Accurate prediction model of bead geometry in crimping butt of the laser brazing using generalized regression neural network

4th Global Conference on Materials Science and Engineering

Rong, Y.M.^1 ; Chang, Y.^1 ; Huang, Y.^1 ; Zhang, G.J.^1 ; Shao, X.Y.^1

State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan

430074, China^1

关键词: Accurate prediction;    Average relative error;    Back propagation artificial neural network (BPANN);    Generalized Regression Neural Network(GRNN);    Generalized regression neural networks;    Mean Square Error (MSE);    Reliability and stability;    Root mean square errors;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/103/1/012036/pdf
DOI  :  10.1088/1757-899X/103/1/012036

来源: IOP

【 摘 要 】

There are few researches that concentrate on the prediction of the bead geometry for laser brazing with crimping butt. This paper addressed the accurate prediction of the bead profile by developing a generalized regression neural network (GRNN) algorithm. Firstly GRNN model was developed and trained to decrease the prediction error that may be influenced by the sample size. Then the prediction accuracy was demonstrated by comparing with other articles and back propagation artificial neural network (BPNN) algorithm. Eventually the reliability and stability of GRNN model were discussed from the points of average relative error (ARE), mean square error (MSE) and root mean square error (RMSE), while the maximum ARE and MSE were 6.94% and 0.0303 that were clearly less than those (14.28% and 0.0832) predicted by BPNN. Obviously, it was proved that the prediction accuracy was improved at least 2 times, and the stability was also increased much more.