Determining the Parameters of Gurson–Tvergaard–Needleman Model for Predicting the Failure of Wrought and Fused Filament Fabricated 17-4 PH Stainless Steel

材料科学 多孔性 空隙(复合材料) 缩颈 极限抗拉强度 体积分数 可塑性 有限元法 复合材料 冶金 结构工程 工程类
作者
Bita Porrang,Mubashir Ali Ghaffar,Sayyed Ali Hosseini
出处
期刊:Journal of Manufacturing Science and Engineering-transactions of The Asme [ASM International]
卷期号:147 (5) 被引量:2
标识
DOI:10.1115/1.4067615
摘要

Abstract Metal additive manufacturing is an emerging technology for creating metallic parts, with metal fused filament fabrication (FFF) rapidly gaining popularity due to its cost-effectiveness. Despite the acceptable mechanical properties of additively manufactured metals using FFF, a significant technical challenge is the presence of undesirable porosity, which affects material performance. This study aims to model the material behavior of FFF 17-4 PH stainless steel, considering its porosity, using the Gurson–Tvergaard–Needleman (GTN) damage model. The GTN model, which incorporates the micromechanical behavior of ductile metals, shows great potential for failure prediction. The GTN model parameters were identified for both wrought and FFF 17-4 PH stainless steel through a series of proposed methods. Initial void volume fractions were determined using density measurements. The evolution of void volume fractions was experimentally assessed through interrupted uniaxial tensile tests, leading to the analytical derivation of three void nucleation parameters based on continuum damage mechanics. Additional GTN model parameters related to material failure were determined through microscopic analysis of rupture surfaces and finite element (FE) trial-and-error methods. FE simulations using the GTN damage model, represented as porous metal plasticity in abaqus, were conducted to verify the identified parameters. The results demonstrated that the numerical calculations of the FE model are in good agreement with the experimental data. The use of experimentally derived GTN model parameters from the proposed methods effectively predicts material behavior, particularly in the post-necking region where traditional FE modeling fails to simulate the realistic material response.
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