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The Study on Stress Analysis of Compound Steel-Foam-Glass Fiber Reinforced Polymer (GFRP) Structure by lock-in thermography

机译:锁定热成像化合物钢 - 泡沫玻璃纤维增​​强聚合物(GFRP)结构应力分析研究

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The application of thermoelastic stress analysis in compound structure is particularly complicated because of the different material components, which determines the different thermoelastic effect to be depended on the different material property and mechanical performance. This paper describes a theoretical and experimental analysis on full-filed stress distribution from thermoelastic measurements and its application to determination of stress concentration for compound Steel-Foam-GFRP structure. A finite element modeling is proposed to calculate the sum of the principal stress under the condition of dynamic cyclic load. The sum of the principal stress can be measured by means of thermal stress analysis (TSA). Lock-in thermography has been applied to measure the sum of principal stress distribution of component by its high thermal resolution. In this study, Experiments were carried out with Steel-Foam-GFRP compound structure under dynamic periodic load. The thermoelastic constant is calibrated for different component of compound structure, respectively. An artificial neural network (ANN) is proposed to identify the different component stress distribution on whole compound structure. The experimental result shows that the stress distribution of compound structure can be measured and analyzed using lock-in thermography. It is found that the stress distribution of compound structure can be evaluated with good accuracies by lock-in thermography.
机译:热弹性应力分析在化合物结构中的应用特别复杂,因为不同的材料组分,这决定了不同的热弹性效果,依赖于不同的材料性能和机械性能。本文介绍了对热弹性测量的全源应力分布及其应用于复合钢 - 泡沫-GFRP结构的应力浓度的理论和实验分析。提出了有限元建模,以计算动态循环负荷条件下的主应力的总和。主要应力的总和可以通过热应力分析(TSA)来测量。已锁定热成像通过其高热分辨率测量组分的主要应力分布之和。在该研究中,在动态的周期性负荷下用钢 - 泡沫-GFRP化合物结构进行实验。热弹性常数分别校准化合物结构的不同组分。提出了一种人工神经网络(ANN)以识别整个化合物结构上的不同分量应力分布。实验结果表明,可以使用锁定热成像测量和分析化合物结构的应力分布。结果发现,通过锁定热成像,可以用良好的精度评估化合物结构的应力分布。

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