Temperature Prediction of Laser Directed Energy Deposition Based on ASSFOA-GRNN Model

Abstract:

To address the issues of low accuracy, long time consumption, and high cost of the traditional temperature prediction methods for laser directed energy deposition (LDED), a machine learning model combined with numerical simulation was proposed to predict the temperature during LDED. A finite element (FE) thermal analysis model was established. The model's accuracy was verified through in-situ monitoring experiments, and a basic database for the predictive model was obtained based on FE simulations. Temperature prediction was performed using a generalized regression neural network (GRNN). To reduce dependence on human experience during GRNN parameter tuning and to enhance model prediction performance, an improved adaptive step-size fruit fly optimization algorithm (ASSFOA) was introduced. Finally, the prediction performance of ASSFOA-GRNN model was compared with that of back-propagation neural network model, GRNN model, and fruit fly optimization algorithm (FOA)-GRNN model. The evaluation metrics included the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), training time, and prediction time. Results show that the ASSFOA-GRNN model exhibits optimal performance regarding RMSE, MAE, and R² indexes. Although its prediction efficiency is slightly lower than that of the FOA-GRNN model, its prediction accuracy is significantly better than that of the other models. This proposed method can be used for temperature prediction in LDED process and also provide a reference for similar methods.