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Predictive Elastoplastic Damage Constitutive Law: Establishment of Equivalence Relation between Intrinsic and Extrinsic Material Parameters

机译:预测的弹塑性破坏本构关系:本征和外在材料参数之间的等效关系的建立

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The purpose of the current work is the development and application of a new identification method of material parameters of elastoplastic damage constitutive model under large strains. A relationship relating the intrinsic and extrinsic parameters of a reference material is built and transformed in equivalence relation. Extrinsic parameters concern the shape of their experimental tensile force/elongation curve, however, intrinsic parameters deal with Swift hardening law coupled with an isotropic damage variable. The relationship is carried out from a statistical characterization of a material reference (standard-steel E24). It based on multiple linear regression of a data set obtained according to a full factor design of numerical simulations of mechanical tensile tests. All materials satisfying this equivalence relation belong to the same equivalence class. This is motivated by observing that gathered materials must behave somewhat like the reference material. The material parameters can be immediately identified by only one task by running the found relationship. The current method facilitates the identification procedure and offers a substantial savings in CPU time. However it just needs only one simulation for the identification of similar behavior instead of the few hundred required when using other methods.
机译:当前工作的目的是开发和应用一种新的大应变下弹塑性损伤本构模型材料参数识别方法。建立与参考材料的内在和外在参数有关的关系,并将其转换为等效关系。外在参数涉及其实验拉伸力/伸长率曲线的形状,但是,内在参数处理的是Swift硬化定律以及各向同性的损伤变量。该关系是根据材料参考(标准钢E24)的统计特性来执行的。它基于数据集的多元线性回归,该数据集是根据机械拉伸试验数值模拟的全因子设计获得的。满足此等价关系的所有材料都属于同一等价类。这是通过观察收集到的材料在某种程度上必须类似于参考材料来激发的。通过运行找到的关系,只能通过一项任务立即识别材料参数。当前方法简化了识别过程,并大大节省了CPU时间。但是,只需要一个模拟即可识别相似的行为,而使用其他方法则只需要几百个模拟。

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