Abstract
In this research work, a modified fuzzy approach referred to as modified fuzzy logic method (MFLM) is proposed to more conveniently solve intricate decision-making problems under uncertainty and data deficiency. The method resolves major drawbacks like inaccuracy, complexity, and a high volume of computational efforts with the existing approaches for the mentioned problems. Here, MFLM is applied to materials selection for a cryogenic storage tank and a gas turbine blade, which are standard engineering problems used for verification by researchers, and the ranking results are compared with the state-of-the-art techniques in the literature, such as the conventional fuzzy logic method. Spearman’s rank correlation coefficient is used to measure the similarity between the offered rankings. The results show that MFLM, despite its simplicity, provides comparable outputs to the novel existing approaches. The average correlation between MFLM and considered techniques is about 90%, demonstrating a high level of agreement between rankings. MFLM offers SS 301-FH and CMSX-4 materials as the best alternatives for cryogenic storage tank and gas turbine blade examples, respectively. The mentioned materials are confirmed by both industry and other studied techniques and obtained by solving the problem in the fuzzy domain to incorporate uncertainties. Finally, a sensitivity analysis is performed to evaluate the robustness of the proposed method. MFLM indicates to be relatively insensitive to variations in criteria weightings.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Enquiries about data availability should be directed to the authors.
References
Abdel-Basset M, Abdel-Fatah L, Sangaiah AK (2018) Metaheuristic algorithms: a comprehensive review. Elsevier Inc. Doi: https://doi.org/10.1016/B978-0-12-813314-9.00010-4
Alemi-Ardakani M, Milani AS, Yannacopoulos S, Shokouhi G (2016) On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites. Expert Syst Appl 46:426–438. https://doi.org/10.1016/j.eswa.2015.11.003
Amoiralis EI, Georgilakis PS, Gioulekas AT (2006) An artificial neural network for the selection of winding Material in power transformers. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 3955 LNAI, pp 465–468.Doi: https://doi.org/10.1007/11752912_46
Antony PJ, Manujesh PJ, Jnanesh NA (2017) Data mining and machine learning approaches on engineering materials—a review. In: 2016 IEEE international conference recent trends electronics informations on communcations and technology RTEICT 2016—proceedings, pp 69–73. Doi: https://doi.org/10.1109/RTEICT.2016.7807785
Anyfantis K, Stavropoulos P, Foteinopoulos P, Chryssolouris G (2019) An approach for the design of multi-material mechanical components. Proc Inst Mech Eng Part B J Eng Manuf 233(3):960–974. Doi: https://doi.org/10.1177/0954405418763995
Ashby MF (1989) Materials selection in conceptual design. Mater Sci Technol. https://doi.org/10.1179/mst.1989.5.6.517
Ashby MF (2000) Multi-objective optimization in material design and selection. Acta Mater 48:359–369. https://doi.org/10.1016/S1359-6454(99)00304-3
Ashby MF, Cebon D (1993) Materials selection in mechanical design. J Phys IV Colloq 111(C7):3. Doi: https://doi.org/10.1051/jp4:1993701
Babu J, James A, Philip J, Chakraborty S (2017) Application of the grey-based fuzzy logic approach for materials selection. Int J Mater Res 108(9):702–709. https://doi.org/10.3139/146.111538
Čančer V (2012) Criteria weighting by using the 5Ws & H technique. Bsrj 3(2):41–48. https://doi.org/10.2478/v10305-012-0011-3
Celik M, Kahraman C, Cebi S, Er ID (2009) Fuzzy axiomatic design-based performance evaluation model for docking facilities in shipbuilding industry: The case of Turkish shipyards. Expert Syst Appl 36(1):599–615. https://doi.org/10.1016/j.eswa.2007.09.055
Chatterjee P, Athawale VM, Chakraborty S (2009) Selection of materials using compromise ranking and outranking methods. Mater Des 30(10):4043–4053. https://doi.org/10.1016/j.matdes.2009.05.016
Cherian RP, Smith LN, Midha PS (2000) Neural network approach for selection of powder metallurgy materials and process parameters. Artif Intell Eng 14(1):39–44. https://doi.org/10.1016/S0954-1810(99)00026-6
Cicek K, Celik M (2010) Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm. Mater Des 31(4):2129–2133. https://doi.org/10.1016/j.matdes.2009.11.016
Cui X, Wang S, Hu SJ (2008) A method for optimal design of automotive body assembly using multi-material construction. Mater Des 29(2):381–387. https://doi.org/10.1016/j.matdes.2007.01.024
