An advanced fuzzy approach for modeling the yield improvement of making aircraft parts using 3D printing

Abstract

Forecasting the yield is critical for making aircraft parts using three-dimensional (3D) printing. However, the existing methods for the yield forecasting exhibit a common problem: they employ the logarithmic or log-sigmoid value, rather than the original value, of the yield to simplify the computation. To address this problem, an advanced fuzzy approach was proposed in this study. The focus of this study is to investigate the effectiveness of the advanced fuzzy approach for forecasting the yield of a 3D-printed aircraft part. The advanced fuzzy approach derived the direct-solution (DS) versions of the existing fuzzy yield learning models. These DS versions use the original yield value directly, thereby optimizing the forecasting performance. The proposed methodology was applied to the process of making an aircraft part using 3D printing to evaluate its effectiveness. Experimental results revealed significant improvements in the forecasting accuracy of the proposed methodology compared with the aforementioned methods. Furthermore, when the proposed methodology was applied to various fuzzy yield learning models, different improvements were obtained.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Bazaraa MS, Sherali HD, Shetty CM (1993) Nonlinear programming: theory and algorithms. Wiley, New York

    Google Scholar 

  2. 2.

    Chen T (2013) Forecasting the yield of a semiconductor product with a collaborative intelligence approach. Appl Soft Comput 13:1552–1560

    Article  Google Scholar 

  3. 3.

    Chen T (2017) A heterogeneous fuzzy collaborative intelligence approach for forecasting product yield. Appl Soft Comput 57:210–224

    Article  Google Scholar 

  4. 4.

    Chen T, Lin Y-C (2008) A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting. Int J Uncertainty Fuzziness Knowledge Based Syst 16(1):35–58

    Article  Google Scholar 

  5. 5.

    Chen T, Lin Y-C (2017) Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing. Int J Intell Syst 32:394–413

    Article  Google Scholar 

  6. 6.

    Chen T, Wang Y-C (2014) An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting. IEEE Trans Fuzzy Syst 22(1):201–211

    Article  Google Scholar 

  7. 7.

    Chua CK, Wong CH, Yeong WY (2017) Standards, quality control, and measurement sciences in 3D printing and additive manufacturing. Academic Press, London, UK

    Google Scholar 

  8. 8.

    Donoso S, Marin N, Vila MA (2006) Quadratic programming models for fuzzy regression. Proceedings of International Conference on Mathematical and Statistical Modeling in Honor of Enrique Castillo

  9. 9.

    Dua V (2015) Mixed integer polynomial programming. Comput Chem Eng 72:387–394

    Article  Google Scholar 

  10. 10.

    Eberle L, Sugiyama H, Papadokonstantakis S, Graser A, Schmidt R, Hungerbühler K (2016) Data-driven tiered procedure for enhancing yield in drug product manufacturing. Comput Chem Eng 87:82–94

    Article  Google Scholar 

  11. 11.

    Gruber H (1994) Learning and strategic product innovation: theory and evidence for the semiconductor industry. Elsevier Science B. V, The Netherlands

    Google Scholar 

  12. 12.

    Guo L, Qiu J (2018) Combination of cloud manufacturing and 3D printing: research progress and prospect. Int J Adv Manuf Technol 96(5–8):1929–1942

    Article  Google Scholar 

  13. 13.

    Huang R, Riddle M, Graziano D, Warren J, Das S, Nimbalkar S, Cresko J, Masanet E (2016) Energy and emissions saving potential of additive manufacturing: the case of lightweight aircraft components. J Clean Prod 135:1559–1570

    Article  Google Scholar 

  14. 14.

    John YM, Patel R, Mujtaba IM (2017) Modeling and simulation of an industrial riser in fluid catalytic cracking process. Comput Chem Eng 106:730–743

    Article  Google Scholar 

  15. 15.

    Joshi SC, Sheikh AA (2015) 3D printing in aerospace and its long-term sustainability. Virtual Phys Prototyping 10(4):175–185

    Article  Google Scholar 

  16. 16.

    Li N, Li Y, Liu S (2016) Rapid prototyping of continuous carbon fiber reinforced polylactic acid composites by 3D printing. J Mater Process Technol 238:218–225

    Article  Google Scholar 

  17. 17.

    Martin JH, Yahata BD, Hundley JM, Mayer JA, Schaedler TA, Pollock TM (2017) 3D printing of high-strength aluminium alloys. Nature 549(7672):365–369

    Article  Google Scholar 

  18. 18.

    Moon SK, Tan YE, Hwang J, Yoon YJ (2014) Application of 3D printing technology for designing light-weight unmanned aerial vehicle wing structures. Int J Precis Eng Manuf Green Technol 1(3):223–228

    Article  Google Scholar 

  19. 19.

    Murr LE (2016) Frontiers of 3D printing/additive manufacturing: from human organs to aircraft fabrication. J Mater Sci Technol 32(10):987–995

    Article  Google Scholar 

  20. 20.

    Nakagawa Y, Mori KI, Maeno T (2017) 3D printing of carbon fibre-reinforced plastic parts. Int J Adv Manuf Technol 91(5–8):2811–2817

    Article  Google Scholar 

  21. 21.

    Parra AAM, Asmanoglo C, Agar DW (2018) Modeling and optimization of a moving-bed adsorptive reactor for the reverse water-gas shift reaction. Comput Chem Eng 109:203–215

    Article  Google Scholar 

  22. 22.

    Peters G (1994) Fuzzy linear regression with fuzzy intervals. Fuzzy Sets Syst 63:45–55, 1994

    MathSciNet  Article  Google Scholar 

  23. 23.

    Tanaka H, Watada J (1988) Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets Syst 272:275–289

    MathSciNet  Article  Google Scholar 

  24. 24.

    Tavanai H, Taheri SM, Nasiri M (2005) Modeling of color yield in polyethylene terephthalate dyeing with statistical and fuzzy regression. Iran Polym J 14(11):954

    Google Scholar 

  25. 25.

    Thomas DJ (2018) Developing nanocomposite 3D printing filaments for enhanced integrated device fabrication. Int J Adv Manuf Technol 95(9–12):4191–4198

    Article  Google Scholar 

  26. 26.

    Wu HC, Chen TCT (2018) Quality control issues in 3D-printing manufacturing: a review. Rapid Prototyp J 24(3):607–614

    Article  Google Scholar 

  27. 27.

    Yang Y, Tjia R (2010) Process modeling and optimization of batch fractional distillation to increase throughput and yield in manufacture of active pharmaceutical ingredient (API). Comput Chem Eng 34(7):1030–1035

    Article  Google Scholar 

  28. 28.

    Yang Y, Chen Y, Wei Y, Li Y (2016) 3D printing of shape memory polymer for functional part fabrication. Int J Adv Manuf Technol 84(9–12):2079–2095

    Article  Google Scholar 

  29. 29.

    Zimmermann HJ (1991) Fuzzy set theory and its applications. Springer, New York

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yu-Cheng Wang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, T., Wang, Y. An advanced fuzzy approach for modeling the yield improvement of making aircraft parts using 3D printing. Int J Adv Manuf Technol 105, 4085–4095 (2019). https://doi.org/10.1007/s00170-019-03295-z

Download citation

Keywords

  • Yield
  • Forecasting
  • Direct-solution
  • 3D printing
  • Aircraft