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

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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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References

  1. Mohamed OA, Masood SH, Bhowmik JL (2015) Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Adv Manuf 3(1):42–53

    Article  Google Scholar 

  2. Rayegani F, Onwubolu G (2014) Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE). Int J Adv Manuf Technol 73:509–519

    Article  Google Scholar 

  3. Rao RV, Rai DP (2016) Optimization of fused deposition modeling process using teaching-learning-based optimization algorithm. Eng Sci Technol Int J 19(1):587–603

    Article  Google Scholar 

  4. Gurrala PK, Regalla SP (2014) Multi-objective optimisation of strength and volumetric shrinkage of FDM parts: a multi-objective optimization scheme is used to optimize the strength and volumetric shrinkage of FDM parts considering different process parameters. Virtual Phys Prototyping 9(2):127–138

    Article  Google Scholar 

  5. Zhou X, Hsieh S-J, Sun Y (2017) Experimental and numerical investigation of the thermal behaviour of polylactic acid during the fused deposition process. Virtual Phys Prototyping 12(3):221–233

    Article  Google Scholar 

  6. Zhou X, Hsieh S-J, Ting C-C (2018) Modelling and estimation of tensile behaviour of polylactic acid parts manufactured by fused deposition modelling using finite element analysis and knowledge-based library. Virtual and Physical Prototyping 13(2):1–14. https://doi.org/10.1080/17452759.2018.1442681

    Google Scholar 

  7. Koziel S, Leifsson L (2016) Simulation-driven design by knowledge-based response correction techniques. Springer, New York, NY, USA

  8. Bandler JW, Biernacki RM, Chen SH, Grobelny PA, Hemmers RH (1994) Space mapping technique for electromagnetic optimization. IEEE Trans Microwave Theory Tech 42(12):2536–2544

    Article  Google Scholar 

  9. Echeverria D, Lahaye D, Encica L, Lomonova E, Hemker P, Vandenput A (2006) Manifold-mapping optimization applied to linear actuator design. IEEE Trans Magn 42(4):1183–1186

    Article  Google Scholar 

  10. Koziel S, Ogurtsov S (2013) Design optimisation of antennas using electromagnetic simulations and adaptive response correction technique. IET Microwaves Antennas Propag 8(3):180–185

    Article  Google Scholar 

  11. Le Gratiet L, Cannamela C (2015) Cokriging-based sequential design strategies using fast cross-validation techniques for multi-fidelity computer codes. Technometrics 57(3):418–427

    Article  MathSciNet  Google Scholar 

  12. Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, New York, NY, USA

  13. Zio E (2014) Integrated deterministic and probabilistic safety assessment: concepts, challenges, research directions. Nucl Eng Des 280:413–419

    Article  Google Scholar 

  14. Koziel S, Leifsson L (2013) Surrogate-based modeling and optimization. Appl Eng. Springer, New York, NY, USA

  15. Basheer IA, Hajmeer MJJomm (2000) Artificial neural networks: fundamentals, computing, design, and application. 43 (1):3–31

  16. Haykin SS (2009) Neural networks and learning machines/Simon Haykin. Prentice Hall, New York

    Google Scholar 

  17. Ransikarbum K, Ha S, Ma J, Kim N (2017) Multi-objective optimization analysis for part-to-printer assignment in a network of 3D fused deposition modeling. J Manuf Syst 43:35–46

    Article  Google Scholar 

  18. Laboratories MT (2014) Price List (2014). http://www.mtecmechanical.com/_files/pricelist.pdf. Accessed 3/14 2018

  19. Letcher T, Waytashek M (2014) Material property testing of 3D-printed specimen in PLA on an entry-level 3D printer. In: ASME 2014 international mechanical engineering congress and exposition. American Society of Mechanical Engineers, pp 14–22

  20. Labs MP (2007) Moldflow material testing report MAT2238-NatureWorks. PLA Cargill Dow LLC, Minnetonka

    Google Scholar 

  21. ASTM (2014) Standard test method for tensile properties of plastic. ASTM D638. ASTM, U.S.

  22. Partee B, Hollister SJ, Das S (2006) Selective laser sintering process optimization for layered manufacturing of CAPA® 6501 polycaprolactone bone tissue engineering scaffolds. J Manuf Sci Eng 128(2):531–540

    Article  Google Scholar 

  23. Alrbaey K, Wimpenny D, Tosi R, Manning W, Moroz A (2014) On optimization of surface roughness of selective laser melted stainless steel parts: a statistical study. J Mater Eng Perform 23(6):2139–2148

    Article  Google Scholar 

  24. Chockalingam K, Jawahar N, Ramanathan K, Banerjee PS (2006) Optimization of stereolithography process parameters for part strength using design of experiments. Int J Adv Manuf Technol 29(1–2):79–88

    Article  Google Scholar 

  25. Shrestha S, Manogharan GJJ (2017) Optimization of binder jetting using Taguchi method. JOM 69(3):491–497

    Article  Google Scholar 

  26. Torres J, Cole M, Owji A, DeMastry Z, Gordon AP (2016) An approach for mechanical property optimization of fused deposition modeling with polylactic acid via design of experiments. Rapid Prototyping Journal 22 (2):387–404

  27. Peng A, Xiao X, Yue R (2014) Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. The International Journal of Advanced Manufacturing Technology 73 (1-4):87–100

  28. Deng X, Zeng Z, Peng B, Yan S, Ke W (2018) Mechanical Properties Optimization of Poly-Ether-Ether-Ketone via Fused Deposition Modeling. Materials 11 (2):216

  29. Spoerk M, Arbeiter F, Cajner H, Sapkota J, Holzer C (2017) Parametric optimization of intra-and inter-layer strengths in parts produced by extrusion-based additive manufacturing of poly (lactic acid). Journal of Applied Polymer Science 134 (41)

  30. Onwubolu GC, Rayegani F (2014) Characterization and optimization of mechanical properties of ABS parts manufactured by the fused deposition modelling process. International Journal of Manufacturing Engineering 2014

  31. Srivastava M, Maheshwari S, Kundra T, Rathee S (2017) Multi-response optimization of fused deposition modelling process parameters of ABS using response surface Methodology (RSM)-Based desirability analysis. Materials Today: Proceedings 4 (2):1972–1977

  32. Villalpando L, Eiliat H, Urbanic R (2014) An optimization approach for components built by fused deposition modeling with parametric internal structures. Procedia CIRP 17:800–805

  33. Boparai KS, Singh R, Singh H (2016) Modeling and optimization of extrusion process parameters for the development of Nylon6–Al–Al2O3 alternative FDM filament. Progress in Additive Manufacturing 1 (1-2):115–128

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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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Authors and Affiliations

  1. Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA

    Xunfei Zhou & Sheng-Jen Hsieh

  2. Department of Engineering Technology & Industrial Distribution, Texas A&M University, College Station, TX, USA

    Sheng-Jen Hsieh

  3. Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan

    Sheng-Jen Hsieh & Jia-Chang Wang

  4. Additive Manufacturing Center for Mass Customization Production, National Taipei University of Technology, Taipei, Taiwan

    Sheng-Jen Hsieh & Jia-Chang Wang

Authors
  1. Xunfei Zhou
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  2. Sheng-Jen Hsieh
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  3. Jia-Chang Wang
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Correspondence to Sheng-Jen Hsieh.

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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

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