OPTIMUM LEARNING MODEL FOR TEMPERATURE PROFILE PREDICTION IN ADDITIVE MANUFACTURING PROCESS

Abstract

In recent years, several industries have made extensive use of additive manufacturing (AM) as it creates complicated parts by layer-by-layer deposition and offers high customization and rapid production. Nonetheless, hardware failure or thermal stress during the AM process can result in defects. In addition, the thermal history of the AM process is typically simulated using finite element research, but they are expensive and time-consuming. In this research, an essential element of a methodological approach for developing real-time control systems based on data-driven models is designed and developed. Finite element techniques are used to generate the database and resolve time-dependent heat equations The proposed approach makes use of the adaptive falcsreech buzpullet search algorithm (FBSO-LSTM) model, which forecasts the temperatures of subsequent voxels utilizing inputs like laser characteristics, to address problems with extremely unpredictable solutions as well as the temperatures of preceding voxels. The performance with the lowest error values is achieved by the adaptive FBSO-LSTM model utilizing the GAMMA database with values of 6.00, 107.51, 5.24, and 0.26 for MAE, MAPE, MSE and RMSE, respectively. Similarly, the FEM database with values of 6.00, 49.51, 5.26, and 0.27 for MAE, MAPE, MSE and RMSE, respectively.