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Algorithms for design optimization of chemistry of hard magnetic alloys using experimental data
- Publication Year :
- 2016
-
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.
- Subjects :
- Materials science
Random number generation
Alloy
Response surfaces
02 engineering and technology
engineering.material
Design of alloys, Magnetic materials, Computational materials design, Response surfaces, Meta-models, Multi-objective optimization, Pareto-optimized predictions
01 natural sciences
Multi-objective optimization
Condensed Matter::Materials Science
Phase (matter)
0103 physical sciences
Materials Chemistry
Sensitivity (control systems)
Magnetic materials
010302 applied physics
Mechanical Engineering
Metals and Alloys
Pareto principle
Experimental data
Atmospheric temperature range
021001 nanoscience & nanotechnology
Computational materials design
Pareto-optimized predictions
Mechanics of Materials
engineering
Meta-models
0210 nano-technology
Algorithm
Design of alloys
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....9ac01c3cb3a58e81e36f929588f926a9