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.

Welding Challenges and Quality Assurance in Electric Vehicle Battery Pack Manufacturing

“…The analysis of the monitoring data showed that the movement of the electrodes reflected thermomechanical phenomena during the process, such as unbalanced spot heating. Following this, a more advanced approach in [67] presents a ML-based quality prediction model for the case of single-sided double-jointed RSW of overlapped Ni-Cu sheets. By using a constant voltage controller and considering the input process parameters as predictors and the nugget diameters and tensile shear load of the joints as targets, the authors developed a multi-output regression model.…”

“…In particular, those that have been found either include process optimisation as a side research task or focus on modelling of some aspects of the process. In [67], as mentioned above, the authors trained a ML model to predict the nugget diameters and the tensile shear loads of the joints. Later, they used this model to identify the optimal process parameters and maximise the tensile shear load of the joints or select the right parameters to achieve a given value.…”

Exploring Resistance Spot Welding for Grade 2 Titanium Alloy: Experimental Investigation and Artificial Neural Network Modeling

“…Resistance spot welding (RSW), which is categorized as a pressure welding technique, is now the primary assembly process used in the automobile industry and other sectors. For several decades, the aerospace industry has recognized the economic benefits of spot welding [1][2][3][4]. Around 90% of all vehicle body assemblies use resistance spot welds [5].…”

Automated Battery Making Fault Classification Using Over-Sampled Image Data CNN Features

“…This manual inspection is too expensive and time consuming. In response to the problems associated with the manual inspection of defects [ 5 ], automatic detection techniques such as pattern recognition and machine learning are mainly used [ 6 , 7 , 8 ]. Machine-vision-based defect detection systems can also be deployed to detect faults in batteries.…”