CMT spot welding deformation of sheet metal based on BP neural network and genetic algorithm

Welding was a key link in automobile body manufacturing. The quality of welding seriously affected the quality of automobile body, so the selection of welding parameters was very important. Aiming at the quality control of thin plate welding, the advantage of BP neural network was used to solve the non-linear problem, and established the mapping model between welding deformation and process parameters. Combining with genetic algorithm, the optimization system of welding process parameters was constructed based on genetic neural network. Then the orthogonal test was designed and compared with the proposed model. The results showed that the method could effectively achieve welding deformation prediction and optimization of process parameter on CMT (cold metal transfer) spot. The reasonable parameters were given by the prediction model to guide the CMT spot welding deformation test of steel sheet and aluminium alloy sheet, and to improve the welding efficiency.