Application of data mining techniques for assessment of fracture load and energy in double cantilever beam solder joints
DOI:
https://doi.org/10.62676/9v4azj82Keywords:
DCB solder joint, Fracture energy, Random forest model, Lasso regression, NSGA-II algorithmAbstract
Predicting the exact values of fracture load and energy has a significant effect in preventing solder joint failure. In this research, various double cantilever beam (DCB) solder joints with different geometric constraints, including adherend thickness, adherend width, loading arm length, and solder thickness were tested under mode I crack propagation and at a strain rate of 0.03 . According to ANOVA test results, all the mentioned geometric factors influence the fracture load, but adherend width and solder thickness don't change the failure energy remarkably, considering the type I error is equal to 0.05. The failure load and energy forecasting using the random forest algorithm showed that the prediction accuracy is 92% and 81% respectively. Linear regression and lasso regression were also utilized to identify the solder joint's fracture behavior. The coefficient of determination in both methods is acceptable for solder joint fracture force prediction, but for fracture energy, it has decreased to about 70%. Finally, multi-objective optimization was done with the help of the NSGA-II method, and the Pareto front diagram drawn according to the problem's constraints to find the optimal values for fracture force and energy.
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