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

Abstract

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. In this study, the neural network was investigated to predict the hot stamping heating temperature and time conditions based on the color exhibited on the sheet surface after the process. Additionally, the neural network was combined with numerical models to predict the inter-diffusion layer thickness in the Al-Si coating layer, which affects the weldability of the vehicle part, and the amount of hydrogen uptake that directly influences hydrogen embrittlement.