Reviewing machine learning of corrosion prediction in a data-oriented perspective

Abstract

This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to determine which ML models have been applied and how well they performed depending on the corrosion topic considered. From an extensive review of corrosion articles presenting comparable performance metrics, a ‘Machine learning for corrosion database’ was created, guiding corrosion experts and model developers in their applications of ML to corrosion. Potential research gaps and recommendations are discussed, and a broad perspective for future research paths is provided.

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision

“…highlighted the power of unsupervised algorithms on the examples clustering of particle distributions, biological tissues, and atomic structures, that is still, however, poorly exploited in electrochemistry. [39,42,[46][47][48] Indeed, such automatized data treatment will provide various local descriptors for each (and all) nanoobjects imaged which can be used to reconstruct the global electrochemical response and in turns unveil the nanoscale contribution contained in an electrode response.…”

CORDATA: an open data management web application to select corrosion inhibitors

“…Data driven technologies and machine leaning are among the latest developments and most promising approaches in corrosion science to guide the discovery and design of more effective and environmentally benign corrosion inhibitors and protective coating systems 3,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . However, one of the main challenges dealing with the application of machine learning to understand and design protective systems is building the datasets required for training the predictive models 26,27 . The collection of experimental data, as well as data management and curation, are among the most time-consuming tasks in the machine learning workflow.…”

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

“…Since the 1980s, researchers have applied artificial neural networks, random forests, and other machine learning algorithms to predict the uniform corrosion of materials and design new corrosion-resistant materials, making notable progress in solving the multi-factor coupling corrosion problem [16][17][18][19][20][21] . This has helped to predict the corrosion rate of low alloy steel and carbon steel, analyze the important factors that affect the corrosion rate, and forecast the local corrosion behavior of Co-based alloys under different compositions, preparation processes, temperatures, static corrosion environments, and corrosion times [22][23][24][25][26] provides a data-oriented overview of the rapidly growing research field covering ML applied to predicting electrochemical corrosion, which highlights assessing the predictive power of different approaches and elaborate on the current status of regression modeling for various corrosion topics 27 . Sharma et al have employed Random Forest method to model measurements of corrosion rates of carbon steel as a function of time when corrosion inhibitors are added in different dosage and doseschedules 28 .…”