Publications

Full List of Publications: Google Scholar

Book / Book Chapters

[B1] G. Singh, F. Imani, A. Tewari, and S. Mishra, “Machine Learning for Powder-Based Metal Additive Manufacturing”, Elsevier Series in Additive Manufacturing Materials and Technologies, ISBN 9780443221453, 2023.

Journal Articles

* Students Advised

[J38] S. Rescsanski*, S. Ghungrad, A. Haghighi, J. Tang, and F. Imani, “Constrained Motion Planning for Reduced Jerk in Robotic Additive Manufacturing Systems”, Robotics and Computer-Integrated Manufacturing, Vol.101, p103311, 2026. DOI
[J37] S. Ghungrad, R. Arabpoor, S. Rescsanski*, F. Imani, and A. Haghighi, “Kinematics-guided Multi-task Learning for Transferable Models in Robotic Manufacturing”, Robotics and Computer-Integrated Manufacturing, Vol.101, p103295, 2026. DOI
[J36] Q. Zhou, Y. Zhang, Z. Chen*, F. Jalil Piran*, F. Imani, and J. Tang, “Few-shot Vision-Language Models Grounded with Hierarchical Retrieval for Wind Turbine Blade Inspection”, Results in Engineering, Vol.30, p111187, 2026. DOI
[J35] D. Hoang*, D. Gorsich, M. P. Castanier, and F. Imani, “Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning”, International Journal of Production Economics, Vol.300, p110091, 2026. DOI
[J34] Q. Zhou, Y. Zhang, F. Imani, and J. Tang, “EPicMT: Edge-aware Physics-informed Concurrent Multiplexed Transformer for High-fidelity Signal Compression, Reconstruction, and Diagnosis/Prognosis”, Mechanical Systems and Signal Processing, Vol.253, p114337, 2026. DOI
[J33] Y. Zhang, Q. Zhou, F. Imani, and J. Tang, “Seeing the Unseen: Toward Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models”, IEEE Access, Vol.14, pp.91606-91623, 2026. DOI
[J32] K. Naghavi Khanghah, Z. Chen*, L. Romeo, Q. Yang, R. Malhotra, F. Imani, and H. Xu, “Zero-shot Anomaly Detection in Laser Powder Bed Fusion Using Multimodal Retrieval-Augmented Generation and Large Language Models”, ASME Journal of Mechanical Design, Vol.148, No.7, p072001, 2026. DOI
[J31] D. Hoang*, A. Patel, R. Chen, R. Malhotra, and F. Imani, “Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment”, Manufacturing Letters, Vol.47, pp.89-93, 2026. DOI
[J30] Z. Chen* and F. Imani, “A Multi-expert Framework for Enhancing Multimodal Large Language Models in Industrial Anomaly Detection”, Pattern Recognition, Vol.172, p112752, 2026. DOI
[J29] D. Hoang*, R. Chen, G. Bollas, and F. Imani, “Hyperdimensional Computing for Explainable Information Fusion and Multi-task Adaptation in Advanced Manufacturing”, Information Fusion, Vol.128, p103898, 2026. DOI
[J28] Q. Zhou, Y. Zhang, J. Kim, F. Imani, and J. Tang, “Spatially-Informed Online Prediction of Milling Surface Deformation Using Multiphysics-Infused Graph Neural Network for Digital Twinning”, ASME Journal of Manufacturing Science and Engineering, Vol.147, No.12, p121003, 2025. DOI
[J27] Q. Zhu, Q. Chen, H. Chen, W. Chen, S. Islam, F. Imani, J. Rao, and M. Xu, “Upcycling Soybean Meal into Edible 3D Printing Inks: The Role of Particle Size and Trace Xanthan”, ACS Food Science & Technology, Vol.5, No.11, pp.4384-4395, 2025. DOI
[J26] S. Rescsanski*, R. Hebert, A. Haghighi, J. Tang, and F. Imani, “Towards Intelligent Cooperative Robotics in Additive Manufacturing: Past, Present, and Future”, Robotics and Computer-Integrated Manufacturing, Vol.93, p102925, 2025. DOI
[J25] Z. Chen*, D. Hoang*, F. Jalil Piran*, R. Chen, and F. Imani, “Federated Hyperdimensional Computing for Hierarchical and Distributed Quality Monitoring in Smart Manufacturing”, Internet of Things, Vol.31, p101568, 2025. DOI
[J24] F. Jalil Piran*, P. P. Poduval, H. Errahmouni Barkam, M. Imani, and F. Imani, “Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring”, Engineering Applications of Artificial Intelligence, Vol.147, p110282, 2025. DOI
[J23] F. Jalil Piran*, Z. Chen*, M. Imani, and F. Imani, “Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing”, Computers and Electrical Engineering, Vol.123, p110261, 2025. DOI
[J22] Z. Chen*, H. Chen, M. Imani, R. Chen, and F. Imani, “Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces”, Expert Systems with Applications, Vol.265, p125769, 2025. DOI
[J21] S. Rescsanski*, V. Shah*, J. Tang, and F. Imani, “Stochastic Defect Localization for Cooperative Additive Manufacturing Using Gaussian Mixture Maps”, ASME Journal of Computing and Information Science in Engineering, Vol.24, No.11, p111006, 2024. DOI
[J20] D. Hoang*, H. Errahmouni, H. Chen, S. Rachuri, N. Mannan, R. ElKharboutly, M. Imani, R. Chen, and F. Imani, “Hierarchical Representation and Interpretable Learning for Accelerated Quality Monitoring in Machining Process”, CIRP Journal of Manufacturing Science and Technology, Vol.50, pp.198-212, 2024. DOI
[J19] S. Bansude, F. Imani, and R. Sheikhi, “Performance Assessment of Chemical Kinetics Neural Ordinary Differential Equations in Pairwise Mixing Stirred Reactor”, ASME Open Journal of Engineering, Vol.2, 2023. DOI
[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

