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Accelerating extrusion-based additive manufacturing optimization processes with surrogate-based multi-fidelity models

  • Xunfei Zhou
  • Sheng-Jen HsiehEmail author
  • Jia-Chang Wang
ORIGINAL ARTICLE
  • 42 Downloads

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.

Keywords

Optimization Additive manufacturing Surrogate model Neural network 

Notes

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.

Supplementary material

170_2019_3813_MOESM1_ESM.docx (397 kb)
ESM 1 (DOCX 396 kb)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Engineering Technology & Industrial DistributionTexas A&M UniversityCollege StationUSA
  3. 3.Department of Mechanical EngineeringNational Taipei University of TechnologyTaipeiTaiwan
  4. 4.Additive Manufacturing Center for Mass Customization ProductionNational Taipei University of TechnologyTaipeiTaiwan

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