Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence

Artificial intelligence (AI) and additive manufacturing (AM) have propelled the next wave of technological innovation by integrating data-driven intelligence with design freedom, thereby enabling adaptive, efficient, and multifunctional systems. This review highlights the transformative role of AI and machine learning (ML) in addressing the key challenges associated with process complexity, parameter tuning, and multifunctional design in AM. Starting with the historical evolution of AI-AM integration, the progression from rule-based modeling to contemporary deep learning, reinforcement learning, and physics-informed frameworks that enable autonomous and self-optimizing manufacturing systems was summarized. Attention is directed toward ML-driven topology and lattice optimization, data-driven methods for predicting process and structural properties, and Multiphysics optimization, demonstrating how AI replaces labor-intensive experimentation with predictive and adaptive models. In parallel, the integration of AI into smart materials (SMs) and 4D printing has been explored, with emphasis on property tuning for piezoelectric, shape-memory, and self-healing systems. The review concludes by highlighting key challenges, including data scarcity, limited interpretability, and the lack of standardized datasets, while pointing toward hybrid physics-informed ML and digital twin approaches for closed-loop and intelligent manufacturing. Collectively, this study provides a comprehensive roadmap illustrating how AI enables AM to evolve from empirical fabrication to autonomous, multifunctional manufacturing paradigms.