Selective laser melting quality prediction based on adaptive GDSA-BPNN

To solve the quality defect problem of selected laser melting(SLM) parts in additive manufacturing, a new adaptive multi-input and multi-output Back Propagation Neural Network(BPNN) was proposed to predict SLM product quality and solve the problem that the traditional method can not adapt the hyperparameter to different search stages. Firstly, the important process parameters and quality indexes of SLM molding were determined, Huber function was selected as the loss function of BP model, and BP model containing the objective function of structural risk minimization strategy was constructed. Secondly, a prediction model (GDSA-BPNN) based on the combination of the Gradient Descent Search Algorithm (GDSA) and BPNN was established. Three strategies with different learningrates were selected and put intothe adaptivestrategy library,and an adaptive mechanism was used to optimize the hyperparameters of BP model. Finally, the prediction results of GDSA-BPNNmodelwere compared with those of other four models, and the results show that the prediction accuracy of SLM parts quality prediction method based on GDSA-BPNN model has good prediction effect and high prediction accuracy.