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Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis

Engineering and Technology

Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis

X. Jiang, Y. Yan, et al.

Discover groundbreaking research by Xue Jiang, Yu Yan, and Yanjing Su that utilizes a data-driven approach with Long Short-Term Memory neural networks to predict free corrosion potential in cobalt-based alloys and duplex stainless steels. This innovative method significantly enhances the forecasting of corrosion behavior over time, surpassing traditional machine learning techniques.

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Playback language: English
Abstract
Predicting localized corrosion in corrosion-resistant alloys is challenging due to the complexities of pitting evolution. This study explores a data-driven method using time-series analysis, specifically a Long Short-Term Memory (LSTM) neural network, to predict free corrosion potential (*E*<sub>corr</sub>) in cobalt-based alloys and duplex stainless steels. The LSTM model, trained on 150 days of immersion testing data, outperformed traditional machine learning methods in predicting *E*<sub>corr</sub> for the subsequent 70 days, demonstrating its ability to capture the time-series dependency of pitting propagation.
Publisher
npj Materials Degradation
Published On
Nov 11, 2022
Authors
Xue Jiang, Yu Yan, Yanjing Su
Tags
localized corrosion
corrosion-resistant alloys
data-driven method
Long Short-Term Memory
free corrosion potential
pitting evolution
neural network

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