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Data-Driven Hard-Magnetic Material Selection for AC Applications by Multiple Attribute Decision Making

  • Sunny Pinnam
  • Tanjore V. JayaramanEmail author
Conference paper
  • 591 Downloads
Part of the The Minerals, Metals & Materials Series book series (MMMS)

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.

Keywords

Material selection Multiple attribute decision making Hard-magnetic materials 

Notes

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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Copyright information

© The Minerals, Metals & Materials Society 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of MichiganDearbornUSA

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