Skip to main content
Log in

Fuzzy collaborative intelligence fuzzy analytic hierarchy process approach for selecting suitable three-dimensional printers

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Three-dimensional (3D) printing presents numerous opportunities for improving rapid prototyping and mass customization. However, the existing methods for assessing the performance of a 3D printer are associated with several problems. To solve these problems, a fuzzy collaborative intelligence fuzzy analytic hierarchy process (FAHP) approach is proposed in this study for assessing the performance of a 3D printer. In the proposed methodology, the alpha-cut operations method is first applied to derive the fuzzy priority of each criterion for each decision maker. Based on the derived priorities, the fuzzy-weighted average is then computed to assess the overall performance of each 3D printer for each decision maker. Thereafter, fuzzy intersection is applied to aggregate the assessment results of the decision makers. Finally, the center-of-gravity method is applied to defuzzify the aggregation result. By using the proposed methodology, the consensus among decision makers can be guaranteed. The proposed fuzzy collaborative intelligence FAHP approach has been applied to a real case of 10 3D printers. The experimental results indicated that the accuracy of the proposed methodology is superior to that of the existing methods, which yielded different ranking results owing to the use of approximation.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Business Performance Management Singapore (2013) AHP – high consistency ratio. https://bpmsg.com/ahp-high-consistency-ratio/. Accessed 10 Sept 2019

  • Çalık A (2020) A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Comput 23:1–13

    Google Scholar 

  • Chen T (2017) Ubiquitous clinic recommendation by predicting a patient’s preferences. Electron Commer Res Appl 23:14–23

    Article  Google Scholar 

  • Chen T (2020) Guaranteed-consensus posterior-aggregation fuzzy analytic hierarchy process method. Neural Comput Appl 32:7057–7068

    Article  Google Scholar 

  • Chen T, Chuang YH (2018) Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation. J Ambient Intell Humaniz Comput 9(2):275–284

    Article  Google Scholar 

  • Chen TCT, Honda K (2019) Linear fuzzy collaborative forecasting methods. Fuzzy collaborative forecasting and clustering. Springer, Cham, pp 9–26

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Csutora R, Buckley JJ (2001) Fuzzy hierarchical analysis: the Lambda-Max method. Fuzzy Sets Syst 120(2):181–195

    Article  MathSciNet  MATH  Google Scholar 

  • Espalin D, Muse DW, MacDonald E, Wicker RB (2014) 3D Printing multifunctionality: structures with electronics. Int J Adv Manuf Technol 72(5–8):963–978

    Article  Google Scholar 

  • Garrett C (2019) How to choose the right 3D printer for you. https://makerhacks.com/choose-3d-printer/. Accessed Aug 2019

  • Groll J, Boland T, Blunk T, Burdick JA, Cho DW, Dalton PD, Derby B, Forgacs G, Li Q, Mironov VA, Moroni L, Nakamura M, Shu W, Takeuchi S, Vozzi G, Woodfield TBF, Xu T, Yoo JJ, Malda J (2016) Biofabrication: reappraising the definition of an evolving field. Biofabrication 8(1):013001

    Article  Google Scholar 

  • Guh YY, Hon CC, Wang KM, Lee ES (1996) Fuzzy weighted average: a max–min paired elimination method. Comput Math Appl 32(8):115–123

    Article  MATH  Google Scholar 

  • Güran A, Uysal M, Ekinci Y, Güran CB (2017) An additive FAHP based sentence score function for text summarization. Inf Technol Control 46(1):53–69

    Google Scholar 

  • Hoffman T (2019) The best 3D printers for 2019. https://www.pcmag.com/roundup/328263/the-best-3d-printers. Accessed Jul 2019

  • Jiang H, Eastman JR (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogr Inf Sci 14(2):173–184

    Article  Google Scholar 

  • Kao C, Liu ST (1999) Competitiveness of manufacturing firms: an application of fuzzy weighted average. IEEE Trans Syst Man Cybern Part A Syst Hum 29(6):661–667

    Article  Google Scholar 

  • Karasan A, Ilbahar E, Kahraman C (2019) A novel pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Comput 23(21):10953–10968

    Article  Google Scholar 

  • Krejčí J, Pavlačka O, Talašová J (2017) A fuzzy extension of analytic hierarchy process based on the constrained fuzzy arithmetic. Fuzzy Optim Decis Mak 16:89–110

    Article  MathSciNet  MATH  Google Scholar 

  • Kwak K, Kim W, Park K (2018) Complementary multiplatforms in the growing innovation ecosystem: evidence from 3D printing technology. Technol Forecast Soc Chang 136:192–207

