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Corrosion Management in a Digital World
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Corrosion Management in a Digital World

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Corrosion".

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 8831

Special Issue Editor

Dr. Andrew Spowage

E-Mail Website
Guest Editor
School of Engineering and Materials Science, Queen Mary University of London, London, UK
Interests: performance of materials; characterisation of materials; testing of materials; failure investigation; materials selection; materials in design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To a greater or lesser extent, scientific and engineering professionals have been living and working within the digital economy for several years. Digitalisation and the wide range of disruptive technologies that it brings with it are sweeping across many industrial sectors. Big data, analytics, machine learning, artificial intelligence, digital twin and the Internet of Things may only be buzzwords to many, but they are becoming essential components in corrosion management. However, these technologies hold tremendous potential advantages if properly integrated into the engineer’s tool box. If not integrated properly, mistakes can be extremely expensive and present tremendous risks to people, assets and the environment.

In recent years, there has been a wide range examples of digitalisation presenting new opportunities in corrosion research, corrosion design and operational corrosion management. However, integration of traditional corrosion knowledge into the new digital economy and vice versa is still very much in its infancy. Further, integrating these new technologies into existing practices and updating our own skills sets presents significant challenges to researchers, engineers and companies.

This Special Issue presents a unique opportunity for corrosion engineers, researchers and digital technologists to interact with and report on current best practice, lesions learned, the latest technologies and projections for what the future may have in store. It is my pleasure to invite academicians, engineers, corrosion professionals and digital leaders to submit a manuscript to this Special Edition. The scope of the Special Edition is broad; the following areas are of particular interest:

  • Data and design
  • Corrosion prediction and management;
  • Corrosion monitoring and measurement;
  • Corrosion inspection and integrity management;
  • Using data for more proactive corrosion management;
  • Making data-driven decisions;
  • Integrating digital technologies;
  • Corrosion and drone-based technologies;
  • Digitalisation and cost reduction.

Dr. Andrew Spowage
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Digital revolution
  • Internet of Things
  • Digital twin
  • Big data
  • Data analytics
  • Artificial intelligence
  • Corrosion
  • Corrosion management
  • Asset Integrity
  • Monitoring and inspection
  • Maintenance

Published Papers (4 papers)

