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    基于多电磁无损检测的管线钢硬度检测模型

    Hardness detection model of pipeline steel based on multiple electromagnetic nondestructive testing

    • 摘要: 针对材料在塑性变形或者服役过程中,可能产生应力集中的问题,基于3MA综合无损检测技术所提取特征参数与管线钢表面硬度的关系,构建逐步回归模型和BP神经网络模型,实现对管线钢表面硬度的检测。结果表明:电磁特征信号与表面硬度存在相关性;无论是逐步回归模型还是BP神经网络模型,在10%的误差范围内,置信度可达100%。

       

      Abstract: Aiming at the problem that stress concentration could occur during plastic deformation or service of materials, based on the relationship between the characteristic parameters extracted by 3MA comprehensive nondestructive testing technology and the surface hardness of pipeline steel, the stepwise regression model and BP neural network model were constructed to realize the detection of the surface hardness of pipeline steel. The results show that there was a correlation between electromagnetic characteristic signal and surface hardness. Whether stepwise regression model or BP neural network model, in the 10% error range, the confidence could reach 100%.

       

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