激光焊接接头剪切GTN模型损伤参数识别

doi:10.13229/j.cnki.jdxbgxb.20230157

摘要/Abstract

摘要：

利用试验与仿真结果结合神经网络和遗传算法的方法，对DP980激光焊接接头的力学性能不均匀特性进行了研究，得到了剪切修正Gurson-Tvergaard-Needleman模型优化焊接接头的损伤参数，将优化结果代入有限元模型和试验结果进行对比以验证拟合参数的准确性，并通过孔洞体积分数变化分析杯突试样横向裂纹扩展的原因。通过以上方法获得了准确的焊接接头力学性能，可应对复杂应力状态下的焊接接头断裂损伤的分析预测。

关键词: 材料加工, 激光焊接, 损伤模型, 参数识别, 杯突试验

Abstract:

Experimental and simulation results were combined with neural network and genetic algorithm methods to study the non-uniform mechanical properties of DP980 laser welded joints. The shear correction Gurson-Tvergaard-Needleman （GTN） model was obtained to optimize the damage parameters of the welded joints. The optimization results were substituted into the finite element model and compared with the experimental results to verify the accuracy of the fitting parameters， and analyze the cause of transverse crack propagation in cup-shaped specimens through changes in pore volume fraction. Accurate mechanical properties of welded joints were obtained through the above methods to analyze and predict fracture damage of welded joints under complex stress states.

Key words: material processing, laser welding, damage model, parameter identification, cupping test

中图分类号:

TG407

陈兵,张杨坤,王洋,刘晟哲,韩烬阳. 激光焊接接头剪切GTN模型损伤参数识别[J]. 吉林大学学报(工学版), 2024, 54(12): 3468-3477.

Bing CHEN,Yang-kun ZHANG,Yang WANG,Sheng-zhe LIU,Jin-yang HAN. Damage parameter identification of laser welded joint shear Gurson⁃Tvergaard⁃Needleman model[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3468-3477.

图/表 19

表1

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表2

遗传算法优化结果"

结束	$f N w$	$ε N w$	$f c w$	$f F w$	$k w w$	$f N h$	$ε N h$	$f c h$	$f F h$	$k w h$
优化值	0.085 2	0.119 2	0.146 2	0.170 1	2.982 1	0.125 33	0.121 2	0.125 3	0.190 2	2.887 2
最终取值	0.085	0.119	0.146	0.170	2.982	0.125	0.121	0.125	0.190	2.887

表2

图11

图12

图13

图14

图15

图16

图17

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材料组分	质量分数/%	材料组分	质量分数/%
C	0.095	CR	0.500
Si	0.350	Mo	0.200
Mn	2.300	Ti	0.040
P	0.015	Fe	余量
S	0.004