Research Area

Primary field: Integrated Computational Materials Engineering; Microstructure-Centered Materials Design

Methods: CALPHAD; Phase-field Methods; Generative Machine Learning

Microstructure image databases play a crucial role in the field of materials science. These databases contain a vast collection of images representing the microstructures of different materials, including metals, ceramics, polymers, and composites. The importance of microstructure image databases lies in their ability to provide researchers and scientists with a wealth of information about the structure and properties of materials.

Microstructure image databases serve as valuable resources for materials scientists to study and analyze the relationship between microstructure and material properties. By examining the images in these databases, researchers can gain insights into the grain size, shape, distribution, and orientation of the constituent phases in a material. This information is vital in understanding how these microstructural features influence the mechanical, thermal, electrical, and chemical behavior of materials. By having access to a comprehensive microstructure image database, materials scientists can accelerate their research, identify trends, and make informed decisions when designing new materials or optimizing existing ones.

Microstructure image databases facilitate the development and validation of computer-based image analysis techniques. With the increasing availability of high-resolution imaging technologies, there is a growing need for accurate and efficient methods to extract quantitative information from microstructure images. These databases provide a benchmark for evaluating the performance of image analysis algorithms and software tools. Researchers can compare the results of their algorithms with the ground truth data in the database to assess their accuracy and reliability. This iterative process helps to refine and improve the image analysis techniques, enabling more accurate characterization and measurement of microstructural features.

In conclusion, microstructure image databases are of paramount importance in the field of materials science. They provide a valuable resource for studying the relationship between microstructure and material properties, enabling scientists to make informed decisions when designing and optimizing materials. Moreover, these databases facilitate the development and validation of computer-based image analysis techniques, leading to more accurate and efficient characterization of microstructural features. As the field of materials science continues to advance, the importance of microstructure image databases is only expected to grow, providing a foundation for future research and innovation.

Selected Publications

Screenshot of www.sciencedirect.com
Screenshot of www.sciencedirect.com

List of publications:

1.
Khatamsaz D, Attari V, Arroyave R, Allaire DL. Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples [Internet]. arXiv; 2023 [cited 2023 Feb 21].
1.
Mancias J, Attari V, Arróyave R, Tourret D. On the Effect of Nucleation Undercooling on Phase Transformation Kinetics. Integr Mater Manuf Innov [Internet]. 2022 Dec 1 [cited 2023 Feb 21];11(4):628–36.
1.
Harrington GH, Kelly C, Attari V, Arroyave R, Kalidindi SR. Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition. Integr Mater Manuf Innov [Internet]. 2022 Sep 1 [cited 2022 Oct 27];11(3):433–49.
1.
Kunselman C, Sheikh S, Mikkelsen M, Attari V, Arróyave R. Microstructure classification in the unsupervised context. Acta Materialia [Internet]. 2022 Jan 15 [cited 2022 Mar 30];223:117434.
1.
Attari V, Arróyave R. Phase-Field Study of Thermomigration in 3-D IC Micro Interconnects. IEEE Transactions on Components, Packaging and Manufacturing Technology. 2020 Sep;10(9):1466–73.
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McClenny L, Haile M, Attari V, Sadler B, Braga-Neto U, Arroyave R. Deep Multimodal Transfer-Learned Regression in Data-Poor Domains [Internet]. arXiv; 2020 [cited 2022 Oct 27].
1.
Kunselman C, Attari V, McClenny L, Braga-Neto U, Arroyave R. Semi-supervised learning approaches to class assignment in ambiguous microstructures. Acta Materialia [Internet]. 2020 Apr 15 [cited 2022 Oct 27];188:49–62.
1.
Duong TC, Hackenberg RE, Attari V, Landa A, Turchi PEA, Arróyave R. Investigation of the discontinuous precipitation of U-Nb alloys via thermodynamic analysis and phase-field modeling. Computational Materials Science [Internet]. 2020 Apr 1 [cited 2022 Oct 27];175:109573.
1.
Davidson R, Verma A, Santos D, Hao F, Fincher CD, Zhao D, et al. Mapping mechanisms and growth regimes of magnesium electrodeposition at high current densities. Mater Horiz [Internet]. 2020 Mar 9 [cited 2022 Oct 27];7(3):843–54.
1.
Karayagiz K, Johnson L, Seede R, Attari V, Zhang B, Huang X, et al. Finite interface dissipation phase field modeling of Ni–Nb under additive manufacturing conditions. Acta Materialia [Internet]. 2020 Feb 15 [cited 2022 Oct 27];185:320–39.
1.
Attari V, Honarmandi P, Duong T, Sauceda DJ, Allaire D, Arroyave R. Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model. Acta Materialia [Internet]. 2020 Jan 15 [cited 2022 Oct 27];183:452–70.
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Attari V, Cruzado A, Arroyave R. Exploration of the Microstructure Space in TiAlZrN Ultra-Hard Nanostructured Coatings. Acta Materialia [Internet]. 2019 May 31 [cited 2019 May 31];
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Attari V, Duong T, Arroyave R. Electromigration response of microjoints in 3DIC packaging systems. arXiv preprint arXiv:190509901. 2019;
1.
Sanghvi M, Honarmandi P, Attari V, Duong T, Arroyave R, Allaire DL. Uncertainty propagation via probability measure optimized importance weights with application to parametric materials models. In: AIAA Scitech 2019 forum. 2019. p. 0967.
1.
Attari V, Ghosh S, Duong T, Arroyave R. On the interfacial phase growth and vacancy evolution during accelerated electromigration in Cu/Sn/Cu microjoints. Acta Materialia [Internet]. 2018 Nov 1 [cited 2022 Oct 27];160:185–98.
1.
Attari V, Arroyave R. Phase Field Modeling of Joint Formation During Isothermal Solidification in 3DIC Micro Packaging. J Phase Equilib Diffus [Internet]. 2016 Aug 1 [cited 2017 Feb 16];37(4):469–80.
1.
Attari V. Enhancement of the Low Temperature Deformation Ability of Magnesium Alloys. 2012;