Peer-reviewed Conference Papers

[C37] K. Naghavi Khanghah, Z. Chen*, L. Romeo, Q. Yang, R. Malhotra, F. Imani, and H. Xu, “Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion Using Large Language Models,” ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), Vol.3A: 51st Design Automation Conference (DAC), 2025. DOI
[C36] Z. Chen*, H. Chen, M. Imani, and F. Imani, “Can Multimodal Large Language Models Be Guided to Improve Industrial Anomaly Detection?,” ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), Vol.2B: 45th Computers and Information in Engineering Conference (CIE), 2025. DOI
[C35] Q. Zhou, Y. Zhang, Z. Chen*, F. Jalil Piran*, F. Imani, and J. Tang, “Few-Shot Visual Reasoning for Wind Turbine Blade Damage Detection via RAG with Vision-Language Model,” IFAC-PapersOnLine, Vol.59, No.30, pp.37-42, 2025. DOI
[C34] I. Morales Soto, Q. Zhou, F. Imani, and J. Tang, “Machinery Diagnosis Leveraging Machine Learning Assisted by Physics-guided Signal Processing,” SPIE Digital Twins, AI, and NDE for Industry Applications and Energy Systems, 2025. DOI
[C33] D. Hoang*, D. Gorsich, M. Castanier, and F. Imani, “Enabling Grounded Answers Through Knowledge Graphs and Retrieval Augmented Generation,” NDIA Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), SAE Technical Paper 2025-01-0488, 2025. DOI
[C32] F. Jalil Piran*, P. P. Poduval, H. Errahmouni Barkam, M. Imani, and F. Imani, “Privacy-Preserving In-Situ Monitoring in Additive Manufacturing Through Hyperdimensional Computing,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), Vol.2: Advanced Manufacturing, 2024. DOI
[C31] Z. Chen*, D. Hoang*, R. Chen, and F. Imani, “Distributed Hyperdimensional Computing for Real-Time Data Aggregation and Interpretable Quality Monitoring in Manufacturing,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), Vol.2: Advanced Manufacturing, 2024. DOI
[C30] S. Rescsanski*, T. Nardi*, V. Shah*, J. Tang, and F. Imani, “Multi-Fidelity Sensing and Digital Twin System for Automated Monitoring in Cooperative Robotic Additive Manufacturing,” ASME International Symposium on Flexible Automation (ISFA), pp. V001T01A003, 2024. DOI
[C29] D. Hoang*, H. Chen, M. Imani, R. Chen, and F. Imani, “Brief Paper: Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics with Parts Geometrical Deviations,” ASME International Manufacturing Science and Engineering Conference (MSEC), Vol.2, 2024. DOI
[C28] F. Jalil Piran*, H. Errahmouni Barkam, M. Imani, and F. Imani, “Hyperdimensional Cognitive Computing for Lightweight Cyberattack Detection in Industrial Internet of Things,” ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), Vol.7: 19th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), 2023. DOI
[C27] D. Hoang*, R. Chen, D. Mishra, S. K. Pal, and F. Imani, “Data Fusion Cognitive Computing for Characterization of Mechanical Property in Friction Stir Welding Process,” ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), Vol.2: 43rd Computers and Information in Engineering Conference (CIE), 2023. DOI
[C26] S. Rescsanski*, A. Yadollahi, M. Khanzadeh, and F. Imani, “Anomaly Detection of Laser-Based Metal Additive Manufacturing Using Neural-Variational Auto-Encoder,” ASME International Manufacturing Science and Engineering Conference (MSEC), Vol.1, 2023. DOI
