The prime objective of this work is to find the best combination of parameters in laser welding of stainless-steel 304 H plates. CO2 laser welding was used to join 5 mm thick stainless-steel 304 H plates. Laser power, welding speed, and focal position were used as input parameters, and experiments were conducted on the basis of the Taguchi L27 matrix. The quality of the weld was analysed by measuring depth of penetration, bead width and hardness. The best parameter combination was found using artificial neural network and genetic algorithm. The Levenberg–Marquardt algorithm predicted output responses more accurately. A confirmatory test was carried out for the optimized parameters identified by the genetic algorithm. The percentage error between the experimental and predicted genetic algorithm values was below 6%. In comparison to the base metal, the optimized weld hardness was increased by 8%. The electron backscatter diffraction analysis of the optimized weld revealed the presence of a higher percentage of high-angle grain boundary than low-angle grain boundary.

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