Abstract
Fused deposition modeling (FDM) is by far the most common extrusion-based additive manufacturing technology. Affordability and feasibility promote the development of FDM technologies; nevertheless, product quality problems hinder the future growth of this advanced manufacturing technique. Optimizing the parameters of the manufacturing process can improve product quality. However, traditional optimization techniques require extensive experiments to determine the optimum condition. In this study, a low-fidelity numerical simulation predictive model and a high-fidelity experimental model were combined to iteratively optimize the additive manufacturing process. Although the proposed method was initially targeted for extrusion-based additive manufacturing processes, it was also verified with various practical additive manufacturing optimization problems. It is demonstrated that the proposed optimization algorithm outperformed traditional optimization algorithms by reducing the optimization cost by at least 14.6%. Moreover, the optimizer demonstrated superb noise tolerance ability.
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Acknowledgments
The authors acknowledge Texas A&M High Performance Research Computing for providing software support for our numerical simulation. We would also like to thank Dr. Terry Creasy and Dr. Alex (Gwo-Ping) Fang for using tensile testing machines of their labs.
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Zhou, X., Hsieh, SJ. & Wang, JC. Accelerating extrusion-based additive manufacturing optimization processes with surrogate-based multi-fidelity models. Int J Adv Manuf Technol 103, 4071–4083 (2019). https://doi.org/10.1007/s00170-019-03813-z
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DOI: https://doi.org/10.1007/s00170-019-03813-z