniformity, making precise control of operational variables indispensable. In response, this investigation systematically tailored three pivotal parameters—namely, laser power (P), overlap ratio (D), and powder feed rate (V f )—with the aim of diminishing crack prevalence and elevating coating quality. Initial orthogonal experiments were conducted to delineate the parametric influence trends. Subsequently, a back-propagation neural network, synergistically optimized via a genetic algorithm and particle swarm optimization (GA-PSO-BPNN),was proposed for quantifying the relationship correlating the processing variables with the resulting crack density,yielding high-precision prediction (R 2 > 0.97) and thereby facilitating a global optimization strategy. Empirical findings reveal that the ranking of factor significance on crack density is P > D > V f . The optimal configuration, identified as 650 W, 6.3 r/min, and 24% overlap, successfully produced the lowest crack density of 0.024827 ± 0.00092 mm/mm 2 . Confirmatory deposition experiments substantiate that the resultant coating displays favorable macro-morphology, devoid of observable defects or metallurgical irregularities. Collectively, these outcomes corroborate that hybrid machine-learning paradigms outperform traditional design-of-experiment methodologies, offering a time-efficient, highly precise, and robust tool for refining the processing conditions to achieve the desired cladding morphology.