1
Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Ash Sharqia Governorate, Egypt
(aabdelmounem@zu.edu.eg)
2
Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Ash Sharqia Governorate, Egypt
(mahsabe@yahoo.com)
Abstract :
Choice of materials is difficult since it requires considering several factors, assigning relative importance to those criteria, and ultimately choosing the most relevant criterion. Finally, it is important to set the criteria in a way that takes into account both the known material attributes and the needs of the application. Therefore, MCDM techniques may be used to the process of selecting materials. A possibility of incomplete dilemmas arises due to the decision maker's language inputs. Therefore, the inputs might be supplied as fuzzy numbers in order to circumvent the issue. Considering that a neutrosophic set is a metaphor to overcome uncertainty of human perceptions. To assess this recruitment process in a neutrosophic setting, this paper employs a neutrosophic-based version of the MCDM tool TOPSIS to determine which alternative materials should be used for the center console of an electric car.
Keywords :
Neutrosophic sets; MCDM; Automotive Instrument; Material Selection;
References :
[1] M. F. Ashby and K. Johnson, Materials and design: the art and science of material selection in product design. Butterworth-Heinemann, 2013.
[2] P. Chatterjee and S. Chakraborty, “Material selection using preferential ranking methods,” Materials & Design, vol. 35, pp. 384–393, 2012.
[3] N. Mahato, A. Banerjee, A. Gupta, S. Omar, and K. Balani, “Progress in material selection for solid oxide fuel cell technology: A review,” Progress in Materials Science, vol. 72, pp. 141–337, 2015.
[4] D.-H. Jee and K.-J. Kang, “A method for optimal material selection aided with decision making theory,” Materials & Design, vol. 21, no. 3, pp. 199–206, 2000.
[5] S. Balasubramaniam, A. Mohanty, S. K. Balasingam, S. J. Kim, and A. Ramadoss, “Comprehensive insight into the mechanism, material selection and performance evaluation of supercapatteries,” Nano-Micro Letters, vol. 12, pp. 1–46, 2020.
[6] D. Rai, G. K. Jha, P. Chatterjee, and S. Chakraborty, “Material selection in manufacturing environment using compromise ranking and regret theory-based compromise ranking methods: A comparative study,” Universal Journal of Materials Science, vol. 1, no. 2, pp. 69–77, 2013.
[7] A. Jahan, F. Mustapha, M. Y. Ismail, S. M. Sapuan, and M. Bahraminasab, “A comprehensive VIKOR method for material selection,” Materials & Design, vol. 32, no. 3, pp. 1215–1221, 2011.
[8] R. J. Girubha and S. Vinodh, “Application of fuzzy VIKOR and environmental impact analysis for material selection of an automotive component,” Materials & Design, vol. 37, pp. 478–486, 2012.
[9] M. Gul, E. Celik, A. T. Gumus, and A. F. Guneri, “A fuzzy logic based PROMETHEE method for material selection problems,” Beni-Suef University Journal of Basic and Applied Sciences, vol. 7, no. 1, pp. 68–79, 2018.
[10] S. R. Maity and S. Chakraborty, “Tool steel material selection using PROMETHEE II method,” The International Journal of Advanced Manufacturing Technology, vol. 78, pp. 1537–1547, 2015.
[11] R. V. Rao, “A decision making methodology for material selection using an improved compromise ranking method,” Materials & Design, vol. 29, no. 10, pp. 1949–1954, 2008.
[12] I. Ribeiro, P. Peças, A. Silva, and E. Henriques, “Life cycle engineering methodology applied to material selection, a fender case study,” Journal of Cleaner Production, vol. 16, no. 17, pp. 1887–1899, 2008.
[13] M.-C. Choi, Y. Kim, and C.-S. Ha, “Polymers for flexible displays: From material selection to device applications,” Progress in Polymer Science, vol. 33, no. 6, pp. 581–630, 2008.
[14] F. Dweiri and F. M. Al-Oqla, “Material selection using analytical hierarchy process,” International journal of computer applications in technology, vol. 26, no. 4, pp. 182–189, 2006.
[15] R. V. Rao, “A material selection model using graph theory and matrix approach,” Materials Science and Engineering: A, vol. 431, no. 1–2, pp. 248–255, 2006.
[16] I. Emovon and O. S. Oghenenyerovwho, “Application of MCDM method in material selection for optimal design: A review,” Results in Materials, vol. 7, p. 100115, 2020.
[17] M. F. Ashby and D. CEBON, “Materials selection in mechanical design,” Le Journal de Physique IV, vol. 3, no. C7, pp. C7-1, 1993.
