Research on thermal efficiency and weld forming coefficient prediction of ultra-high strength steel welded joint under different energy inputs - Siliang Li; Haijiang Liu; Heng Zhang; Xuanjun Pan; Swee Leong Sing (Journal of Materials Research and Technology, 2024)
As an ultra-high strength steel, Al–Si coated 22MnB5 hot stamping steel is widely used in the manufacturing of body-in-white structural parts. Compared with traditional welding technology, laser welding is widely used in the field of automobile body-in-white manufacturing due to its high energy density, small heat affected zone, and high weld quality. For ultra-high strength steel laser welded joints, the weld forming coefficient is an important index to reflect the welding quality. In order to investigate the influence of weld forming coefficient on the Al content and the thermal efficiency, laser welding experiments of ultra-high strength steel under different laser energy inputs are carried out. The melting of the base metal and coating under different laser energy inputs is also considered. Particle swarm optimization (PSO) algorithm is introduced into radial basis function (RBF) neural network model to optimize the central parameters as the RBF neural network model alone is not sufficient in predicting the outcomes. The results shows that if the laser power density is constant (7 × 105 W/cm2), when laser energy inputs are 400 J/cm to 1200 J/cm, the Al content in welded joints is 1.458%–1.886%, the melting efficiency of welded joints is 0.23–0.26, the energy conversion efficiency of welded joints is 0.45–0.46. When the laser energy inputs are lower than 522 J/cm, the Al content of the laser welded joint increases gradually. When the laser energy inputs are higher than 522 J/cm, the Al content of the laser welded joint decreases. The root mean square error(RMSE) of PSO-optimized RBF neural network testing sets prediction results is 0.1551, R-square (R2) is 0.771 and mean absolute percentage error (MAPE) is 2.758%. The prediction accuracy of the PSO-optimized RBF neural network model for the upper surface weld width, waist weld width, and lower surface weld width is 93.9%, 91.1%, and 92.2%.