【6h】

Cost reduction method for delamination monitoring using electrical resistance changes of CFRP beam

机译:利用CFRP梁的电阻变化进行分层监测的降低成本的方法

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摘要

Delamination is a significant defect of laminated composites. The present study employs an electrical resistance change method in an attempt to identify internal delaminations experimentally. The method adopts reinforcing carbon fibers as sensors. In our previous paper, an actual delamination crack in a Carbon Fiber Reinforced Plastics (CFRP) laminate was experimentally identified with artificial neural networks (ANN) or response surfaces created from a large number of experiments. The experimental results were used for learning of the ANN or regression of the response surfaces. For the actual application of the method, it is indispensable to reduce the number of experiments to suppress the total experimental cost. In the present study, therefore, FEM analyses are employed to make sets of data for learning of the ANN. First, electrical conductivity of the CFRP laminate is identified by means of the least estimation error method. After that, the results of FEM analyses are used for learning of the ANN. The method is applied to actual delamination monitoring of CFRP beams. As a result, the method successfully monitored the delamination location and size only with ten experiments.
机译:分层是层压复合材料的重大缺陷。本研究采用电阻变化方法来尝试通过实验识别内部分层。该方法采用增强碳纤维作为传感器。在我们之前的论文中,通过人工神经网络(ANN)或大量实验创建的响应面,通过实验确定了碳纤维增强塑料(CFRP)层压板中的实际分层裂纹。实验结果用于学习神经网络或响应面的回归。对于该方法的实际应用,减少实验次数以抑制总实验成本是必不可少的。因此,在本研究中,采用FEM分析来生成用于学习ANN的数据集。首先,借助最小估计误差法确定CFRP层压板的电导率。之后,将有限元分析的结果用于神经网络的学习。该方法应用于CFRP梁的实际分层监测。结果,该方法仅通过十次实验就成功地监测了分层的位置和大小。

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