[C25] D. Hoang*, N. Mannan, R. ElKharboutly, R. Chen, and F. Imani, “Edge Cognitive Data Fusion: From In-Situ Sensing to Quality Characterization in Hybrid Manufacturing Process,” ASME International Manufacturing Science and Engineering Conference (MSEC), Vol.2, 2023. DOI
[C24] M. Imani, Y. Kim, B. Khaleghi, J. Morris, H. Alimohamadi, F. Imani, and H. Latapie, “Hierarchical, Distributed and Brain-Inspired Learning for Internet of Things Systems,” 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS), pp.511-522, 2023. DOI
[C23] J. R. Rubalcava-Cortés, A. Hernandez-Cano, A. C. Pacheco-Tovar, F. Imani, R. Cammarota, and M. Imani, “Privacy-preserving Neural Representation for Brain-inspired Learning,” Design, Automation and Test in Europe Conference (DATE), IEEE, 2023.
[C22] S. Rescsanski*, M. Imani, and F. Imani, “Heterogeneous Sensing and Bayesian Optimization for Smart Calibration in Additive Manufacturing Process,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), 2022. (Accepted)
[C21] F. Imani and R. Chen, “Latent Representation and Characterization of Scanning Strategy on Laser Powder Bed Fusion Additive Manufacturing,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), 2022. (Accepted)
[C20] K. Safari, S. Khalfalla*, and F. Imani, “A Novel Fuzzy-BELBIC Structure for the Adaptive Control of Satellite Attitude,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), 2022. (Accepted)
[C19] K. Safari and F. Imani, “Dependency Evaluation of Defect Formation and Printing Location in Additive Manufacturing,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), 2022. (Accepted)
[C18] P. Poduval, Y. Ni, Z. Zou, F. Imani, Y. Kim, R. Cammarota, K. Ni, and M. Imani, “NetHD: Brain-Inspired Hyperdimensional System for Robust Data Transmission and Integrated Learning,” IEEE International Conference on Computer Communications (INFOCOM), 2022. (Submitted)
[C17] M. Imani, A. Zakeri, H. Chen, T. Kim, P. Poduval, H. Lee, Y. Kim, E. Sadredini, and F. Imani, “Neural Computation for Robust and Holographic Face Detection,” Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC), pp.31-36, 2022. DOI
[C16] S. Bansude, F. Imani, and R. Sheikhi, “Implementation and Assessment of Chemical Kinetics Neural ODEs for Combustion Simulations,” 75th Annual Meeting of the APS Division of Fluid Dynamics, 2022.
[C15] Z. Zou, Y. Kim, F. Imani, H. Alimohamadi, R. Cammarota, and M. Imani, “Scalable edge-based hyperdimensional learning system with brain-like neural adaptation,” International Conference for High Performance Computing, Networking, Storage and Analysis, pp.1-15, 2021. DOI
[C14] P. Poduval, M. Issa, F. Imani, C. Zhuo, X. Yin, H. Najafi, and M. Imani, “Robust In-Memory Computing with Hyperdimensional Stochastic Representation,” IEEE/ACM International Symposium on Nanoscale Architectures, pp.1-6, 2021. DOI
[C13] F. Imani, and M. Khanzadeh, “Image-Guided Multi-Response Modeling and Characterization of Design Defects in Metal Additive Manufacturing,” ASME The International Mechanical Engineering Congress and Exposition (IMECE), 2021. DOI
