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This study presents a transfer learning-based physics-informed neural network (PINN) model for accurately predicting the 3D temperature field in laser-based directed energy deposition of aluminum-magnesium alloy. The model integrates a lightweight architecture and incorporates physical laws to enhance prediction accuracy, achieving an average temperature prediction error of less than 1.3%. The proposed framework demonstrates significant improvements in computational efficiency and can be adapted for other m

Author

Tao

Language

EN