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Microstructure-sensitive estimation of small fatigue crack growth in bridge steel welds

机译:桥梁钢焊缝中微小疲劳裂纹扩展的微观敏感性估计

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摘要

A probabilistic finite element model is implemented to estimate microstructurally small fatigue crack growth in bridge steel welds. Simulations are based on a microstructure-sensitive crystal plasticity model to quantify fatigue indicator parameters (FIPs) at the slip system level and a fatigue model that relates FIPs to fatigue lives of individual grains. Microstructures from three weld zones, namely, fusion zone (FZ), heat affected zone (HAZ), and base metal (BM), are constructed based on their microstructural attributes such as grain morphology, size, and orientation. Statistical volume elements (SVEs) are generated and meshed independently for the three welding zones. Each grain within the SVEs is divided into several slip bands parallel to crystallographic planes. During the loading process, cracks nucleate at the slip bands (SBs) with the largest FIP next to the free surface. The crack extension path is assumed to be transgranular along SBs and the number of cycles required to crack the neighbor grain is calculated by the corresponding FIP-based crack growth rate equation. The simulation process is carried out using ABAQUS with a user defined subroutine UMAT for crystal plasticity. After the calibration of the constitutive model and irreversibility parameters, numerical simulations for small crack growth in three zones are presented. The crack length vs. the predicted fatigue resistance shows significant differences in the mean values and variability among the three weld zones.
机译:应用概率有限元模型来估计桥梁钢焊缝中的微结构小疲劳裂纹扩展。模拟基于对微观结构敏感的晶体可塑性模型来量化滑移系统水平的疲劳指标参数(FIP),以及将FIP与单个晶粒的疲劳寿命相关联的疲劳模型。根据三个焊接区的微结构属性(例如晶粒形态,尺寸和方向)构造了三个焊接区的微结构,即熔合区(FZ),热影响区(HAZ)和贱金属(BM)。统计体积元素(SVE)会针对三个焊接区域独立生成和划分网格。 SVE内的每个晶粒被分成平行于晶体平面的几个滑移带。在加载过程中,裂纹在滑动带(SBs)处成核,且自由表面附近的FIP最大。假定裂纹扩展路径沿SB沿晶界方向延伸,并且通过相应的基于FIP的裂纹扩展速率方程计算裂纹相邻晶粒所需的循环数。使用带有用户定义的子程序UMAT的ABAQUS进行模拟过程,以实现晶体可塑性。在对本构模型和不可逆参数进行校准后,给出了三个区域小裂纹扩展的数值模拟。裂纹长度与预计的抗疲劳性之间在三个焊接区域之间均值和变异性均表现出显着差异。

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