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.
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
Chen T (2017) Ubiquitous clinic recommendation by predicting a patient’s preferences. Electron Commer Res Appl 23:14–23
Chen T (2020) Guaranteed-consensus posterior-aggregation fuzzy analytic hierarchy process method. Neural Comput Appl 32:7057–7068
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
Chen TCT, Honda K (2019) Linear fuzzy collaborative forecasting methods. Fuzzy collaborative forecasting and clustering. Springer, Cham, pp 9–26
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
Csutora R, Buckley JJ (2001) Fuzzy hierarchical analysis: the Lambda-Max method. Fuzzy Sets Syst 120(2):181–195
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
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
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
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
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
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
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
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
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
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
Liou TS, Wang MJJ (1992) Fuzzy weighted average: an improved algorithm. Fuzzy Sets Syst 49(3):307–315
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
Martin JH, Yahata BD, Hundley JM, Mayer JA, Schaedler TA, Pollock TM (2017) 3D printing of high-strength aluminium alloys. Nature 549(7672):365
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
Pan NF (2008) Fuzzy AHP approach for selecting the suitable bridge construction method. Autom Constr 17(8):958–965
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
Roberson DA, Espalin D, Wicker RB (2013) 3D printer selection: a decision-making evaluation and ranking model. Virtual Phys Prototyp 8(3):201–212
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
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
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
Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York
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
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
Wang YC, Chen T (2018) A direct-solution fuzzy collaborative intelligence approach for yield forecasting in semiconductor manufacturing. Procedia Manuf 17:110–117
Wang YC, Chen T (2019) A partial-consensus posterior-aggregation FAHP method—supplier selection problem as an example. Mathematics 7(2):179
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
Wedley WC (1993) Consistency prediction for incomplete AHP matrices. Math Comput Modell 17(4–5):151–161
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
Yeh CC, Chen YF (2018) Critical success factors for adoption of 3D printing. Technol Forecast Soc Chang 132:209–216
Author information
Authors and Affiliations
Corresponding author
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
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05436-z