Adaptive Path Planning and Process Optimization for FDM of Structural Components with Complex Geometric Features

Abstract

Fused deposition modeling (FDM) faces significant challenges in simultaneously achieving high forming efficiency and surface quality when processing models with complex geometric features, such as multibranch structures and independent holes. This article proposes an adaptive path planning method for such models. First, an adaptive partitioning and hybrid infill strategy based on structural boundary features is developed to reduce the dimensional complexity of cross-sections and optimize local paths. Second, a hybrid intelligent algorithm integrating Particle Swarm Optimization and Simulated Annealing is constructed to perform a two-level, “intra-region and inter-region” decoupled optimization, mitigating premature convergence common in single algorithms when handling large-scale discrete nodes. In addition, the synergistic influence of process parameters on print quality is investigated through transient thermodynamic simulations combined with response surface experiments, and the optimal parameter set is calibrated. Physical printing tests on an S-shaped guide plate and an H-shaped structural model demonstrate that the proposed method achieves an average reduction in surface roughness (Ra) of approximately 14% and 25% relative to traditional contour offset and zigzag paths, respectively. Microscopic defects at feature intersections are reduced, and the dimensional accuracy of the models is improved. This study provides a feasible process optimization framework for efficient and accurate FDM fabrication of models with complex geometric features.