Document Type

Thesis

Degree

Doctor of Philosophy (PhD)

Major/Program

Materials Science and Engineering

First Advisor's Name

Professor George S. Dulikravich

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Professor Arvind Agarwal

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Professor Nirupam Chakraborti

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Professor Sakhrat Khizroev

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Professor Surendra S. Saxena

Fifth Advisor's Committee Title

Committee Member

Keywords

AlNiCo magnets, Data-driven Materials Science, Composition-property relationship, Materials Genome Initiative (MGI-ICME), Meta-model (Response Surfaces), Evolutionary (Genetic) Algorithms, Multi-objective Optimization, Pareto Optimality, Principal Component Analysis (PCA), Heirarchichal Clustering Analysis (HCA)

Date of Defense

5-20-2016

Abstract

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties.

In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.

Identifier

FIDC000704

ORCID

http://orcid.org/0000-0002-7169-8659

Comments

This work was funded by "Air Force Office of Scientific Research (AFOSR), NM."

In the present research, we proposed a novel approach to efficiently use limited information from literature and experimental database and ways to couple it with a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys.

Notable features of this work are as follows:

  • We have presented this work at several International conferences and our approach was appreciated by experts from leading organizations working on multi-scale modeling of materials.
  • We have published a book chapter, and several journal articles and refereed conference proceedings on this work.
  • Currently, multi-scale modeling (Integrated Computational Materials Engineering or ICME approach) integration of microstructure, properties, numerical codes, experimental methods etc, and the future is to automate this approach.
  • Our current research will help in moving a step ahead of current ICME, that is, towards realizing virtual material design paradigm for the design and accelerated deployment of alloys for targeted properties. This approach can be extended for designing of new alloys or improving properties of existing alloys and its accelerated deployment. We have tested our approach on Nickel based superalloys.

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