Study on residual stress prediction of SLM GH3625 high temperature alloy based on GA-BP and PSO-BP neural networks
An artificial neural network model with PSO-BP and GA-BP hybrid algorithms was used to predict the residual stress of GH3625 superalloy by selective laser melting forming.The sample set was generated for the experimental design by response surface meth-od,and the laser power,scanning speed and scanning space were used as the input layer of the model,and the residual stress was used as the output layer of the model for prediction optimization.The correlation coefficient R2 and the average absolute relative error eAARE evaluation indexes were used to validate and compare the prediction models.The results show that BP,GA-BP and PSO-BP neural net-work models can well predict the residual stress of GH3625 superalloy with different process parameters,and the BP neural network opti-mized by the algorithms has higher prediction accuracy.Among them,the GA-BP neural network has the highest prediction accuracy and superior model performance for the residual stress of GH3625 superalloy formed by laser melting forming in the selected area,and its cor-relation coefficient R2 and relative average absolute error eAARE are 0.909 and 2.06％,respectively.
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维普
