Self-organizing maps for pattern recognition in design of alloys

ABSTRACT

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.

KEYWORDS:

• Alloys
• AlNiCo
• design
• magnets
• metamodels
• optimization
• properties
• SOM

Acknowledgments

This work was partially funded by the US Air Force Office of Scientific Research (grant number FA9550-12-1-0440) monitored by Dr. Ali Sayir. The lead author is also thankful for the Dissertation Year Fellowship provided by the University Graduate School, Florida International University. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US Air Force Office of Scientific Research or the US Government. The US Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation thereon.