Machine Learning Modeling and Hyper-Parameter Optimization for Weld Nugget Formation and Failure Behavior of Resistance Spot Welds

Abstract

The welding process parameters of resistance spot welding are determined by quality indicators, such as nugget diameter, and tensile shear test behavior, such as failure load and location. In this study, deep-learning models were investigated to predict the quality indicators from base materials and process parameter information. For each model, hyperparameters, such as the number of hidden layers, number of nodes in the hidden layer, learning rate of the optimizer, and number of epochs, were optimized based on the model performance. The regression models for nugget diameter and failure load showed coefficients of determination of 0.90 and 0.95, respectively. Two models were developed to classify failure location: a 1-step model that estimates the failure location from the base material information and process parameters, and a 2-step model that estimates the failure location from the base material information and the nugget diameter as predicted by the developed regression model. The classification models for failure location showed similar accuracies of approximately 90%.