Cui X, Zhang H, Wang S, Zhang L, Ko J (2011) Design of lightweight multi-material automotive bodies using new material performance indices of thin-walled beams for the material selection with crashworthiness consideration. Mater Des 32(2):815–821. https://doi.org/10.1016/j.matdes.2010.07.018
Dargam F et al. (2014) Preface. Lect Notes Bus Inf Process 184 LNBIP:141–156. Doi: https://doi.org/10.1007/978-3-319-11364-7
Dehghan-Manshadi B, Mahmudi H, Abedian A, Mahmudi R (2007) A novel method for materials selection in mechanical design: combination of non-linear normalization and a modified digital logic method. Mater Des 28(1):8–15. https://doi.org/10.1016/j.matdes.2005.06.023
Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137. Doi: https://doi.org/10.1016/j.cie.2019.106040
Dweiri F, Al-Oqla PM (2006) Material selection using analytical hierarchy process. Int J Comput Appl Technol 26(4):182–189. https://doi.org/10.1504/IJCAT.2006.010763
Esawi AM, Ashby MF (1996) Systematic process selection in mechanical design. Int Des Eng Tech Conf Comput Inf Eng Conf. https://doi.org/10.1115/96-DETC/DFM-1403
Farag MM (1997) Materials selection for engineering design. Prentice Hall, Prentice
Fayazbakhsh K, Abedian A, Manshadi BD, Khabbaz RS (2009) Introducing a novel method for materials selection in mechanical design using Z-transformation in statistics for normalization of material properties. Mater Des 30(10):4396–4404. https://doi.org/10.1016/j.matdes.2009.04.004
Gauthier TD (2001) Detecting trends using Spearman’s rank correlation coefficient. Environ Forensics 2(4):359–362. https://doi.org/10.1006/enfo.2001.0061
Golmohammadi D (2011) Neural network application for fuzzy multi-criteria decision making problems. Int J Prod Econ 131(2):490–504. https://doi.org/10.1016/j.ijpe.2011.01.015
Gul M, Celik E, Gumus AT, Guneri AF (2018) A fuzzy logic based PROMETHEE method for material selection problems. Beni-Suef Univ J Basic Appl Sci 7(1):68–79. https://doi.org/10.1016/j.bjbas.2017.07.002
Jahan A, Edwards KL (2013) Weighting of dependent and target-based criteria for optimal decision-making in materials selection process: biomedical applications. Mater Des 49:1000–1008. https://doi.org/10.1016/j.matdes.2013.02.064
Jahan A, Ismail MY, Mustapha F, Sapuan SM (2010) Material selection based on ordinal data. Mater Des 31(7):3180–3187. https://doi.org/10.1016/j.matdes.2010.02.024
Jain V, Sangaiah AK, Sakhuja S, Thoduka N, Aggarwal R (2018) Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput Appl 29(7):555–564. https://doi.org/10.1007/s00521-016-2533-z
Jalal M, Mukhopadhyay AK, Goharzay M (2019) Bat algorithm as a metaheuristic optimization approach in materials and design: optimal design of a new float for different materials. Neural Comput Appl 31(10):6151–6161. https://doi.org/10.1007/s00521-018-3430-4
Kahraman C, Çebi S (2009) A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Expert Syst Appl 36(3) PART 1:4848–4861. Doi: https://doi.org/10.1016/j.eswa.2008.05.041
Kasaei A, Abedian A, Milani AS (2014) An application of quality function deployment method in engineering materials selection. Mater Des 55:912–920. https://doi.org/10.1016/j.matdes.2013.10.061
Khandekar AV, Chakraborty S (2015) Decision-making for materials selection using fuzzy axiomatic design principles. Int J Ind Syst Eng 20(1):117–138. https://doi.org/10.1504/IJISE.2015.069003
Kleemann S, Fröhlich T, Türck E, Vietor T (2017) A methodological approach towards multi-material design of automotive components. Procedia CIRP 60:68–73. https://doi.org/10.1016/j.procir.2017.01.010
Komsiyah S, Wongso R, Pratiwi SW (2019) Applications of the fuzzy ELECTRE method for decision support systems of cement vendor selection. Procedia Comput Sci 157:479–488. https://doi.org/10.1016/j.procs.2019.09.003
Kulak O, Kahraman C (2005) Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Inf Sci (ny) 170(2–4):191–210. https://doi.org/10.1016/j.ins.2004.02.021
Maghsoodi AI, Afezalkotob A, Ari IA, Maghsoodi SI, Hafezalkotob A (2018) Selection of waste lubricant oil regenerative technology using entropy-weighted risk-based fuzzy axiomatic design approach. Inform 29(1):41–74. https://doi.org/10.15388/Informatica.2018.157
Maniya K, Bhatt MG (2010) A selection of material using a novel type decision-making method: Preference selection index method. Mater Des 31(4):1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020
Mayyas A, Omar MA, Hayajneh MT (2016) Eco-material selection using fuzzy TOPSIS method. Int J Sustain Eng 9(5):292–304. https://doi.org/10.1080/19397038.2016.1153168
