Unsupervised machine learning for automated corrosion staging using optical microscopy images
Abstract Corrosion poses a substantial economic burden, and machine learning is increasingly being explored for its potential in staging, predictive maintenance, and data-driven decision making. This study presents an unsupervised automated corrosion staging method based on image processing and mach...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00635-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849343526491389952 |
|---|---|
| author | Ashwin RajKumar Naveen Paluru Raji Susan Mathew Prathamesh Shenai Dana Abdeen Nicholas Laycock Phaneendra K. Yalavarthy |
| author_facet | Ashwin RajKumar Naveen Paluru Raji Susan Mathew Prathamesh Shenai Dana Abdeen Nicholas Laycock Phaneendra K. Yalavarthy |
| author_sort | Ashwin RajKumar |
| collection | DOAJ |
| description | Abstract Corrosion poses a substantial economic burden, and machine learning is increasingly being explored for its potential in staging, predictive maintenance, and data-driven decision making. This study presents an unsupervised automated corrosion staging method based on image processing and machine learning using optical microscopy (OM) images. It detects and computes (i) the local porosity in a neighborhood of 5 μm Ã- 5 μm at pore locations, and (ii) the deposit thickness in (μm). The local porosity and deposit thickness were used to estimate the chloride concentration factor, associated pH, and the corrosion stage. The approach was tested on 48 ex-service OM images of under-deposit corrosion (UDC). A thickness-based approach yielded an accuracy of ~73% in classifying the corrosion stage in UDC compared to previous time-consuming approaches. This is a significant step in automating the evaluation of the corrosion stage, enabling scalable data-driven corrosion assessment across critical infrastructures. |
| format | Article |
| id | doaj-art-b811209f520f485e87e60cb6736b19dd |
| institution | Kabale University |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-b811209f520f485e87e60cb6736b19dd2025-08-20T03:42:56ZengNature Portfolionpj Materials Degradation2397-21062025-07-01911910.1038/s41529-025-00635-1Unsupervised machine learning for automated corrosion staging using optical microscopy imagesAshwin RajKumar0Naveen Paluru1Raji Susan Mathew2Prathamesh Shenai3Dana Abdeen4Nicholas Laycock5Phaneendra K. Yalavarthy6Department of Computational and Data Sciences, Indian Institute of ScienceDepartment of Computational and Data Sciences, Indian Institute of ScienceSchool of Data Science, Indian Institute of Science Education and ResearchShell Technology Centre, Shell India Markets Private LimitedQatar Shell Research and Technology Centre, Qatar Shell GTL LtdQatar Shell Research and Technology Centre, Qatar Shell GTL LtdDepartment of Computational and Data Sciences, Indian Institute of ScienceAbstract Corrosion poses a substantial economic burden, and machine learning is increasingly being explored for its potential in staging, predictive maintenance, and data-driven decision making. This study presents an unsupervised automated corrosion staging method based on image processing and machine learning using optical microscopy (OM) images. It detects and computes (i) the local porosity in a neighborhood of 5 μm Ã- 5 μm at pore locations, and (ii) the deposit thickness in (μm). The local porosity and deposit thickness were used to estimate the chloride concentration factor, associated pH, and the corrosion stage. The approach was tested on 48 ex-service OM images of under-deposit corrosion (UDC). A thickness-based approach yielded an accuracy of ~73% in classifying the corrosion stage in UDC compared to previous time-consuming approaches. This is a significant step in automating the evaluation of the corrosion stage, enabling scalable data-driven corrosion assessment across critical infrastructures.https://doi.org/10.1038/s41529-025-00635-1 |
| spellingShingle | Ashwin RajKumar Naveen Paluru Raji Susan Mathew Prathamesh Shenai Dana Abdeen Nicholas Laycock Phaneendra K. Yalavarthy Unsupervised machine learning for automated corrosion staging using optical microscopy images npj Materials Degradation |
| title | Unsupervised machine learning for automated corrosion staging using optical microscopy images |
| title_full | Unsupervised machine learning for automated corrosion staging using optical microscopy images |
| title_fullStr | Unsupervised machine learning for automated corrosion staging using optical microscopy images |
| title_full_unstemmed | Unsupervised machine learning for automated corrosion staging using optical microscopy images |
| title_short | Unsupervised machine learning for automated corrosion staging using optical microscopy images |
| title_sort | unsupervised machine learning for automated corrosion staging using optical microscopy images |
| url | https://doi.org/10.1038/s41529-025-00635-1 |
| work_keys_str_mv | AT ashwinrajkumar unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages AT naveenpaluru unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages AT rajisusanmathew unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages AT prathameshshenai unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages AT danaabdeen unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages AT nicholaslaycock unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages AT phaneendrakyalavarthy unsupervisedmachinelearningforautomatedcorrosionstagingusingopticalmicroscopyimages |