Andrieux, F.F.AndrieuxFrie, C.C.FrieSun, D.-Z.D.-Z.Sun2022-05-062022-05-062021https://publica.fraunhofer.de/handle/publica/416888Aluminum die casting components are widely used in vehicle constructions because of their good compromise between weight reduction and improvement of mechanical properties. The complex geometries of these components with inhomogeneous defect distribution are a relevant issue, as material with higher defect content shows lower fracture strain. It makes the analysis of the damage behavior for crash simulation more challenging. An extensive experimental investigation is required to quantify the scatter as well as the development of a suitable material model to describe it. The casting alloy Castasil® 37 (AlSi9MnMoZr) is investigated. First, a screening investigation based on tensile tests with specimens cut from different positions of a component together with metallographic and computer tom ography (CT) analyses is performed. After segmentation of the fracture surfaces and CT scans the defect distributions are mapped into FE mesh. Moreover, global defect features are extracted from the CT scans and a stochastic model is developed to realize synthetic defect distributions. A defect dependent material model is derived based on the relationship between defect fraction and elastic, plastic and failure properties and assuming the properties of the defect free material. The model is implemented as User Material Subroutine in the FE program LS-DYNA. The stress state dependent matrix failure strain is calibrated considering the upper bound of the experimental tests on specimens with different geometries. Digital image correlation (DIC) analyses are performed to determine local strain s. The model requires a defect distribution as initial condition. Simulation of tensile and bending tests are performed using defect distributions from the fracture surfaces, the CT scans and the stochastic model.enAluminiumgussVersagenPorositätDefektStochastik620Damage modeling of aluminum casting components considering defect distribution for crashworthiness predictionconference paper