AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications

Area of Science:

• Materials Science
• Chemical Engineering
• Computer Science

Background:

• Polymeric coatings are crucial for protecting structural materials from environmental degradation.
• Traditional methods for coating development are often time-consuming and resource-intensive.
• Advances in artificial intelligence (AI) offer new paradigms for materials engineering.

Purpose of the Study:

• To provide a comprehensive review of AI-driven strategies for polymeric coatings in structural applications.
• To highlight the integration of AI techniques like machine learning and computer vision into coating design.
• To discuss the potential of AI in developing advanced coatings with enhanced properties.

Main Methods:

• Review of recent literature on AI applications in polymeric coatings.
• Analysis of AI-assisted material selection, performance prediction, and intelligent evaluation systems.
• Examination of case studies on self-healing, smart, and sustainable coatings.

Main Results:

• AI enables data-driven discovery of novel polymeric coating materials.
• Predictive models enhance understanding of coating degradation mechanisms (corrosion, fatigue).
• Intelligent evaluation systems improve inspection efficiency and accuracy.

Conclusions:

• AI integration is transforming the design and application of polymeric coatings.
• Future research should focus on explainable AI, autonomous systems, and circular economy principles for coating development.
• Addressing challenges like data scarcity and model interpretability is key for widespread AI adoption.