Amir Kordijazi

Assistant Professor of Industrial Engineering

207-780-5447

amir.kordijazi@maine.edu

135 John Mitchell Center, Gorham Campus

Education

PhD (Industrial & Manufacturing Engineering), 2021
MSc (Materials Science & Engineering), 2012
BSc (Materials Science & Engineering), 2009

Current Courses

EGN 401 Senior Design Project I and Engineering Profession
EGN 304: Engineering Economics
EGN 160: Introduction to Programming: Python

Research Interests

Dr. Kordijazi’s current research interests include:

Computer vision for industrial applications
Predictive analytics for advanced manufacturing processes
Autonomous experimentation

His research has broad applicability across diverse industry sectors, spanning biomedical, marine, aerospace, and semiconductor industries, among others.

Department of Engineering College of Science, Technology, & Health

Dr. Kordijazi is an interdisciplinary scholar with a strong passion for teaching and research, specializing in the field of Industry 4.0. His expertise centers around the application of artificial intelligence (AI) and data analytics in industrial context. In 2021, he earned his Ph.D. in Industrial & Manufacturing Engineering from the University of Wisconsin, Milwaukee. During his doctoral studies, he dedicated his research to developing and employing AI/ML and statistical techniques, aimed to enhance the design, characterization, and manufacturing processes of high-performance alloys and nanocomposites. Following the completion of his Ph.D., he took on a postdoctoral position at the University at Albany – SUNY. During this phase, his research concentrated on the development of state-of-the-art computer vision algorithms designed for characterizing and performing metrology on the latest generations of nanoscale electronic materials, utilizing four-dimensional scanning electron microscopy data.

In the fall of 2023, Dr. Kordijazi took on the position of Assistant Professor of Industrial Engineering at the University of Southern Maine. His research group is involved in thrusts such as computer vision for industrial applications, predictive analytics for advanced manufacturing processes, and autonomous experimentation. Recently, his lab secured grants from the Maine Space Grant Consortium (MSGC) and the Maine Economic Improvement Fund (MEIF) to establish self-driving materials manufacturing labs. This facility aims to design innovative materials with functional and structural applications. Additionally, in spring 2024, he commenced an Inaugural Associate position at the Institute of Medicine at the University of Maine, where he directs his research towards designing novel materials and manufacturing processes with biomedical applications.

Dr. Kordijazi has published over 35 peer-reviewed articles in renowned journals, including Surfaces and Interfaces, Tribology International, Langmuir, and Materials Today Communications. His academic achievements include receiving the Graduate Student Excellence Fellowship and Chancellor’s Graduate Student Awards from the University of Wisconsin, Milwaukee. Additionally, he earned two Best Paper Awards from the American Foundry Society.

Selected Publications

Rohatgi, M., & Kordijazi, A. (2023). Application of machine learning to mechanical properties of copper-graphene composites, MRS Communications, 13, 111–116.
Alrfou, K., Kordijazi, A., Rohatgi, P. & Zhao, T. (2022). Synergy of unsupervised and supervised machine learning methods for the segmentation of the graphite particles in the microstructure of ductile iron, Materials Today Communications, 30, 103174
Zhao, J., Kordijazi, A., Valensa, C., Roshan, H., Kolhe, Y., & Rohatgi, P. (2022). Behavior of Steel Foam Sandwich Members Cast with 3D Printed Sand Cores, JOM, 74 (5), 2083–2093.
Hasan, M. S., Kordijazi, A., Rohatgi, P. K., & Nosonovsky, M. (2022). Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites. Tribology International, 165, 107326.
Hasan, M. S., Kordijazi, A., Rohatgi, P. K., & Nosonovsky, M. (2022). Triboinformatics approach for friction and wear prediction of Al-Graphite composites using machine learning methods. Journal of Tribology, 144(1), 011701
Kordijazi, A., Zhao, T., Zhang, J., Alrfou, K., & Rohatgi, P. (2021). A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications. JOM, 73, 2060–2074.
Hasan, M. S., Kordijazi, A., Rohatgi, P. K., & Nosonovsky, M. (2021). Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms. Tribology International, 161, 107065.
Kordijazi, A., Behera, S., Patel, D., Rohatgi, P., & Nosonovsky, M. (2021). Predictive Analysis of Wettability of Al–Si Based Multiphase Alloys and Aluminum Matrix Composites by Machine Learning and Physical Modeling. Langmuir, 37(12), 3766-3777.
Kordijazi, A., Roshan, H. M., Dhingra, A., Povolo, M., Rohatgi, P. K., & Nosonovsky, M. (2020). Machine-learning methods to predict the wetting properties of iron-based composites. Surface Innovations, 9(2–3), 111-119.
Kordijazi, A., Weiss, D., Das, S., Behera, S., Roshan, H. M., & Rohatgi, P. (2020). Effect of solidification time on microstructure, wettability, and corrosion properties of A205-T7 aluminum alloys. International Journal of Metalcasting, 15(1), 2-12.
Kordijazi, A., Behera, S. K., Suri, S., Wang, Z., Povolo, M., Salowitz, N., & Rohatgi, P. (2020). Data-driven modeling of wetting angle and corrosion resistance of hypereutectic cast Aluminum-Silicon alloys based on physical and chemical properties of surface. Surfaces and Interfaces, 20, 100549.

Additional Publications

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