# Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
###### Abstract  
In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO-MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO-MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs. resistance spot welding quality prediction; process optimisation; chaos game optimisation; multi-output least-squares support vector regression; particle swarm algorithm +
Footnote †: journal: Applied  
The quality inspection of resistance-welded joints in industrial production mainly uses off-line inspection methods [8], such as manual random sampling destructive experiments, which have high leakage rates, low efficiency, limitations, and high costs. The resistance spot welding nucleation process is fast and closed, is highly nonlinear, has multivariate coupling characteristics, but lacks professional testing equipment that can monitor the nucleus growth process online. The welding process is accompanied by many signals: electrical, thermal, force, displacement, and others, all of which contain a wealth of information on nucleus growth and performance characteristics that can be used for process monitoring and quality prediction. In recent years, resistance welding quality monitoring techniques based on process data became an area of research interest, and considerable progress was made [9]. [PERSON] and [PERSON] [10] investigated the effects of welding parameters, such as electrode force, welding current, and welding time, on resistance spot welding nugget size and failure strength, using the kernel ridge regression (KRR) method; they found that welding current and time had the greatest impact, and that the improved algorithm reduced the mean square error compared with the least-squares method. [PERSON] et al. [11] used a deep neural network (DNN) model to predict the width of the fusion nucleus of a spot-welded joint and found that the DNN prediction ability was superior to models such as KNN, KStar, and SVM-eps. For neural networks, the expression between welding parameters and between weld quality is implicit, the choice of their initialisation parameters affects computational efficiency, and the prediction results are prone to overfitting when small sample data sets are used. Based on predicted welding quality, and to further achieve its control, the process parameters need to be self-adjusting and self-optimising according to the predicted results. Traditional methods for the optimisation of welding process parameters mainly include response surface methodology [12; 13] and analysis of variance [14]. [PERSON] et al. [15] selected the optimal process parameters based on response surface methodology to obtain the required weld quality for small-scale resistance spot welding. Analysis of variance (ANOVA) was used to study the effect of micro resistance welding parameters on the weld quality of titanium alloys, and optimum process parameters were obtained [16].