[C12] X. Zhao, A. Imandoust, M. Khanzadeh, F. Imani, and Linkan Bian, “Automated Anomaly Detection of Laser-based Additive Manufacturing Using Melt Pool Sparse Representation and Unsupervised Learning,” Solid Free Form Fabrication Symposium, 2021. LINK
[C11] J. Quevedo, M. Abdelatti, F. Imani, and M. Sodhi, “Using Reinforcement Learning for Tuning Genetic Algorithms,” The Genetic and Evolutionary Computation Conference (GECCO), 2021. DOI
[C10] F. Imani, Z. Qiu, and H. Yang, “Markov Decision Process Modeling for Multi-stage Optimization of Intervention and Treatment Strategies in Breast Cancer,” Proceeding of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 20-24, 2020, the EMBS Virtual Academy. DOI
[C9] Z. Qiu, F. Imani, and H. Yang, “Hierarchical Gaussian Process Modeling and Estimation of State-action Transition Dynamics in Breast Cancer,” Proceeding of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, July 20-24, 2020, the EMBS Virtual Academy. DOI
[C8] F. Imani, R. Chen, C. Tucker, and H. Yang, “Random Forest Modeling for Survival Analysis of Cancer Recurrences,” Proceeding of the 15th IEEE International Conference on Automation Science and Engineering (CASE), Aug 22-26, 2019, Vancouver, BC, Canada. DOI (IEEE CASE invited talk)
[C7] F. Imani, R. Chen, E. Diewald, E. Reutzel, and H. Yang, “Image-guided Variant Shape Analysis of Layerwise Build Quality in Additive Manufacturing,” Proceeding of the 14th International Manufacturing Science and Engineering Conference (MSEC), June 10-14, 2019, Erie, PA. LINK
[C6] A. Gaikwad, F. Imani, P. Rao, H. Yang, and E. Reutzel, “On Design Rules and In-process Build Quality Monitoring of Thin-wall Features Made Using Laser Powder Bed Fusion Additive Manufacturing Process,” Proceeding of the 14th International Manufacturing Science and Engineering Conference, June 10-14, 2019, Erie, PA. LINK
[C5] F. Imani, B. Yao, R. Chen, P. Rao, and H. Yang, “Fractal Pattern Recognition of Image Profiles for Manufacturing Process Monitoring and Control,” Proceeding of the 13th International Manufacturing Science and Engineering Conference, pp. V003T02A003, June 10-14, 2018, College Station, TX. DOI (The 2nd Place in Service Enterprise Engineering Competition)
[C4] F. Imani, A. Gaikwad, M. Montazeri, P. Rao, H. Yang, and E. Reutzel, “Layerwise In-process Quality Monitoring in Laser Powder Bed Fusion,” Proceeding of the 13th International Manufacturing Science and Engineering Conference, pp. V001T01A038, June 10-14, 2018, College Station, TX. DOI
[C3] F. Imani, C. Cheng, R. Chen, and H. Yang, “Nested Gaussian Process Modeling for High-dimensional Data Imputation in Healthcare Systems,” Proceeding of Institute of Industrial and System Engineering (IISE) Annual Conference, pp. 1312-1317, May 19-22, 2018, Orlando, FL. LINK (Best paper finalist in IISE Conference Healthcare Systems Division)
[C2] F. Imani, T. Jin, and L. Bai, “Modeling and Simulation of Hybrid Battery Swapping Stations with Fast Onboard Charging,” Proceeding of Industrial and Systems Engineering Research Conference (ISERC), pp. 1028-1033, May 21-24, 2016, Anaheim, CA. LINK
[C1] K.H. Gabriel Bae, L. Zheng, and F. Imani, “A Simulation Analysis of the Vehicle Axle and Spring System Assembly Line,” Proceeding of 2015 Winter Simulation Conference (WSC), pp. 2249-2259, December 6-9, 2015, Huntington Beach, CA. DOI