A Review on AI-Based Process Monitoring and Quality Control for Additive Cladding Technologies in Industrial Component Remanufacturing

With the transition toward global carbon neutrality and a circular economy, remanufacturing technologies for high-value industrial components have gained significant attention. Additive cladding processes such as laser cladding, directed energy deposition (DED), wire arc additive manufacturing (WAAM), and plasma transferred arc (PTA) are key solutions for localized structural restoration. However, dynamic melt pool instability and complex thermal histories often induce defects like porosity and microcracks, degrading product reliability. Recently, artificial intelligence (AI)-assisted multi-sensor monitoring and closed-loop control systems have been introduced to enhance process stability and repeatability. This review comprehensively categorizes additive cladding processes for remanufacturing and explores recent advancements in AI-driven sensing, deep-learning-based defect prediction, adaptive feedback control, and digital twin integration. Finally, critical technical challenges and future perspectives for industrial deployment are discussed.