Balanced Optimization of Dimensional Accuracy and Printing Efficiency in FDM Based on Data-Driven Modeling

Abstract

Additive manufacturing (AM), particularly fused deposition modeling (FDM), has emerged as a transformative technology in modern manufacturing processes. The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance. To achieve sustainable manufacturing in FDM, it is necessary to optimize the print quality and time efficiency concurrently. However, owing to the complex interactions of printing parameters, achieving a balanced optimization of both remains challenging. This study examines four key factors affecting dimensional accuracy and print time: printing speed, layer thickness, nozzle temperature, and bed temperature. Fifty parameter sets were generated using enhanced Latin hypercube sampling. A whale optimization algorithm (WOA)-enhanced support vector regression (SVR) model was developed to predict dimensional errors and print time effectively, with non-dominated sorting genetic algorithm III (NSGA-III) utilized for multi-objective optimization. The technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied to select a balanced solution from the Pareto front. In experimental validation, the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency. This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM. In addition, the printing of a specific prosthetic component was used as a case study, highlighting the high demands on both dimensional precision and printing efficiency. The optimized process parameters required significantly less printing time, while satisfying the dimensional accuracy requirements. This study provides valuable insights for achieving sustainable AM using FDM.

Topics & Concepts