锻压技术

热冲压DAC模具钢磨损研究及预测

英文标题：Wear research and prediction on hot stamping DAC die steel

作者：程晓宇张赛军郑朝尹丁伟洋宋世光

分类号：TG76

出版年,卷(期):页码：2025,50(7):211-218

摘要：

为提高热冲压模具磨损预测精度，提出一种耦合温度因素Archard修正模型以及磨损可视化实现方法。针对某汽车A柱加强板模具，设计完成温度范围为25~300 ℃的高温硬度实验和等温摩擦磨损实验。结果显示：DAC模具钢与22MnB5高强钢之间的摩擦因数随温度上升而下降；DAC模具钢的硬度在温升初期逐渐降低，后趋于平稳。整理实验结果，将温度函数项耦合到Archard磨损模型，并基于ABAQUS仿真平台，构建模具热冲压有限元仿真模型。结合Python语言实现模具表面节点的更新算法，并通过后处理云图预测模具表面磨损形貌演化。66332次冲压仿真结果显示，磨损量与蓝光三维扫描仪测量值的误差均控制在8%以内，说明修正模型具有较高的预测精度，为工艺优化及缺陷控制提供理论支持。

In order to improve the prediction accuracy of hot stamping die wear, a temperature-coupled Archard modification model and a wear visualization methodology were proposed. Then, for the die of an automotive A-pillar reinforcement plate, high-temperature hardness experiments at 25-300 ℃ and isothermal friction and wear tests were designed and conducted. The results show that the friction factor between DAC die steel and 22MnB5 high-strength steel decreases with the increasing of temperature, and the hardness of DAC die steel gradually decreases at the beginning of temperature rise and then tends to be stable. The experimental results are summarized and the temperature function is coupled to the Archard wear model. Based on simulation platform ABAQUS, a finite element simulation model of the hot stamping of the die is constructed, the update algorithm of the die surface nodes is implemented in combination with Python language, and the wear morphology evolution of the die surface is predicted by post-processing contour map. The simulation results of 66332 stamping times show that the error between the wear amount and the measurement value of the blue light 3D scanner is controlled within 8%. The results show that the modified model achieves a high prediction accuracy and provides the theoretical support for process optimization and defect control.

基金项目：

高品质热作模具钢研发与产业化 (2020B010184002)

作者简介：程晓宇(2000-)，男，硕士研究生 E-mail：1563142619@qq.com 通信作者：张赛军(1978-)，男，博士，副教授 E-mail：mesjzhang@scut.edu.cn

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