Research Progress and Challenges in Shape‐Performance Control Technologies for Large‐Scale <scp>3D</scp> Printing: From Process Characteristics to Slicing Algorithms

ABSTRACT Large‐scale 3D printing technology has emerged as an important driver of the digital, green, and personalized paradigm shifting within industrial manufacturing, progressing from shape formation to shape–performance integration in manufacturing. This study provides a systematic review of large‐scale 3D printing technologies from a shape–performance perspective. The discussion focuses on three representative system architectures, including centralized, distributed, and hybrid strategies. In particular, the review emphasizes recent advances in slicing algorithms and their role in shape–performance control across different printing systems. Centralized 3D printing exhibits a high degree of precision and material property control, yet its system complexity and cost impose limitations on its scalability. Conversely, distributed 3D printing enhances manufacturing flexibility and adaptability through multi‐machine coordination but faces challenges in collaborative control and ensuring quality consistency. Hybrid strategies leverage the strengths of both approaches, thereby establishing a novel fabrication paradigm that achieves a balance between precision and scalability. With regard to the algorithms employed for slicing, centralized systems prioritize the generation of efficient, high‐precision paths and the compensation of errors. In contrast, distributed systems emphasize the coordination of multiple machines and the dynamic scheduling of tasks. Moreover, hybrid systems achieve global optimization through the allocation of tasks and the fusion of paths. Slicing algorithms play a critical role in enabling shape‐performance integration by directly governing material deposition paths, geometric accuracy, and functional property distribution. A comprehensive review of the extant research indicates that future large‐scale 3D printing is expected to develop along several directions: synergistic shape‐performance control, intelligent algorithm‐driven processes, and extreme environment adaptability. This convergence will provide fundamental support for intelligent manufacturing in aerospace, energy equipment, space architecture, and other fields.