The role of Artificial Neural Networks and computer vision in predicting, monitoring, and intelligently controlling the corrosion process

Natural material deterioration, or corrosion, is a large economic burden that accounts for around ۳.۱% of a nation's GNP. It also poses serious concerns to human safety and the environment in critical areas like transportation and infrastructure. For early identification and prompt intervention, traditional corrosion management, which depends on techniques such as visual inspection, frequently falls short. To overcome these constraints, this thorough analysis investigates how deep neural networks and computer vision might be used to anticipate, track, and intelligently manage the corrosion process. These sophisticated AI-powered methods are especially good at analyzing complicated data and spotting minute trends that point to early corrosion indicators, for tasks like forecasting corrosion rate and type in applications like oil pipelines. Artificial Neural Networks (ANNs) are used to detect patterns and create connections between datasets. Additionally, computer vision is used to mimic human perception, allowing robots to identify, evaluate, and categorize damaged areas from visual inputs (pictures or videos), which works well in large-surface or difficult-to-reach locations. The creation of intelligent and preventive corrosion control systems, such as smart self-healing coatings, is made easier by the integration of these techniques. Even though AI has a lot of promise, there are some major obstacles that must be overcome before it can be widely used in industry. These obstacles include the lack of big, standardized, high-quality datasets and the requirement for specialized knowledge. To improve the reliability and usefulness of corrosion analysis tools, future work should concentrate on smoothly fusing AI with conventional corrosion analysis techniques.

کلیدواژه ها:

Artificial Intelligence (AI) ، Corrosion Monitoring ، Artificial Neural Networks (ANNs) ، Computer Vision