Self-organizing maps for pattern recognition in design of alloys

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 journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1067-1074
Number of pages8
JournalMaterials and Manufacturing Processes
Issue number10
StatePublished - Jul 27 2017

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


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