Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning

In a small sample welding test space, and to achieve online prediction self-optimisation of process parameters for the resistance joint quality power lithium battery packs, this paper proposes model. The model combines chaos game optimisation algorithm (CGO) with multi-output least-squares support vector regression machine (MLSSVR), multi-objective parameter method based on particle swarm algorithm. First, MLSSVR was constructed, hyperparameter strategy CGO designed. Next, predicted using CGO–MLSSVR Finally, (PSO) used obtain optimal parameters. experimental results show that can effectively predict positive negative electrode nugget diameters, tensile shear loads, root mean square errors 0.024, 0.039, 5.379, respectively, which is better than similar methods. average relative error in weld within 4%, proposed has good application value spot packs.