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

JOURNAL OF ALLOYS AND COMPOUNDS

卷:682

Article

Jha, Rajesh¹  Dulikravich, George S.¹  Chakraborti, Nirupam²  Fan, Min³  Schwartz, Justin³  Koch, Carl C.³  Colaco, Marcelo J.⁴  Poloni, Carlo⁵  Egorov, Igor N.⁶ 

[1] Florida Int Univ, MAIDROC Lab, Dept Mech & Mat Engn, 10555 West Flagler St, Miami, FL 33174 USA

[2] Indian Inst Technol, Dept Mat Engn, Kharagpur 721302, W Bengal, India

[3] N Carolina State Univ, Mat Sci & Engn Dept, Raleigh, NC 27695 USA

[4] Univ Fed Rio de Janeiro, COPPE, Dept Mech Engn, BR-21945 Rio De Janeiro, Brazil

[5] Univ Trieste, Dipartimento Ingn & Architettura, Via Valerio 10, I-34127 Trieste, Italy

[6] SIGMA Technol, Electrozavodskaia St 20, Moscow 107023, Russia

关键词: Design of alloys;    Magnetic materials;    Computational materials design;    Response surfaces;    Meta-models;    Multi-objective optimization;    Pareto-optimized predictions;   

DOI  :  10.1016/j.jallcom.2016.04.218

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free