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This study proposes a genetic algorithm-optimized model for the control of the fatigue life of AISI 1040 steel components after a high-power diode laser hardening process. First, the effect of the process parameters, i.e., laser power and scan speed, on the fatigue life of the components after the laser treatment was evaluated by using a rotating bending machine. Then, in light of the experimental findings, the optimization model was developed and tested in order to find the best regression model able to fit the experimental data in terms of the number of cycles until failure. The laser treatment was found to significantly increase the fatigue life of the irradiated samples, thus revealing its suitability for industrial applications. Finally, the application of the proposed genetic algorithm-based method led to the definition of an optimal regression model which was able to replicate the experimental trend very accurately, with a mean error of about 6%, which is comparable to the standard deviation associated with the process variability.

An Optimal Genetic Algorithm for Fatigue Life Control of Medium Carbon Steel in Laser Hardening Process