Evaluation of energy consumption dynamics and concurrent optimization of quality in fused deposition modeling processes

With increasing pressure for sustainable manufacturing, reducing energy consumption while maintaining part quality has become a key challenge in fused deposition modeling (FDM). However, the collaborative optimization of process parameters and multi-part fabrication strategies remains insufficiently explored. In this study, four key process variables, namely nozzle temperature, layer thickness, infill density, and printing speed, were investigated. Regression models for dimensional error, surface roughness, and tensile strength were established, together with an energy-consumption prediction model. Based on these models, an improved multi-objective artificial hummingbird algorithm was employed to achieve collaborative optimization of quality and energy consumption for single-part fabrication. The optimal process parameters were identified as 220 °C nozzle temperature, 0.2 mm layer thickness, 38% infill density, and 58 mm s −1 printing speed. Furthermore, a dynamic energy-efficiency evaluation framework was developed for multi-part FDM fabrication. Considering the spatial arrangement and build-orientation strategies of parts with different geometric features, multiple fabrication plans were generated and assessed. The results show that the optimized strategy increased area utilization by 5.8% compared with the conventional approach. In addition, for target part A under plan-4, the energy-efficiency rating reached Grade A, while energy consumption and printing time were reduced by 11.2% and 4.8%, respectively, relative to plan-1. These results demonstrate that the proposed framework can effectively improve both quality and energy performance, and provides a practical approach for low-carbon and efficient FDM manufacturing.