ABSTRACT AlNiCo magnets are permanent magnetic alloys based on the Al-Ni-Co-Fe system. In the present work, Sobol’s algorithm was used to randomly generate alloy compositions so that they are properly distributed in the variable space. The final 80 candidate alloys were selected after examining the phase equilibria and magnetic property predicted by thermodynamic database FactsageTM in the desired temperature of exposure. The magnets were synthesized and tested for desired properties of interest. The various properties were fitted by response surfaces generated by Radial Basis Function module available in commercial optimizer,”modeFRONTIER”. It was followed by optimization for predicting alloy composition for improved properties. This task was simultaneously performed by another commercial optimizer, IOSO. The 5 Pareto optimized candidate alloys were synthesized and tested. One of the optimized candidate alloys dominated the initial 80 candidate alloys in most of the properties. This proves the efficacy of response surface methodology in optimizing the desired properties while minimizing the time and cost in synthesizing the alloys by random experimentation [less]

A COMBINED COMPUTATIONAL-EXPERIMENTAL APPROACH TO DESIGN OF HIGH-INTENSITY PERMANENT MAGNETIC ALLOYS

POLONI, CARLO
2014

Abstract

ABSTRACT AlNiCo magnets are permanent magnetic alloys based on the Al-Ni-Co-Fe system. In the present work, Sobol’s algorithm was used to randomly generate alloy compositions so that they are properly distributed in the variable space. The final 80 candidate alloys were selected after examining the phase equilibria and magnetic property predicted by thermodynamic database FactsageTM in the desired temperature of exposure. The magnets were synthesized and tested for desired properties of interest. The various properties were fitted by response surfaces generated by Radial Basis Function module available in commercial optimizer,”modeFRONTIER”. It was followed by optimization for predicting alloy composition for improved properties. This task was simultaneously performed by another commercial optimizer, IOSO. The 5 Pareto optimized candidate alloys were synthesized and tested. One of the optimized candidate alloys dominated the initial 80 candidate alloys in most of the properties. This proves the efficacy of response surface methodology in optimizing the desired properties while minimizing the time and cost in synthesizing the alloys by random experimentation [less]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11368/2833412
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