# Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
###### Abstract
Although the response surface model can guarantee an optimal solution using a quadratic polynomial model and exact methods (such as the simplex method), it may easily become trapped in a local optimum and fail to solve non-deterministic polynomial-time hardness problems within a reasonable time, when it is faced with highly nonlinear processes and complex problems [17; 18]. With the rapid development of modern testing technology, intelligent control technology, and artificial intelligence technology, research on welding quality prediction and process parameter optimisation based on process data developed in the direction of information diversification and algorithm intelligence. Based on the Taguchi method of establishing small sample data sets, [PERSON] et al. [19] modelled the relationship between welding process parameters (welding current, welding pressure, sample thickness, number of weld joints, and spacing of weld joints) and tensile shear strength using a regression equation and a particle swarm optimisation method. The maximum deviation between the experimental and optimised results was 0.571741%. [PERSON] et al. [20] combined Kriging with the NSGA-II algorithm to predict and optimise the weld quality and process parameters for laser-EM hybrids; the experimental results showed that this method could reduce the generation of weld defects using a small sample data set. In summary, the method proposed in the above literature is reliable, and the swarm intelligence optimisation algorithm was used to optimise process parameters for a variety of welding types, such as resistance spot welding (RSW), laser-magnetic hybrid welding (LMW), and gas tungsten arc welding (GTAW) [21], to address practical engineering issues such as non-destructive testing of weld quality, enhancement of mechanical properties, and reduction of welding defects, but there are certain limitations. When predicting welding quality, neural network prediction of multiple welding quality indicators is prone to overfitting phenomena, and the regression model requires that multiple welding quality indicators are not a good predictor of welding quality indicators. indicators be combined into a single integrated welding quality indicator for prediction; however, it cannot accurately reflect the relationship between the specific process parameters and welding quality. Moreover, there are few reports relating to the use of regression models to predict multiple indicators simultaneously and solve the generalisation problem of small samples. On the other hand, nugget geometry in weld quality is directly related to tensile shear load. When the nugget size is increased, the tensile shear load first rises; however, after reaching the maximum strength, the increase in nugget size then results in a decrease in the tensile shear load, which may lead to welding defects. At the same time, the quality of the weld is related to the welding voltage and time [22]. It is a difficult technique to guarantee the diameter consistency of two nuggets and to obtain a sufficiently large tensile shear load in single-sided double-joint welding, but few people studied this. There are limited published data on the application of RSW in the manufacture of power lithium battery packs. In this paper, to address the above issues, a single-sided double-point welding process experiment is carried out on copper-nickel plate material, commonly used in lithium batteries, and welding quality prediction and process optimisation for multivariable outputs in a small sample welding test space are studied. First, a combination of the chaos game optimisation algorithm (CGO) [23] and the multi-output least-squares support vector regression machine (MLSSVR) model [24; 25] is proposed. The MLSSVR model was used to establish the characteristic relationship between the process parameters and the quality of welded joints, and the hyperparameters of MLSSVR were optimally sought using the CGO optimisation algorithm.