Abstract
Hard-magnetic materials are ubiquitous and are used in a myriad of applications, including but not limited to computers, green energy technologies, and defense systems. Over the years, a variety of hard-magnetic materials were developed to cater to the immanent technological demands. In the recent past, materials informatics has been an essential component of materials discovery, design, and development. We present a methodology that combines various multiple attribute decision-making methods, hierarchical clustering, and principal component analysis for data-driven hard-magnetic material selection . Shannon’s entropy model evaluated the relative weights of multiple properties followed by the ranking of the hard-magnetic materials by the various multiple attribute decision-making methods. Akin to Ashby charts, two-dimensional plots were developed to provide a visual presentation, based on the decision-making models, clustering, and component analysis followed by the assessment of the predictive capability of the data-driven model.
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Pecharsky VK, Gschneidner KA Jr (1999) Magnetocaloric effect and magnetic refrigeration. J Magn Magn Mater 200:44–56
Pyrhönen J, Nerg J, Kurronen P, Puranen J, Haavisto M (2010) Permanent magnet technology in wind power generators. https://doi.org/10.1109/icelmach.2010.5608312
Buschow KHJ (1998) Permanent-magnet materials and their applications. Trans Tech Publications, Zuerich-Uetikon
Ashby MF (2005) Materials selection in mechanical design, 3rd edn. Elsevier, Butterworth-Heinemann, ISBN 0-7506-6168-2
Zavadskas E, Turskis Z, Kildiene S (2014) State of art surveys of overviews on MCDM/MADM methods. Technol Econ Develop Econ 20:165–179. https://doi.org/10.3846/20294913.2014.892037
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 159:1095–1104. https://doi.org/10.1016/j.jpowsour.2005.12.092 (Elsevier B.V.)
Chan JWK, Tong TKL (2007) Multi-criteria material selections and end-of-life product strategy: grey relational analysis approach. Mater Des 28:1539–1546. https://doi.org/10.1016/j.matdes.2006.02.016 (Elsevier Ltd.)
Ranjan R, Chakraborty S (2015) Performance evaluation of Indian technical institutions using PROMETHEE-GAIA Approach. Inf Educ 14(1):103–125. http://dx.doi.org/10.15388/infedu.2015.07 (Vilnius University)
Jahan A, Mustapha F, Ismail MY, Sapuan SM (2011) A comprehensive VIKOR method for material selection. Mater Des 32(3):1215–1221. https://doi.org/10.1016/j.matdes.2010.10.015
Cardarelli F (2000) Materials handbook. Springer, New York, pp 513–515
Triantaphyllou E, Shu B, Sanchez SN, Ray T (1998) Multi-criteria decision making: an operations research approach. In: Webster JG (ed) Encyclopedia of electrical and electronics engineering, vol 15. Wiley, New York, pp 175–186
Budiharjo, Windarto AP, Muhammad A (2017) Comparison of weighted sum model and multi attribute decision making weighted product methods in selecting the best elementary school in Indonesia. Int J Software Eng Appl 11(4):69–90. http://dx.doi.org/10.14257/ijseia.2017.11.4.06
Yazdani M, Payam AF (2015) A comparative study on material selection of microelectromechanical systems electrostatic actuators using Ashby, VIKOR and TOPSIS. Mater Des 65:328–334. http://dx.doi.org/10.1016/j.matdes.2014.09.004 (Elsevier Ltd.)
Rao RV (2012) Weighed Euclidean distance based approach as a multiple attribute decision making for plant layout design selection. Int J Ind Eng Comput 3(3):365–382
Madic M, Radovanovic M, Manic M (2016) Application of the ROV method for the selection of cutting fluids. Decis Sci Lett 5:245–254. https://doi.org/10.5267/j.dsl.2015.12.001 (Growing Science Ltd.)
Chatterjee P, Chakraborty S (2013) Gear material selection using complex proportional assessment and additive ratio assessment-based approaches: a comparative study. Int J Mater Sci Eng 1(2). https://doi.org/10.12720/ijmse.1.2.104-111 (Engineering and Technology Publishing)
Vommi VB, Kakollu SR (2017) A simple approach to multiple attribute decision making using loss functions. J Ind Eng Int 13:107–116. https://doi.org/10.1007/s40092-016-0174-6
Vidal R, Ma Y, Sastry SS (2016) Generalized principal component analysis. Interdisc Appl Math 40. https://doi.org/10.1007/978-0-387-87811-9_2
Xu R, Wunsch DC (2009) Clustering. II Copyright © 2009 Institute of Electrical and Electronics Engineers
Acknowledgements
The authors would like to thank the College of Engineering and Computer Science and the Institute of Advanced Vehicle Systems at the University of Michigan in Dearborn for the financial and infrastructural support to conduct the investigation.
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Pinnam, S., Jayaraman, T.V. (2020). Data-Driven Hard-Magnetic Material Selection for AC Applications by Multiple Attribute Decision Making. In: TMS 2020 149th Annual Meeting & Exhibition Supplemental Proceedings. The Minerals, Metals & Materials Series. Springer, Cham. https://doi.org/10.1007/978-3-030-36296-6_149
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DOI: https://doi.org/10.1007/978-3-030-36296-6_149
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