Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data

Rajesh Jha, George S. Dulikravich, Nirupam Chakraborti, Min Fan, Justin Schwartz, Carl C. Koch, Marcelo J. Colaco, Carlo Poloni, Igor N. Egorov

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

A multi-dimensional random number generation algorithm was used to distribute chemical concentrations of each of the alloying elements in the candidate alloys as uniformly as possible while maintaining the prescribed bounds on the minimum and maximum allowable values for the concentration of each of the alloying elements. The generated candidate alloy compositions were then examined for phase equilibria and associated magnetic properties using a thermodynamic database in the desired temperature range. These initial candidate alloys were manufactured, synthesized and tested for desired properties. Then, the experimentally obtained values of the properties were fitted with a multi-dimensional response surface. The desired properties were treated as objectives and were extremized simultaneously by utilizing a multi-objective optimization algorithm that optimized the concentrations of each of the alloying elements. This task was also performed by another conceptually different response surface and optimization algorithm for double-checking the results. A few of the best predicted Pareto optimal alloy compositions were then manufactured, synthesized and tested to evaluate their macroscopic properties. Several of these Pareto optimized alloys outperformed most of the candidate alloys on most of the objectives. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of the alloys. A sensitivity analysis of each of the alloying elements was also performed to determine which of the alloying elements contributes the least to the desired macroscopic properties of the alloy. These elements can then be replaced with other candidate alloying elements such as not-so-rare earth elements.

Original languageEnglish (US)
Pages (from-to)454-467
Number of pages14
JournalJournal of Alloys and Compounds
Volume682
DOIs
StatePublished - Oct 15 2016

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Alloying elements
Random number generation
Design optimization
Multiobjective optimization
Rare earth elements
Chemical analysis
Chemical elements
Phase equilibria
Sensitivity analysis
Magnetic properties
Thermodynamics

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry

Cite this

Jha, Rajesh ; Dulikravich, George S. ; Chakraborti, Nirupam ; Fan, Min ; Schwartz, Justin ; Koch, Carl C. ; Colaco, Marcelo J. ; Poloni, Carlo ; Egorov, Igor N. / Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data. In: Journal of Alloys and Compounds. 2016 ; Vol. 682. pp. 454-467.
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Jha, R, Dulikravich, GS, Chakraborti, N, Fan, M, Schwartz, J, Koch, CC, Colaco, MJ, Poloni, C & Egorov, IN 2016, 'Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data', Journal of Alloys and Compounds, vol. 682, pp. 454-467. https://doi.org/10.1016/j.jallcom.2016.04.218

Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data. / Jha, Rajesh; Dulikravich, George S.; Chakraborti, Nirupam; Fan, Min; Schwartz, Justin; Koch, Carl C.; Colaco, Marcelo J.; Poloni, Carlo; Egorov, Igor N.

In: Journal of Alloys and Compounds, Vol. 682, 15.10.2016, p. 454-467.

Research output: Contribution to journalArticle

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