摘要
加筋板的极限载荷是结构设计与校核中的一项重要指标,当前横向拉伸载荷作用下的研究还比较少见。为此,本文首先采用结合数字图像相关技术的拉伸试验及机器学习方法对激光焊接加筋板的极限载荷进行了系统研究,初步结果表明,机器学习方法虽然可高效预测与壳单元有限元模型吻合很好的结果,但预测结果较试验结果偏大。然后,为了探究其原因,通过考虑焊接变形、残余应力和材料性能弱化,结合试验结果并基于建立的极限载荷有限元精细分析模型,对激光焊接加筋板的失效机理进行了详细分析。研究结果表明,焊接变形和残余应力会弱化结构的承载性能,为了获得更加精确的预测结果,需在机器学习模型中考虑两者的影响。
The ultimate load of a stiffened plate is an important indicator in design and check,but the research is relatively scarce under transverse tensile loads.Therefore,this paper firstly conducted a systematic study using a tension experiment combined with digital image correlation techniques and a machine learning method.Preliminary results indicate that although machine learning methods can efficiently predict results that align well with the finite element model with shell elements,the prediction data tend to be larger than experimental data.To investigate the mechanisms for this phenomena,a detailed model of the failure of the laser-welded stiffened plates was conducted based on the established finite element model of ultimate load considering welding deformation,residual stresses,and material property degradation.The research results indicate that welding deformation and residual stresses weaken the structural load capacity.To achieve more accurate predictive results,the influence of both factors needs to be considered in machine learning models.
作者
郭振飞
雷振坤
GUO Zhenfei;LEI Zhenkun(National Frontiers Science Center for Industrial Intelligence and Systems Optimization,Northeastern University,Shenyang 110819,Liaoning,China;State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology,Dalian 116024,Liaoning,China)
出处
《实验力学》
CSCD
北大核心
2024年第5期637-646,共10页
Journal of Experimental Mechanics
基金
国家自然科学基金项目(12272080,11972106)。
关键词
极限载荷
数字图像相关
激光焊接
机器学习
残余应力
ultimate load
digital image correlation
laser welding
machine learning
residual stresses