[18] A. Jahan, F. Mustapha, S. M. Sapuan, M. Y. Ismail, and M. Bahraminasab, “A framework for weighting of criteria in ranking stage of material selection process,” The International Journal of Advanced Manufacturing Technology, vol. 58, pp. 411–420, 2012.
[19] Y. C. Wu, J. P. Qian, and J. N. Yu, “The fusion-driven hybrid system and its material selection,” Journal of Nuclear Materials, vol. 307, pp. 1629–1636, 2002.
[20] M. Ashby and K. Johnson, “The art of materials selection,” Materials today, vol. 6, no. 12, pp. 24–35, 2003.
[21] M. İpek, İ. H. Selvi, F. Findik, O. Torkul, and I. H. Cedimoğlu, “An expert system based material selection approach to manufacturing,” Materials & Design, vol. 47, pp. 331–340, 2013.
[22] A. Jahan, M. Y. Ismail, F. Mustapha, and S. M. Sapuan, “Material selection based on ordinal data,” Materials & Design, vol. 31, no. 7, pp. 3180–3187, 2010.
[23] L. Anojkumar, M. Ilangkumaran, and V. Sasirekha, “Comparative analysis of MCDM methods for pipe material selection in sugar industry,” Expert systems with applications, vol. 41, no. 6, pp. 2964–2980, 2014.
[24] A. and Shanian and O. Savadogo, “A material selection model based on the concept of multiple attribute decision making,” Materials & Design, vol. 27, no. 4, pp. 329–337, 2006.
[25] M. A. Maleque1, S. Dyuti, and M. M. Rahman, “Material selection method in design of automotive brake disc,” in Proceedings of the world congress on engineering, 2010, vol. 3.
[26] H. Zhang, J. Wang, and X. Chen, “An outranking approach for multi-criteria decision-making problems with interval-valued neutrosophic sets,” Neural Computing and Applications, vol. 27, no. 3, pp. 615–627, 2016.
[27] I. Deli, “Interval-valued neutrosophic soft sets and its decision making,” International Journal of Machine Learning and Cybernetics, vol. 8, no. 2, pp. 665–676, 2017.
[28] J. Ye and S. Du, “Some distances, similarity and entropy measures for interval-valued neutrosophic sets and their relationship,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 2, pp. 347–355, 2019.
[29] S. Broumi and F. Smarandache, “Cosine similarity measure of interval valued neutrosophic sets,” Infinite Study, 2014.
| Style | # |
|---|---|
| MLA | Ahmed Abdelmonem, Mahmoud M.Ismail. "An Integrated Neutrosophic MCDM Methodology for Material Selection." Journal of Neutrosophic and Fuzzy Systems, Vol. 1, No. 1, 2021 ,PP. 69-79 (Doi : https://doi.org/10.54216/JNFS.010108) |
| APA | Ahmed Abdelmonem, Mahmoud M.Ismail. (2021). An Integrated Neutrosophic MCDM Methodology for Material Selection. Journal of Journal of Neutrosophic and Fuzzy Systems, 1 ( 1 ), 69-79 (Doi : https://doi.org/10.54216/JNFS.010108) |
| Chicago | Ahmed Abdelmonem, Mahmoud M.Ismail. "An Integrated Neutrosophic MCDM Methodology for Material Selection." Journal of Journal of Neutrosophic and Fuzzy Systems, 1 no. 1 (2021): 69-79 (Doi : https://doi.org/10.54216/JNFS.010108) |
| Harvard | Ahmed Abdelmonem, Mahmoud M.Ismail. (2021). An Integrated Neutrosophic MCDM Methodology for Material Selection. Journal of Journal of Neutrosophic and Fuzzy Systems, 1 ( 1 ), 69-79 (Doi : https://doi.org/10.54216/JNFS.010108) |
| Vancouver | Ahmed Abdelmonem, Mahmoud M.Ismail. An Integrated Neutrosophic MCDM Methodology for Material Selection. Journal of Journal of Neutrosophic and Fuzzy Systems, (2021); 1 ( 1 ): 69-79 (Doi : https://doi.org/10.54216/JNFS.010108) |
| IEEE | Ahmed Abdelmonem, Mahmoud M.Ismail, An Integrated Neutrosophic MCDM Methodology for Material Selection, Journal of Journal of Neutrosophic and Fuzzy Systems, Vol. 1 , No. 1 , (2021) : 69-79 (Doi : https://doi.org/10.54216/JNFS.010108) |