Analysis of characteristic prediction of aluminized boron steel after the hot stamping process using an image color-based neural network.

Hot stamping is an innovative technology that enables the production of high-strength automotive body parts by heating the material to a high temperature and simultaneously forming and quenching it in-die. The process results in parts with excellent strength-to-weight ratios, which are essential for the automotive industry. The widely used 22MnB5 material is heated to temperatures above 900 °C, and an Al-Si coating is applied to prevent the formation of oxide scale on the sheet surface. The distinctive color on the sheet surface after hot stamping is produced by the Al-Si coating. This phenomenon is attributed to the formation of Al2O3 on the surface of the Al-Si coating layer and the diffusion of Fe from the substrate into the Al-Si coating layer, both of which are significantly influenced by the heating time and temperature. The hot stamping materials coated with Al-Si show an increase in the thickness of the inter-diffusion layer as the heating temperature and time increase. This leads to an increase in welding resistance and the potential for weld performance. Moreover, as hydrogen uptake increases, the likelihood of hydrogen embrittlement becomes significantly higher. Thus, the optimal heating temperature and duration time are critical parameters in the hot stamping process. To confirm that hot stamping components were subjected to the proper heating conditions, it was essential to extract a sample for analysis. However, in this study, a neural network approach was utilized, enabling the heating temperature and duration time conditions of the hot stamping section to be determined based solely on image information. Furthermore, an image-based model was applied to quantitatively predict the thickness of the inter-diffusion layer that affects weldability and the hydrogen uptake that influences hydrogen embrittlement. [ABSTRACT FROM AUTHOR]