Heat conduction analysis and performance prediction of metallurgical materials based on finite element method

In this paper, finite element simulation of heat transfer process is carried out using Cu composites reinforced with TiB2 of different particle sizes.Based on the FEA data, the BP neural network algorithm is integrated and optimized by the MEA algorithm to establish the FEA-MEA-BP performance prediction model.The results of thermal conductivity analysis show that the correction factor of the simulated thermal conductivity value of TiB2/Cu composites can be calculated using the finite element method as 2.3.Compared with the actual value measured by the LINSEISLFA1600 laser thermal conductivity meter, the fluctuation of the simulated thermal conductivity results from the experimental results is no more than 10% between 50~200C, and the simulation performance has a high degree of accuracy.Taking 304L stainless steel as a sample, the RMSE, MAE and R are improved to different degrees compared with other models, so the performance of the FEA-MEA-BP model is excellent in terms of the accuracy of prediction.