基于神经网络代理模型的缺口试样韧性损伤有限元模拟  

Finite Element Simulation of Ductile Damage in Notched Specimens Based on a Neural Network Surrogate Model

作　　者：潘清卓 凌超 Qingzhuo Pan;Chao Ling(School of Science,Harbin Institute of Technology,Shenzhen,518055)

机构地区：[1]哈尔滨工业大学(深圳)理学院,深圳518055

出　　处：《固体力学学报》2025年第1期54-66,共13页Chinese Journal of Solid Mechanics

基　　金：国家自然科学基金项目(12002105)资助.

摘　　要：金属材料在外力和环境作用下,可能发生变形、断裂、腐蚀、磨损等不同形式的失效破坏,其中断裂是其中危害性和破坏性最为显著的一种.韧性断裂是金属中常见的一种断裂形式.从材料内部来看,金属的韧性断裂与微孔洞的形核、扩展和聚合的过程有关.这个过程受应力状态、孔洞体积分数、孔洞形状及温度等众多因素的影响.目前,考虑孔洞演化的韧性损伤模型多采用球形孔洞假设,在理论模型推导中考虑非球形孔洞及其演化对材料韧性损伤过程的影响具有较大难度.此外,在试样和构件尺度上开展韧性损伤的力学分析需要解决跨尺度关联难题.针对这些问题,本文建立了含不同初始形状孔洞的弹塑性代表体元模型,通过有限元模拟系统地分析了初始孔洞形状和应力状态对代表体元应力应变响应和韧性损伤演化的影响.基于代表体元模拟数据和神经网络模型,发展了考虑初始孔洞形状影响的韧性损伤本构代理模型.在此基础上开发了基于神经网络本构模型的有限元计算程序,并分析了初始孔洞形状对带缺口试样韧性损伤过程的影响.Under mechanical loading,metallic materials can fail in various ways,including yielding,fracture,buckling,wear,fatigue,and so on,with fracture being particularly destructive.Ductile fracture,characterized by dimples on the fracture surface,is commonly observed in pure metals and alloys.From the microscopic point of view,the ductile fracture of metals and alloys is closely associated with the nucleation,propagation,and coalescence of voids,influenced by factors such as stress state,void size,void volume fraction,void shape,and temperature.Micromechanics-based models developed for ductile damage considering the void evolution,such as the Gurson model and its extensions,usually presume spherical voids,but creating models that consider realistic void shapes and their evolution presents significant challenges.Moreover,conducting mechanical analyses of ductile failure across specimen and component scales requires addressing cross-scale issues.This study first constructed representative volume element models incorporating isolated voids of different initial shapes.Finite element simulations were carried out based on the representative volume elements by adopting a J2 plasticity model for the matrix,systematically analyzing how initial void shape affects stress-strain responses and ductile damage under triaxial tensile and shear loading conditions.A neural network-based surrogate model was trained with the numerical data generated by the simulations to approximate stress-strain responses and damage evolution.This model effectively predicted how initial void shape influences ductile damage.Subsequently,a user-defined material subroutine was developed and integrated into a commercial finite element code to simulate the impact of initial void shapes on the ductile failure process in notched specimens.Results indicated that a reduced aspect ratio for the voids decreased the damage rate,leading to delayed softening at the specimen level.This work demonstrates the potential of using surrogate models to predict ductile damage i

关 键 词：韧性损伤 孔洞形状 孔洞演化 神经网络 

分 类 号：TG1[金属学及工艺—金属学]