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Research

13 pages, 10039 KiB  
Article
Design of a Real-Time Corrosion Detection and Quantification Protocol for Automobiles
by Kunj Dhonde, Mitra Mirhassani, Edwin Tam and Susan Sawyer-Beaulieu
Materials 2022, 15(9), 3211; https://0-doi-org.brum.beds.ac.uk/10.3390/ma15093211 - 29 Apr 2022
Cited by 2 | Viewed by 1670
Abstract
Corrosion can compromise the integrity of the vehicle. Instead, “rust proofing” a vehicle can prolong its usable life span, reducing material waste overall and permitting greater salvageability at the end of the vehicle’s life. For rust proofing, a definitive and consistent approach for [...] Read more.
Corrosion can compromise the integrity of the vehicle. Instead, “rust proofing” a vehicle can prolong its usable life span, reducing material waste overall and permitting greater salvageability at the end of the vehicle’s life. For rust proofing, a definitive and consistent approach for detecting corrosion could be beneficial. Instead, most vehicle corrosion detection and assessment is performed visually and in an ad hoc manner without following any particular guidelines. The visual examination of corrosion depends highly on the method of analyzing and interpreting the corrosion, as well as operator’s experience in assessing and applying rust proofing. As a result, any visual assessment strategy needs standardization to minimize human error. An automated method is proposed to identify and analyze surface rust and appraise its severity for vehicles. The method demonstrated is 96% effective, low-cost, and has low computational complexity. Subsequently, the method has the potential to be conveyed to different advanced devices, such as smartphones, to measure corrosion, decreasing errors and improving measurement accuracy. Low implementation cost, and high reliability of the method contributes to its ease of use in the field, and hence, advances its accessibility to automotive professionals to identify and monitor corrosion levels, without the interference of human errors. Full article
(This article belongs to the Special Issue Corrosion Management in a Digital World)
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19 pages, 2460 KiB  
Article
Measuring Corrosion on Vehicles, in Real-Time, Using Digital Imaging and Analysis Techniques
by Susan Sawyer-Beaulieu, Edwin Tam and Abdulkadir Hussein
Materials 2022, 15(9), 3053; https://0-doi-org.brum.beds.ac.uk/10.3390/ma15093053 - 22 Apr 2022
Cited by 1 | Viewed by 1600
Abstract
This research outlines a digital imaging method under development to systemize a rapid in-field corrosion evaluation measure, to evaluate and monitor the degree of corrosion on target corrosion-prone parts on light-duty vehicles. This procedure uses digital imaging to study and compare corrosion levels [...] Read more.
This research outlines a digital imaging method under development to systemize a rapid in-field corrosion evaluation measure, to evaluate and monitor the degree of corrosion on target corrosion-prone parts on light-duty vehicles. This procedure uses digital imaging to study and compare corrosion levels of 228 vehicles that were treated with aftermarket applications of corrosion prevention products versus 141 vehicles that were untreated. It introduces a Corrosion Index (CI) as a common measure. Single-factor and two-factor analysis of variance (ANOVA) of the digitally-based corrosion measurements show statistically significant correlations between CI and treatment (treated versus untreated), as well as CI, vehicle age, and treatment. The ANOVA results show that the aftermarket-treated vehicles have statistically significantly less corrosion than the untreated vehicles, demonstrating that digital image analysis is a viable method of measuring corrosion on corrosion-prone vehicle parts, offering the potential to monitor and track the performance/efficacy of aftermarket corrosion treatment in real-time. Full article
(This article belongs to the Special Issue Corrosion Management in a Digital World)
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11 pages, 1768 KiB  
Article
Migration of Volatile Organic Compounds (VOCs) from PEX-a Pipes into the Drinking Water during the First Five Years of Use
by Aino Pelto-Huikko, Merja Ahonen, Mia Ruismäki, Tuija Kaunisto and Martti Latva
Materials 2021, 14(4), 746; https://0-doi-org.brum.beds.ac.uk/10.3390/ma14040746 - 05 Feb 2021
Cited by 4 | Viewed by 2273
Abstract
A brand-new office building in Rauma, Finland, was used to study the first five years of PEX-a drinking water pipes in normal use. Both pipe material and water samples from hot and cold-water pipelines were analyzed. Migration of volatile organic compounds (VOC) from [...] Read more.
A brand-new office building in Rauma, Finland, was used to study the first five years of PEX-a drinking water pipes in normal use. Both pipe material and water samples from hot and cold-water pipelines were analyzed. Migration of volatile organic compounds (VOC) from the PEX-a pipes into the drinking water was observed to decrease rapidly during the first months. Deterioration of the PEX-a material was observed to take place due to the wearing down of organic antioxidants added into the PEX-a material during the manufacturing of the pipes. Tert-butyl alcohol (TBA) concentrations were high during the first months after commissioning of use. The stagnation time of the drinking water in contact with the PEX-a material before the actual water sample was taken had a major impact on analyzed migration of organic compounds. Hence, the amount of organic compounds able to migrate from materials into the drinking water will increase when the stagnation time increases. In this study, the water samples were taken after overnight stagnation, whereas in normal use it is advisable to run water properly before drinking it. Instructions will be needed for the average user to avoid harmful health effects. Full article
(This article belongs to the Special Issue Corrosion Management in a Digital World)
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13 pages, 2607 KiB  
Article
Analysis of Environmental Factors Affecting the Atmospheric Corrosion Rate of Low-Alloy Steel Using Random Forest-Based Models
by Luchun Yan, Yupeng Diao and Kewei Gao
Materials 2020, 13(15), 3266; https://0-doi-org.brum.beds.ac.uk/10.3390/ma13153266 - 23 Jul 2020
Cited by 15 | Viewed by 2519
Abstract
As one of the factors (e.g., material properties, surface quality, etc.) influencing the corrosion processes, researchers have always been exploring the role of environmental factors to understand the mechanism of atmospheric corrosion. This study proposes a random forest algorithm-based modeling method that successfully [...] Read more.
As one of the factors (e.g., material properties, surface quality, etc.) influencing the corrosion processes, researchers have always been exploring the role of environmental factors to understand the mechanism of atmospheric corrosion. This study proposes a random forest algorithm-based modeling method that successfully maps both the steel’s chemical composition and environmental factors to the corrosion rate of low-alloy steel under the corresponding environmental conditions. Using the random forest models based on the corrosion data of three different atmospheric environments, the environmental factors were proved to have different importance sequence in determining the environmental corrosivity of open and sheltered exposure test conditions. For each exposure test site, the importance of environmental features to the corrosion rate is also ranked and analyzed. Additionally, the feasibility of the random forest model to predict the corrosion rate of steel samples in the new environment is also demonstrated. The volume and representativeness of the corrosion data in the training data are considered to be the critical factors in determining its prediction performance. The above results prove that machine learning provides a useful tool for the analysis of atmospheric corrosion mechanisms and the evaluation of corrosion resistance. Full article
(This article belongs to the Special Issue Corrosion Management in a Digital World)
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