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Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis

Chemistry

Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis

L. B. Coelho, D. Torres, et al.

Discover a groundbreaking hybrid approach blending rule-based methods and machine learning to enhance our understanding of pitting corrosion on 316L stainless steel. This research, conducted by a team of experts including Leonardo Bertolucci Coelho and Daniel Torres, reveals insights into the stability of passive films, dramatically impacting engineering practices in materials science.

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Playback language: English
Abstract
This research paper presents a hybrid rule-based/machine learning (ML) approach for estimating pitting corrosion descriptors from high-throughput data obtained using Scanning Electrochemical Cell Microscopy (SECCM) on 316L stainless steel. Linear regression and artificial neural networks (ANNs) were employed to determine pitting corrosion descriptors. Non-parametric density estimation assessed the central tendencies of the distributions. Conditional medians provided more accurate results than conditional means due to their robustness to outliers. The study observed a passive range shortening with increased testing aggressiveness, attributed to delayed passive film stabilization rather than early passivity breakdown.
Publisher
npj Materials Degradation
Published On
Oct 21, 2023
Authors
Leonardo Bertolucci Coelho, Daniel Torres, Vincent Vangrunderbeek, Miguel Bernal, Gian Marco Paldino, Gianluca Bontempi, Jon Ustarroz
Tags
pitting corrosion
scanning electrochemical cell microscopy
machine learning
316L stainless steel
passive film stabilization
linear regression
artificial neural networks

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