A comprehensive review of process planning and trajectory optimization in arc-based directed energy deposition

Directed energy deposition (DED) represents a pivotal advancement in intelligent manufacturing, facilitating efficient near-net shape metal part production, particularly suited for aerospace and defense applications demanding high precision. Arc-based DED relies on meticulous process and trajectory planning, where AI-driven manufacturing systems optimize paths and parameters to surmount intricate physical phenomena like material melting and heat transfer. AI methodologies such as deep learning and big data analytics offer promising solutions. The exclusive process planning software for DED-Arc (EPPS−DED) broadens the technology's application domains. This paper comprehensively outlines core algorithms pertinent to EPPS-DED and essential process strategies for meticulous process planning, providing insights for software development and part quality enhancement. Key topics covered include 3D model slicing, path planning, printing efficiency, and scanning order for 2D contours with diverse geometries, alongside strategies for inclined structures and lattices. Moreover, it discusses the latest AI applications in process planning. The paper concludes with current progress and future outlooks aimed at refining the accuracy and performance of DED-fabricated components.