基于Gurson-Tvergaard-Needleman模型统计代表性微观组织数据的板料弯曲中孔洞演化预测
Prediction of void evolution in sheet bending based on statistically representative microstructural data for the Gurson-Tvergaard-Needleman model
摘要：
钢板中的韧性损伤是由空洞引起的。预测弯曲构件中的孔洞分布对产品设计至关重要。由于孔隙体积分数是Gurson-Tvergaard-Needleman（GTN）模型中的状态变量，因此将其应用于预测弯曲过程中的孔隙演化。材料参数识别的基础上的力-位移曲线的双相钢，也通过从全景扫描电子显微镜图像获得的统计微观结构信息。利用最近提出的一种基于机器学习算法的GTN模型的空泡体积分数和特定空泡数的确定方法。
英文摘要：
Ductile damage in sheet steels is caused by voids. It is crucial for product design to predict the distribution of voids in bent components. Since the void volume fraction is a state variable in the Gurson-Tvergaard-Needleman (GTN) model, it is applied to predict the evolution of voids in bending. Material parameters are identified based on force-displacement curves of a dual phase steel and also through statistical microstructural information obtained from panoramic scanning-electron microscopy images. The void volume fraction and particular void populations of GTN-model are determined with a recently proposed scheme, which involves machine learning algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
