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首页> 外文期刊>International journal of systems assurance engineering and management >Identification of plastic properties of metallic structures by artificial neural networks based on plane strain small punch test
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Identification of plastic properties of metallic structures by artificial neural networks based on plane strain small punch test

机译:基于平面应变小冲头试验的人工神经网络识别金属结构的塑性

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摘要

In order to assess the strength of aged and in service components, small punch test (SPT) has emerged. However, it has two disadvantages, firstly using of the hemispherical punch which is difficult to manufacture in most conventional workshops and secondly the known difficulties in obtaining the flat disk samples. This paper discusses a novel approach, the plane strain small punch test to identify the plastic properties of metallic structures. To do so, a new apparatus was designed and manufactured to perform a series of plane strain SPT in room temperature. An artificial neural network was established and trained by the corresponding load displacement responses obtained from the simulations to predict the plastic properties of Stainless Steel 304L.
机译:为了评估老化和维修部件的强度,出现了小型冲压试验(SPT)。然而,它具有两个缺点,首先是使用半球形冲头,这在大多数常规车间中难以制造,其次是在获得平盘样品方面的已知困难。本文讨论了一种新颖的方法,即通过平面应变小冲孔试验来识别金属结构的塑性。为此,设计并制造了一种新设备,以在室温下执行一系列平面应变SPT。建立了一个人工神经网络,并通过从模拟中获得的相应载荷位移响应进行了训练,以预测304L不锈钢的塑性。

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