论文标题
基于Gurson-Tvergaard-Needman模型的统计代表性微观结构数据的板弯曲中的空隙演变预测
Prediction of void evolution in sheet bending based on statistically representative microstructural data for the Gurson-Tvergaard-Needleman model
论文作者
论文摘要
薄板中的延性损伤是由空隙引起的。对于产品设计来说，预测弯曲组件中空隙的分布至关重要。由于空隙体积分数是Gurson-Tvergaard-Needleman（GTN）模型中的状态变量，因此它用于预测弯曲中空隙的演变。材料参数是根据双相钢的力置换曲线以及从全景扫描 - 电子显微镜图像获得的统计微观结构信息来鉴定的。用最近提出的方案确定了GTN模型的空隙体积分数和特定的空隙群，该方案涉及机器学习算法。
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.
