Optimization of Laser-Induced Hybrid Hardening Process Based on Response Surface Methodology and WOA-BP Neural Network

The laser-induced hybrid hardening process integrates laser quenching and electromagnetic induction heating to overcome traditional heat treatment limitations, enhancing the depth and properties of hardened layers for applications like wind turbine bearings. This study uses Box–Behnken design (BBD) experiments to analyze key process parameters and develops response surface methodology (RSM) and whale-optimization-algorithm-optimized back-propagation neural network (WOA-BPNN) models for prediction and optimization. The WOA-BPNN model outperforms the RSM model, achieving superior predictive accuracy with R2 values exceeding 0.995 for both depth and hardness, with a root mean square error (RMSE) for depth of 0.099 mm and of 1.734 HV0.3 for hardness, and with mean absolute percentage error (MAPE) of 0.697% and 0.7867%, respectively. The WOA-BPNN model provides an effective and reliable framework for optimizing laser-induced hybrid hardening, improving production efficiency and extending component life for industrial applications.