铸造技术2025，Vol.46Issue(10)：982-988,7.DOI:10.16410/j.issn1000-8365.2025.5150
基于Transformer模型的8t9Cr3Mo钢锭缩孔疏松预测研究
Prediction of the Shrinkage Porosity of 8-ton 9Cr3Mo Steel Ingot via the Transformer Model
摘要
Abstract
A combined approach of numerical simulation and deep learning was proposed to investigate shrinkage porosity defects during the solidification process of steel ingots.A Transformer neural network model with attention mechanisms was developed for predicting shrinkage porosity.First,finite element numerical simulations were conducted for the solidification process of an 8-ton 9Cr3Mo steel ingot to obtain time series data such as the nodal temperature and solid fraction.The simulation data were subsequently used as input to construct a Transformer regression model with multi-head self-attention mechanisms to predict shrinkage porosity.Finally,the attention weights of the model were analysed to reveal its focus on key features such as the solid fraction at different stages of the solidification process.The results show that the model automatically focuses on the late solidification stage of the steel ingot,which aligns with the physical mechanism of shrinkage formation,providing a data-driven perspective for identifying shrinkage-prone regions.关键词
钢锭/缩孔疏松/数值模拟/Transformer模型/注意力机制/深度学习Key words
steel ingot/shrinkage porosity/numerical simulation/Transformer model/attention mechanism/deep learning分类
金属材料引用本文复制引用
张炜,张超杰,朱喜达,於伟民,陆家生,张立强..基于Transformer模型的8t9Cr3Mo钢锭缩孔疏松预测研究[J].铸造技术,2025,46(10):982-988,7.基金项目
国家自然科学基金(52104317) （52104317）