    Article  Google Scholar 

  • Lin YC, Wang YC, Chen TCT, Lin HF (2019) Evaluating the suitability of a smart technology application for fall detection using a fuzzy collaborative intelligence approach. Mathematics 7(11):1097

    Article  Google Scholar 

  • Liou TS, Wang MJJ (1992) Fuzzy weighted average: an improved algorithm. Fuzzy Sets Syst 49(3):307–315

    Article  MathSciNet  MATH  Google Scholar 

  • Liu F, Mendel JM (2008) Aggregation using the fuzzy weighted average as computed by the Karnik–Mendel algorithms. IEEE Trans Fuzzy Syst 16(1):1–12

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Meshram SG, Alvandi E, Singh VP, Meshram C (2019) Comparison of AHP and fuzzy AHP models for prioritization of watersheds. Soft Comput 23(24):13615–13625

    Article  Google Scholar 

  • Pan NF (2008) Fuzzy AHP approach for selecting the suitable bridge construction method. Autom Constr 17(8):958–965

    Article  Google Scholar 

  • Panda BN, Biswal BB, Deepak BBLV (2014) Integrated AHP and fuzzy TOPSIS approach for the selection of a rapid prototyping process under multi-criteria perspective. In: 5th international and 26th all india manufacturing technology, design and research conference, pp 1–6

  • Pedrycz W (2008) Collaborative architectures of fuzzy modeling. In: IEEE world congress on computational intelligence, pp 117–139

  • Peko I, Bajić D, Veža I (2015) Selection of additive manufacturing process using the AHP method. In: International conference on mechanical technologies and structural materials, pp 119–129

  • Rengier F, Mehndiratta A, Von Tengg-Kobligk H, Zechmann CM, Unterhinninghofen R, Kauczor HU, Giesel FL (2010) 3D printing based on imaging data: review of medical applications. Int J Comput Assist Radiol Surg 5(4):335–341

    Article  Google Scholar 

  • Roberson DA, Espalin D, Wicker RB (2013) 3D printer selection: a decision-making evaluation and ranking model. Virtual Phys Prototyp 8(3):201–212

    Article  Google Scholar 

  • Robinson DK, Lagnau A, Boon WP (2019) Innovation pathways in additive manufacturing: methods for tracing emerging and branching paths from rapid prototyping to alternative applications. Technol Forecast Soc Chang 146:733–750

    Article  Google Scholar 

  • Rong K, Patton D, Chen W (2018) Business models dynamics and business ecosystems in the emerging 3D printing industry. Technol Forecast Soc Chang 134:234–245

    Article  Google Scholar 

  • Roy T, Dutta RK (2019) Integrated fuzzy AHP and fuzzy TOPSIS methods for multi-objective optimization of electro discharge machining process. Soft Comput 23(13):5053–5063

    Article  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  • Shi C, Zhang L, Mai J, Zhao Z (2017) 3D printing process selection model based on triangular intuitionistic fuzzy numbers in cloud manufacturing. Int J Model Simul Sci Comput 8(02):1750028

    Article  Google Scholar 

  • Van Broekhoven E, De Baets B (2006) Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets Syst 157(7):904–918

    Article  MathSciNet  MATH  Google Scholar 

  • Wang YC, Chen T (2018) A direct-solution fuzzy collaborative intelligence approach for yield forecasting in semiconductor manufacturing. Procedia Manuf 17:110–117

    Article  Google Scholar 

  • Wang YC, Chen T (2019) A partial-consensus posterior-aggregation FAHP method—supplier selection problem as an example. Mathematics 7(2):179

    Article  Google Scholar 

  • Wang Z, Porter AL, Wang X, Carley S (2019) An approach to identify emergent topics of technological convergence: a case study for 3D printing. Technol Forecast Soc Chang 146:723–732

    Article  Google Scholar 

  • Wedley WC (1993) Consistency prediction for incomplete AHP matrices. Math Comput Modell 17(4–5):151–161

    Article  MATH  Google Scholar 

  • Wu HC, Chen T, Huang CH (2020) A piecewise linear FGM approach for efficient and accurate FAHP analysis: smart backpack design as an example. Mathematics 8(8):1319

    Article  Google Scholar 

  • Yeh CC, Chen YF (2018) Critical success factors for adoption of 3D printing. Technol Forecast Soc Chang 132:209–216

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toly Chen.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, T., Wu, HC. Fuzzy collaborative intelligence fuzzy analytic hierarchy process approach for selecting suitable three-dimensional printers. Soft Comput 25, 4121–4134 (2021). https://doi.org/10.1007/s00500-020-05436-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05436-z

Keywords

Navigation