Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning
Machine learning (ML) is providing a new design paradigm for many areas of technology, including corrosion inhibition. However, ML models require relatively large and diverse training sets to be most effective. This paper provides an overview of developments in corrosion inhibitor research, focussin...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Corrosion and Materials Degradation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-5558/3/4/37 |
| _version_ | 1827641249183563776 |
|---|---|
| author | Anthony E. Hughes David A. Winkler James Carr P. D. Lee Y. S. Yang Majid Laleh Mike Y. Tan |
| author_facet | Anthony E. Hughes David A. Winkler James Carr P. D. Lee Y. S. Yang Majid Laleh Mike Y. Tan |
| author_sort | Anthony E. Hughes |
| collection | DOAJ |
| description | Machine learning (ML) is providing a new design paradigm for many areas of technology, including corrosion inhibition. However, ML models require relatively large and diverse training sets to be most effective. This paper provides an overview of developments in corrosion inhibitor research, focussing on how corrosion performance data can be incorporated into machine learning and how large sets of inhibitor performance data that are suitable for training robust ML models can be developed through various corrosion inhibition testing approaches, especially high-throughput performance testing. It examines different types of environments where corrosion by-products and electrolytes operate, with a view to understanding how conventional inhibitor testing methods may be better designed, chosen, and applied to obtain the most useful performance data for inhibitors. The authors explore the role of modern characterisation techniques in defining corrosion chemistry in occluded structures (e.g., lap joints) and examine how corrosion inhibition databases generated by these techniques can be exemplified by recent developments. Finally, the authors briefly discuss how the effects of specific structures, alloy microstructures, leaching structures, and kinetics in paint films may be incorporated into machine learning strategies. |
| first_indexed | 2024-03-09T17:10:51Z |
| format | Article |
| id | doaj.art-f30dac7ed25d439cbf2784ed3fe51779 |
| institution | Directory Open Access Journal |
| issn | 2624-5558 |
| language | English |
| last_indexed | 2024-03-09T17:10:51Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Corrosion and Materials Degradation |
| spelling | doaj.art-f30dac7ed25d439cbf2784ed3fe517792023-11-24T14:05:56ZengMDPI AGCorrosion and Materials Degradation2624-55582022-11-013467269310.3390/cmd3040037Corrosion Inhibition, Inhibitor Environments, and the Role of Machine LearningAnthony E. Hughes0David A. Winkler1James Carr2P. D. Lee3Y. S. Yang4Majid Laleh5Mike Y. Tan6Institute for Frontier Materials, Deakin University, Waurn Ponds, Geelong 3216, AustraliaAdvanced Materials and Healthcare Technologies, School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, UKSchool of Materials, University of Manchester, Manchester M13 9PL, UKDepartment of Mechanical Engineering, University College London, London WC1E 7JE, UKCSIRO Manufacturing, Clayton 3168, AustraliaInstitute for Frontier Materials, Deakin University, Waurn Ponds, Geelong 3216, AustraliaSchool of Engineering, Deakin University, Waurn Ponds, Geelong 3216, AustraliaMachine learning (ML) is providing a new design paradigm for many areas of technology, including corrosion inhibition. However, ML models require relatively large and diverse training sets to be most effective. This paper provides an overview of developments in corrosion inhibitor research, focussing on how corrosion performance data can be incorporated into machine learning and how large sets of inhibitor performance data that are suitable for training robust ML models can be developed through various corrosion inhibition testing approaches, especially high-throughput performance testing. It examines different types of environments where corrosion by-products and electrolytes operate, with a view to understanding how conventional inhibitor testing methods may be better designed, chosen, and applied to obtain the most useful performance data for inhibitors. The authors explore the role of modern characterisation techniques in defining corrosion chemistry in occluded structures (e.g., lap joints) and examine how corrosion inhibition databases generated by these techniques can be exemplified by recent developments. Finally, the authors briefly discuss how the effects of specific structures, alloy microstructures, leaching structures, and kinetics in paint films may be incorporated into machine learning strategies.https://www.mdpi.com/2624-5558/3/4/37machine learninghigh-throughput testingcorrosion inhibitionlocalised corrosionX-ray CTdata-constrained modelling |
| spellingShingle | Anthony E. Hughes David A. Winkler James Carr P. D. Lee Y. S. Yang Majid Laleh Mike Y. Tan Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning Corrosion and Materials Degradation machine learning high-throughput testing corrosion inhibition localised corrosion X-ray CT data-constrained modelling |
| title | Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning |
| title_full | Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning |
| title_fullStr | Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning |
| title_full_unstemmed | Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning |
| title_short | Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning |
| title_sort | corrosion inhibition inhibitor environments and the role of machine learning |
| topic | machine learning high-throughput testing corrosion inhibition localised corrosion X-ray CT data-constrained modelling |
| url | https://www.mdpi.com/2624-5558/3/4/37 |
| work_keys_str_mv | AT anthonyehughes corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning AT davidawinkler corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning AT jamescarr corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning AT pdlee corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning AT ysyang corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning AT majidlaleh corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning AT mikeytan corrosioninhibitioninhibitorenvironmentsandtheroleofmachinelearning |