纳米改性微合金钢电化学阻抗谱酸蚀预测的人工神经网络

D. Colorado-Garrido, S. Serna, M. Cruz-Chávez, J. Hernández, B. Campillo
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引用次数: 3

摘要

在时效热处理的基础上,通过纳米改性,在微合金钢的表面组织上形成不同程度的纳米析出物,从而改善钢的力学性能。在酸性腐蚀条件下,电化学阻抗谱(EIS)技术可用于鉴定改性微合金钢的腐蚀性能。本文利用人工神经网络(ANN)建立了微合金钢在酸性腐蚀下的EIS-Ny quist曲线预测模型。对于人工神经网络,采用了基于Levenberg-Marquardt学习算法、双曲正切s型传递函数和线性传递函数的方法。该模型考虑了实际阻抗、时间和钢的暴露温度的变化。所建立的模型可以在较短的模拟时间内进行预测,说明了人工神经网络的实用性。在验证数据集上,所有实验数据库的模拟数据和理论数据检验都符合R2 > 0.98。这些结果表明,人工神经网络可能在基于实验室测量的组件寿命预测中发挥关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Networks for Electrochemical Impedance Spectroscopy Sour Corrosion Predictions of Nano-modified Microalloyed Steels
Micro alloyed steels mechanical properties can be modified by nano-modification based on aging heat treatments inducing different levels of nano precipitates on their surface microstructure. Under sour corrosion, electrochemical impedance spectroscopy (EIS) technique could serve to identify the modified micro alloyed steel corrosion properties. This paper present a predictive model for EIS-Ny quist curves using artificial neural networks (ANN) of micro alloyed steels under sour corrosion. For the ANN, an approach based on Levenberg–Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and a linear transfer function was used. The model takes into account of the variations of the real impedance, time and steel exposure temperature. The developed model can be used for prediction at short simulation times illustrating the utility of the ANN. On the validation data set, the simulations and the theoretical data tests were in good agreement with R2 > 0.98 for all experimental databases. These results suggest that ANN may play a key role in making lifetime predictions for components based on laboratory measurements.
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