Mohamadghasemi A, Hadi-Vencheh A, Lotfi FH, Khalilzadeh M (2020) An integrated group FWA-ELECTRE III approach based on interval type-2 fuzzy sets for solving the MCDM problems using limit distance mean. Complex Intell Syst 6(2):355–389. https://doi.org/10.1007/s40747-020-00130-x
Pamučar D, Stević Ž, Sremac S (2018) A new model for determiningweight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM). Symmetry (basel) 10(9):1–22. https://doi.org/10.3390/sym10090393
Patil SK, Kant R (2014) A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert Syst Appl 41(2):679–693. https://doi.org/10.1016/j.eswa.2013.07.093
Qu G, Zhang Z, Qu W, Xu Z (2020) Green supplier selection based on green practices evaluated using fuzzy approaches of TOPSIS and ELECTRE with a case study in a Chinese internet company. Int J Environ Res Public Health 17(9). Doi: https://doi.org/10.3390/ijerph17093268
Rao RV (2006) A material selection model using graph theory and matrix approach. Mater Sci Eng A 431(1–2):248–255. https://doi.org/10.1016/j.msea.2006.06.006
Rao RV (2008) A decision making methodology for material selection using an improved compromise ranking method. Mater Des 29(10):1949–1954. https://doi.org/10.1016/j.matdes.2008.04.019
Rao RV, Patel BK (2010) A subjective and objective integrated multiple attribute decision making method for material selection. Mater Des 31(10):4738–4747. https://doi.org/10.1016/j.matdes.2010.05.014
Rashid T, Ali A, Chu YM (2021) Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. PLoS One 16(2):1–18. Doi: https://doi.org/10.1371/journal.pone.0246738
Rathod MK, Kanzaria HV (2011) A methodological concept for phase change material selection based on multiple criteria decision analysis with and without fuzzy environment. Mater Des 32(6):3578–3585. https://doi.org/10.1016/j.matdes.2011.02.040
Reddy AS, Kumar PR, Raj PA (2019) Entropy-based fuzzy TOPSIS framework for selection of a sustainable building material. Int J Constr Manag 1–12. https://doi.org/10.1080/15623599.2019.1683695
Sadollah A, Bahreininejad A (2011) Optimum gradient material for a functionally graded dental implant using metaheuristic algorithms. J Mech Behav Biomed Mater 4(7):1384–1395. https://doi.org/10.1016/j.jmbbm.2011.05.009
Sakundarini N, Taha Z, Abdul-Rashid SH, Ghazila RAR (2013) Optimal multi-material selection for lightweight design of automotive body assembly incorporating recyclability. Mater Des 50:846–857. https://doi.org/10.1016/j.matdes.2013.03.085
Sarfaraz Khabbaz R, Dehghan Manshadi B, Abedian A, Mahmudi R (2009) A simplified fuzzy logic approach for materials selection in mechanical engineering design. Mater Des 30(3):687–697. Doi: https://doi.org/10.1016/j.matdes.2008.05.026
Seo M, Jeong S (2010) Analysis of self-pressurization phenomenon of cryogenic fluid storage tank with thermal diffusion model. Cryogenics (guildf) 50(9):549–555. https://doi.org/10.1016/j.cryogenics.2010.02.021
Shanian A, Savadogo O (2006) TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. J Power Sources 159(2):1095–1104. https://doi.org/10.1016/j.jpowsour.2005.12.092
Shojaie AA, Babaie S, Sayah E, Mohammaditabar D (2018) Analysis and Prioritization of green health suppliers using fuzzy ELECTRE method with a case study. Glob J Flex Syst Manag 19(1):39–52. https://doi.org/10.1007/s40171-017-0168-2
Wang YJ, Lee HS (2007) Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Comput Math with Appl 53(11):1762–1772. https://doi.org/10.1016/j.camwa.2006.08.037
Zadeh LA () Fuzzy logic (2013) Comput Complex Theory Tech Appl 9781461418:1177–1200. Doi: https://doi.org/10.1007/978-1-4614-1800-9_73
Zhang J, Huang Y, Wang Y, Ma G (2020) Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms. Constr Build Mater 253:119208. https://doi.org/10.1016/j.conbuildmat.2020.119208
Zhang K, Zhan J, Yao Y (2019) TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection. Inf Sci (ny) 502:297–329. https://doi.org/10.1016/j.ins.2019.06.043
Zhou CC, Yin GF, Hu XB (2009) Multi-objective optimization of material selection for sustainable products: artificial neural networks and genetic algorithm approach. Mater Des 30(4):1209–1215. https://doi.org/10.1016/j.matdes.2008.06.006
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
The authors agree to submit the manuscript entitled “A novel decision-making methodology for materials selection under uncertainty: Modified fuzzy logic method” to the journal of Soft Computing.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sharifan, M., Abedian, A. & Razaghian, P. A novel decision-making methodology for materials selection under uncertainty: modified fuzzy logic method. Soft Comput 26, 12093–12114 (2022). https://doi.org/10.1007/s00500-022-07444-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07444-7