Quality prediction of aluminum alloy resistance spot weld-ing based on correlation analysis and SSA-BP neural net-work
Based on the characteristics of the process signals in the resistance spot welding process,three working condi-tions of different spacing,different gaps and different spacing and gaps are analyzed,and correlation analysis is introduced to extract the correlation between the process signals and the dia-meter of nugget.A resistance spot welding quality prediction model based on Sparrow Search Algorithm-Back Propagation Neural Network(SSA-BP)was established,and power,weld-ing current,welding voltage and dynamic resistance are used as input features of the prediction model.The results show that the BP neural network optimized by the sparrow search al-gorithm outperforms the BP model on the test set with R2,MSE,RMSE and MAE of 0.95,1.55,1.24 and 0.90,respect-ively.It is also determined that there exists a mapping relation-ship between power,welding current,welding voltage and dy-namic resistance and the diameter of the nugget,which provides a basis for the design of process parameters for weld-ing.
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