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Non-destructive determination of microstructural/mechanical properties and thickness variations in API X65 steel using magnetic hysteresis loop and artificial neural networks
Nondestructive Testing and Evaluation ( IF 3.0 ) Pub Date : 2019-09-05 , DOI: 10.1080/10589759.2019.1662901
Ali Mirzaee 1 , Saeed Kahrobaee 1 , Iman Ahadi Akhlaghi 2
Affiliation  

ABSTRACT In this paper, a combination of non-destructive magnetic technique and artificial neural networks is introduced, firstly, to ensure that the heat treatment process applied to a given API X65 steel sample results in desired microstructure and mechanical properties and secondly, to determine thickness variation which may occurs as a result of corrosion effect. To evaluate the effects of microstructure/mechanical properties and thickness variations on magnetic parameters, the magnetic hysteresis loop method has been applied on API X65 steel specimens with the thicknesses of 1–4 mm, each subjected to four different heat-treating cycles (austenitised samples were cooled in furnace, air, oil and water). It was found that the magnetic parameters extracted from hysteresis loop are strongly dependent on both the cooling rate of the applied heat treatment (which varies the morphology and grain size of ferrite phase), and thickness of the sample. In the proposed method, probabilistic and radial-basis function neural networks have been used to simultaneously determine the microstructure, mechanical properties and thickness with high reliability and accuracy. Experimental results show that using a simple probabilistic neural network, the type of heat-treatment process applied to the sample under test could be perfectly determined. Moreover, thickness estimation of the sample, with a radial basis neural network, has an error less than 0.05 mm, which is actually outstanding.

中文翻译:

使用磁滞回线和人工神经网络无损测定 API X65 钢的显微组织/机械性能和厚度变化

摘要 在本文中,介绍了无损磁性技术和人工神经网络的结合,首先,确保应用于给定 API X65 钢样品的热处理工艺产生所需的显微组织和机械性能,其次,确定厚度由于腐蚀效应而可能发生的变化。为了评估微观结构/机械性能和厚度变化对磁性参数的影响,磁滞回线方法已应用于厚度为 1-4 mm 的 API X65 钢试样,每个试样都经过四个不同的热处理循环(奥氏体化试样)在炉、空气、油和水中冷却)。发现从磁滞回线提取的磁参数强烈依赖于施加热处理的冷却速率(改变铁素体相的形态和晶粒尺寸)和样品的厚度。在所提出的方法中,概率和径向基函数神经网络已被用于同时确定微观结构、机械性能和厚度,具有高可靠性和准确性。实验结果表明,使用简单的概率神经网络,可以完美地确定应用于被测样品的热处理工艺类型。而且,采用径向基神经网络对样品的厚度估计,误差小于0.05 mm,实际上非常出色。和样品的厚度。在所提出的方法中,概率和径向基函数神经网络已被用于同时确定微观结构、机械性能和厚度,具有高可靠性和准确性。实验结果表明,使用简单的概率神经网络,可以完美地确定应用于被测样品的热处理工艺类型。而且,采用径向基神经网络对样品的厚度估计,误差小于0.05 mm,实际上非常出色。和样品的厚度。在所提出的方法中,概率和径向基函数神经网络已被用于同时确定微观结构、机械性能和厚度,具有高可靠性和准确性。实验结果表明,使用简单的概率神经网络,可以完美地确定应用于被测样品的热处理工艺类型。而且,采用径向基神经网络对样品的厚度估计,误差小于0.05 mm,实际上非常出色。实验结果表明,使用简单的概率神经网络,可以完美地确定应用于被测样品的热处理工艺类型。而且,采用径向基神经网络对样品的厚度估计,误差小于0.05 mm,实际上非常出色。实验结果表明,使用简单的概率神经网络,可以完美地确定应用于被测样品的热处理工艺类型。而且,采用径向基神经网络对样品的厚度估计,误差小于0.05 mm,实际上非常出色。
更新日期:2019-09-05
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