Abstract
A hybrid rule-based/ML approach using linear regression and artificial neural networks (ANNs) determined pitting corrosion descriptors from high-throughput data obtained with Scanning Electrochemical Cell Microscopy (SECCM) on 316 L stainless steel. Non-parametric density estimation determined the central tendencies of the Epit/log(jpit) and Epass/log(jpass) distributions. Descriptors estimated using conditional mean or median curves were compared to their central tendency values, with the conditional medians providing more accurate results. Due to their lower sensitivity to high outliers, the conditional medians were more robust representations of the log(j) vs. E distributions. An observed trend of passive range shortening with increasing testing aggressiveness was attributed to delayed stabilisation of the passive film, rather than early passivity breakdown.
| Original language | English |
|---|---|
| Article number | 82 |
| Number of pages | 15 |
| Journal | npj Materials Degradation |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Dec 2023 |
Bibliographical note
Funding Information:The author, L.B. Coelho, is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique—FNRS (Belgium), which is gratefully acknowledged. D.T. acknowledges financial support to the Fonds de Recherche dans l’Industrie et dans l’Agriculture (FRIA). J.U. and M.B. acknowledge financial support to the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. F.4531.19 and to the Fonds Wetenschappelijk Onderzoek (FWO) under contract G0C3121N. G.B. and G.P. are supported by the Service Public de Wallonie Recherche under grant nr 2010235–ARIAC by DigitalWallonia4.ai. The authors acknowledge Prof. Marjorie Olivier (University of Mons) for providing stainless steel treated plates. The author, L.B. Coelho, would like to thank Dr. Denis Steckelmacher for fruitful discussions on data manipulation and analysis.
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