Assistant Professor, School of Mechanical, Aerospace and Manufacturing Engineering
| farhad.imani@uconn.edu | |
| Mailing Address | School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, 191 Auditorium Rd. U-3139, Storrs, CT 06269 |
| Campus | Storrs |
| Link | Lab Website |
| Google Scholar Link | |
Brief Bio
Dr. Imani joined the University of Connecticut in 2020 as an Assistant Professor in the Department of Mechanical Engineering. He received his Dual-title Ph.D. in Industrial Engineering and Operations Research from the Pennsylvania State University in 2020. His research interests focus on data analytics, machine learning, statistical learning, and decision theory for process monitoring and control, system diagnostics and prognostics, quality and reliability improvement with applications in advanced manufacturing.
He was a recipient of multiple awards including James E. Marley Graduate Fellowship in Engineering (2020), Brush Graduate Fellowship (2019), University Graduate Fellowship (2016), and Breakthrough Project in National Science Foundation Industry–University Cooperative Research Centers (2020).
- Cognitive Computing in Cyber Manufacturing
- Collective Intelligence in Advanced Manufacturing
- Multi-scale and In-situ Quality Assurance in Additive Manufacturing
- ME 3227 Design of Machine Elements (Spring 2021, Spring 2022, Fall 2022, Spring 2023)
- ME 3295 Computational Foundations of Digital Manufacturing (Fall 2021, Fall 2024)
- ENGR 3215 Statistical Quality Control and Reliability for Manufacturing (Fall 2023)
| J18] | R. Chen, M. Sodhi, M. Imani, M. Khanzadeh, A. Yadollahi, and F. Imani, “Brain-inspired Computing for In-process Melt Pool Characterization in Additive Manufacturing”, CIRP Journal of Manufacturing Science and Technology, 2023. | |
| [J17] | S. Bansude, F. Imani, and R. Sheikhi, “A Data-driven Framework for Computationally Efficient Integration of Chemical Kinetics Using Neural Ordinary Differential Equations”, Journal of Computational Physics, 2022. | |
| [J16] | R. Chen, E. W. Reutzel, M. Khanzadeh, and F. Imani, “Heterogeneous Gaussian Process for Modeling Design-induced Defect in Powder Bed Fusion Additive Manufacturing”, Journal of Additive Manufacturing Letters, Vol.3, p100042, 2022. DOI | |
| [J15] | Z. Zou, H. Alimohamadi, A. Zakeri, F. Imani, Y. Kim, M. Najafi, and M. Imani, “Memory-inspired spiking hyperdimensional network for robust online learning”, Nature Scientific Reports, Vol.3, No. 1, p1-13, 2022. DOI | |
| [J14] | P. Poduval, A. Zakeri, F. Imani, H. Alimohamadi, M. Imani, “Graphd: Graph-based hyperdimensional memorization for brain-like cognitive learning”, Frontiers in Neuroscience, Vol. 16, p.5, 2022. DOI | |
| [J13] | R. Chen, M. Imani, F. Imani, “Joint Active Search and Neuromorphic Computing for Efficient Data Exploitation and Monitoring in Additive Manufacturing”, Elsevier Journal of Manufacturing Process, Vol. 71, p 743-752, 2021. DOI | |
| [J12] | R. Yazdi, F. Imani, and H. Yang, “A Hybrid Deep Learning Model of Process-Build Interactions in Additive Manufacturing”, Elsevier Journal of Manufacturing System, 2020. DOI | |
| [J11] | F. Imani, B. Yao, R. Chen, P. Rao, and H. Yang, “Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control”, ASME Journal of Manufacturing Science and Engineering, Vol. 141, No. 4, p044501, 2019. DOI | |
| [J10] | F. Imani, C. Cheng, R. Chen, and H. Yang, “Nested Gaussian Process Modeling and Imputation of High-dimensional Incomplete Data Under Uncertainty”, IISE Transactions on Healthcare Systems Engineering, Vol. 9, No. 4, p315-326, 2019. DOI | |
| [J9] | F. Imani, R. Chen, E. Diewald, E. Reutzel, and H. Yang, “Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control”, ASME Transactions Journal of Manufacturing Science and Engineering, Vol. 141, No. 11, p111001, 2019. DOI | |
| [J8] | A. Gaikwad, F. Imani, P. Rao, H. Yang, and E. Reutzel, “In-situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion using Deep Learning”, ASTM Journal of Smart and Sustainable Manufacturing Systems, Vol. 3, No. 1, p98-121, 2019. DOI | |
| [J7] | R. Chen, F. Imani, and H. Yang, “Heterogeneous Recurrence Analysis of Disease-altered Spatiotemporal Patterns in Multi-channel Cardiac Signals”, IEEE Journal of Biomedical and Health Informatics, Vol. 24, No. 6, 2019. DOI | |
| [J6] | F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang, and E. Reutzel, “Process Mapping and In-process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging”, ASME Transactions Journal of Manufacturing Science and Engineering, Vol. 140, No. 10, p101009, 2018. DOI | |
| [J5] | B. Yao, F. Imani, H. Yang, and E. Reutzel, “Multifractal Analysis of Image Profiles for the Characterization and Detection of Defects in Additive Manufacturing”, ASME Journal of Manufacturing Science and Engineering, Vol. 140, No. 3, p031014, 2018. DOI | |
| [J4] | B. Yao, F. Imani, and H. Yang, “Markov Decision Process for Image-guided Additive Manufacturing”, IEEE Robotics and Automation Letters, Vol. 3, No. 4, p2792-2798, 2018. DOI | |
| [J3] | R. Chen, F. Imani, E. Reutzel, and H. Yang, “From Design Complexity to Build Quality in Additive Manufacturing – A Sensor-based Perspective”, IEEE Sensor Letters, Vol. 3, No. 4, p1-4, 2018. DOI | |
| [J2] | F. Imani, and K.H. Gabriel Bae, “Preventive Maintenance Modeling in Lifetime Warranty”, International Journal of Quality Engineering and Technology, Vol.6, No. 4, p249-268, 2017. DOI | |
| [J1] | F. Imani, H. Shahriari, and A. Asl Hadad, “An Optimal Preventive Maintenance Policy During the Lifetime Warranty,” Technical Journal of Engineering and Applied Sciences, Vol. 3, No. 24, p3525-3533, 2013 |