The Research on Optimization of Laser-Arc Hybrid Welding Process Parameters Based on Neural Networks for Maximum Tensile Strength of Weld Joint

In laser - arc hybrid welding, the selection of welding parameters is crucial for achieving excellent mechanical properties of weld joints. In this paper, based on the experimental data of laser - arc hybrid plate butt welding, a BP neural network was employed to establish a prediction model between the hybrid - welding process parameters, namely welding current I/A, laser power P/W, welding blunt height D/mm, welding angle α/º, welding gap d/mm, and welded joint tensile strength. Subsequently, the multi - population genetic algorithm (MPGA) was utilized to optimize the internal topology of the BP neural network, aiming to enhance the prediction accuracy. The results indicate that the prediction error of the optimized neural - network model for tensile strength is less than 6%. According to the established BP neural - network model, taking the maximum tensile strength of the welded joint as the objective function, the genetic algorithm (GA) was used to optimize the welding process parameters with the range $\mathrm{D}\in \left[\mathrm{2,4}\right]/\mathrm{m}\mathrm{m},\mathrm{d}\in \left[\mathrm{0.1,0.8}\right]/\mathrm{m}\mathrm{m},\mathrm{ɑ}\in \left[\mathrm{30,60}\right]/°$ $\mathrm{P}\in \left[\mathrm{2300,2800}\right]/\mathrm{W},\mathrm{I}\in \left[\mathrm{200,280}\right]/$A. Finally, the optimal tensile strength of the weld joint was obtained as 1.441 MPa. The combination of hybrid - welding process parameters is as follows: blunt edge of 3.1 mm, welding angle of 51º, welding gap of 0.37 mm, welding current of 200 A, and laser power of 2700 W. Based on the hybrid - welding process parameters optimized by the genetic algorithm, a plate - butt - welding experiment was conducted on the welding test platform. The hybrid - welding conditions were kept unchanged, and the welded sample was processed and tested for tensile strength. The test results indicated that the tensile strength of the welded sample corresponding to the optimized process parameters was 1.35 MPa. This value is higher than the maximum tensile strength of 1.309 MPa in the welded samples of the orthogonal test.

Key-words:
BP neural network; Multi-population genetic algorithm; Maximum tensile strength of sample joint; Optimization of welding process parameters