AI Corrosion Assessment Method Developed by IISc & Qatar Researchers
AI Revolutionizes Corrosion Assessment in Industrial equipment with advanced Machine Learning
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New method published in NPJ Materials Degradation offers automated, data-driven analysis of metal degradation, promising enhanced safety and efficiency.
Bengaluru, India & Doha, Qatar – A groundbreaking automated method for assessing corrosion in industrial equipment, developed by researchers at the Indian Institute of Science (iisc) and collaborators, is set to transform how industries manage material degradation. Published in the prestigious Nature Partner Journals (NPJ) Materials Degradation, the study introduces a novel machine learning algorithm capable of analyzing microscope images of corroded metal surfaces to accurately estimate corrosion severity without human intervention.
Tackling a Silent Threat: The Economic and Safety Impact of Corrosion
Corrosion remains a pervasive and costly challenge across vital industries such as power generation, oil, and gas. Professor Phaneendra K Yalavarthy from the Department of Computational and Data Sciences (CDS) at IISc, the senior author of the study, highlighted the critical nature of this issue. “Corrosion poses meaningful economic and safety challenges in industries like power generation and oil/gas,” he stated. “It silently compromises the integrity of these systems, putting lives and livelihoods at risk and burdening society with escalating maintenance costs.”
Ashwin RajKumar, a former postdoctoral researcher at CDS and co-author, echoed these concerns. “[Corrosion] silently compromises the integrity of these systems, putting lives and livelihoods at risk and burdening society with escalating maintenance costs,” he added.
AI-powered Insights: Analyzing Key Corrosion Indicators
The innovative AI-based technique focuses on two crucial indicators of corrosion: the thickness of corrosive deposits on metal surfaces and the porosity (the presence of tiny holes) within these deposits. By feeding microscopy images of metal surfaces into the algorithm, researchers can quantify these characteristics. This quantification allows the AI to infer key features that reveal the extent of corrosion, including the concentration of corrosive chemicals and the acidity of the surroundings beneath the deposits.
“As the rust-like deposits get thicker, there’s more chloride present, and the surface becomes more acidic,” explained Professor Yalavarthy. “We identified specific pH levels that indicate when corrosion is worsening. For example, when the pH drops below 2.8-3,it means that the corrosion has reached a very severe stage.”
Real-World Validation: Steam Generator Tube Corrosion
The researchers rigorously tested their method by examining the under-deposit corrosion (UDC) of steam generator tubes. UDC is a particularly challenging and prevalent form of corrosion found in industrial boilers and other high-temperature environments.
“The algorithm is quite accurate, getting it right about 73% of the time,” Professor Yalavarthy reported. ”It is faster and more consistent than having people manually examine optical microscopy images to determine the severity of corrosion.”
Future Directions and broader implications
While the developed approach shows immense promise, the researchers acknowledge the need for further refinement. Thay caution that the method would require tailoring to each specific case, as morphological features can vary significantly across different corrosion mechanisms.
“The next critical step is to validate the algorithm on much larger and more diverse datasets, capturing the full spectrum of deposit morphologies and operating conditions encountered in industrial practice,” stated RajKumar.
The potential impact of this AI-driven corrosion assessment technique is ample. It offers a pathway to quantitative, data-driven corrosion evaluation across a wide range of industrial facilities and sectors. Integration with existing digital monitoring systems could further accelerate industrial digitalization, yielding significant benefits in safety, operational efficiency, and advancing materials science research.