利用新型经验相关性和神经网络对热机械载荷下的金属梁进行损伤量化

B. A. Zai, Asif Mansoor, Umer Abdullah Siddiqui, Jahanzeb Javed, Najam us Saqib
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引用次数: 0

摘要

本研究探讨了在热机械载荷作用下,断裂深度和裂缝位置对悬臂梁动态响应的相互作用。结构的刚度受温度影响很大,刚度的变化会改变响应的阻尼、频率和振幅。这些变化为测量铝 2024 试样在热机械载荷下的损坏情况提供了依据。悬臂梁用于在高温(如 50°C、100°C、150°C 和 200°C)和室温(非加热)下进行的实验。该分析考虑了不同初始断裂深度和位置的悬臂梁。实验、分析和数值工作的结果一致。这项研究主要填补了近期对存在耦合载荷的金属结构进行结构健康监测的技术空白。首次在热机械载荷下对梁进行了动态响应实验。动态参数随材料刚度的变化而变化,因此,通过使用专门设计的温度控制器将温度作为关键变量,也对这种变化进行了研究。介绍了一种利用目前可获得的实验数据估算金属结构损伤的新技术,随后将其与神经网络方法进行了比较。该工具有助于利用动态响应、温度以及地下裂缝的发现来发现和量化损伤。结果为诊断运行状态下热机械载荷作用下任何特定瞬间的裂纹生长建立了一条清晰的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Damage quantification of a metallic beam under thermo-mechanical loads using novel empirical correlations and neural network
This research examines the interactions between fracture depth and crack location on a cantilever beam’s dynamic response under thermomechanical loads. The structure’s stiffness is significantly influenced by temperature, and changes in stiffness can alter the response’s damping, frequency, and amplitude. The basis for measuring damage to an Aluminium 2024 specimen under thermomechanical loads is provided by these variations. Cantilever beams are used in experiments that are carried out at elevated temperatures, such as 50°C, 100°C, 150°C, and 200°C, as well as at room temperature (non-heating). This analysis takes into account a cantilever beam with different initial fracture depths and positions. The outcomes of the experimental, analytical, and numerical work are found to be in good accord. This research mainly fill the gaps of recent techniques for structure health monitoring of metallic structures where the coupled loads exist. Dynamic response formulation is presented experimentally on beam for the first time under thermo-mechanical loads. The dynamic parameters vary with material stiffness, thus; this change is also investigated by introducing the temperature as a key variable using a specially designed temperature controller. A novel technique is presented for damage estimation in metallic structures using the experimental data that is currently accessible which is later compared with the neural network approach. This tool helps in finding out and quantifying damagesusing dynamic response, temperature along withfinding of subsurface cracks. The results establisha clearway of diagnosing the crack growth at any particular instant under thermo-mechanical loads within the operational condition.
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