Hybrid Additive Manufacturing and AI for Electromechanical Component Optimization: A Review

Abstract

Hybrid Additive Manufacturing (HAM) has emerged as an effective approach to overcome the limitations of conventional metal additive manufacturing by combining layer-by-layer deposition with the precision of CNC machining. In parallel, artificial intelligence (AI) has enabled significant advances in process monitoring, defect prediction, and adaptive control. This study presents a systematized review of 52 research articles published between 2015 and 2025 to analyze the capabilities, limitations, and applications of HAM and AI in the design and fabrication of electromechanical components. The findings indicate that medium and high integration levels in hybrid systems improve dimensional accuracy, surface quality, and mechanical performance. AI-based methods further enhance process reliability by enabling parameter optimization, early defect detection, and improved repeatability. Despite these advances, challenges remain regarding standardization, limited industrial adoption, and the lack of comparative studies between AM, HAM, and CNC machining. This review provides a conceptual framework that supports future research and guides the implementation of HAM and AI in the development of advanced electromechanical components.