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| 激光焊接加筋板极限载荷的机器学习预测研究 |
| Study on Machine Learning Prediction of Ultimate Load of Laser Welding stiffened Panel |
| 投稿时间：2024-01-09 修订日期：2024-01-31 |
| DOI： |
| 中文关键词: 极限载荷 数字图像相关 激光焊接 机器学习 残余应力 |
| 英文关键词:Ultimate load digital image correlation laser welding machine learning residual stresses |
| 基金项目:国家自然科学基金（12272080, 11972106） |
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| 中文摘要: |
| 加筋板的极限载荷是设计与校核中的一项重要指标，但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实验以及机器学习方法进行了系统研究。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，但预测结果较实验结果偏大。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，结合实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二者的影响。 |
| 英文摘要: |
| The ultimate load of a stiffened plate is an important indicator in design and check, but research is relatively scarce under transverse tensile loads. Therefore, this paper first 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 predicted results tend to be larger than experimental results. To investigate the reasons for this, a detailed analysis of the failure mechanism of the laser-welded stiffened plates was conducted based on the established finite element model of ultimate load, taking into account 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. |
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