Artificial Intelligence in Wire Arc Additive Manufacturing: A Systematic Review and Patent Landscape Analysis

ABSTRACT Wire arc additive manufacturing (WAAM) has emerged as a cost‐effective and scalable approach for producing large and complex metallic components. However, its industrial deployment faces persistent challenges in process stability, real‐time quality assurance, and data transparency. This review provides a comprehensive analysis of the individual applications of artificial intelligence (AI) and Blockchain technologies in WAAM, emphasizing their distinct contributions and future potential for convergence. AI techniques such as artificial neural networks (ANN), support vector machines (SVM), deep learning (DL), adaptive neuro‐fuzzy inference systems (ANFIS), and reinforcement learning (RL) are critically examined for their roles in process modeling, defect prediction, adaptive control, and toolpath optimization. Concurrently, Blockchain's decentralized and tamper‐proof framework is analyzed for its capacity to enhance data integrity, certification, traceability, and supply chain transparency within WAAM ecosystems. A patent landscape analysis identifies AI‐related and blockchain‐related filings, reflecting the rapid global expansion of intelligent and secure additive manufacturing research. Despite these advancements, current studies predominantly address these technologies independently, with limited integration between intelligent decision‐making and secure data management. The review highlights key research gaps, methodological constraints, and offers actionable directions toward developing hybrid AI–Blockchain frameworks tailored for autonomous, traceable, and industry‐ready WAAM systems.