Zhaohui Geng

Zhaohui Geng joined Ohio University in 2023. His major research interests are in the intersection of data science, artificial intelligence, and optimization, with applications in advanced manufacturing and healthcare systems. He was an assistant professor in the Department of Manufacturing and Industrial Engineering at the University of Texas Rio Grande Valley (UTRGV) and a core member of the UTRGV Center for Advanced Manufacturing and Cyber Systems, leading the Cyber Manufacturing and Smart, Connected Systems division. Before that, he was a Health Sciences Research Fellow of the Department of Neurological Surgery in the School of Medicine at the University of Pittsburgh. Zhaohui received his Ph.D. in Industrial engineering (2021), Master of Art in Statistics (2018), and Master of Science in Industrial Engineering (2016) from the University of Pittsburgh, and Bachelor of Engineering in Electronic Science and Technology from Nankai University.

Research Interests: advanced manufacturing, digital transformation, machine learning, knowledge engineering, healthcare systems.

All Degrees Earned: Ph.D., Industrial Engineering, University of Pittsburgh, 2021; M.A., Statistics, University of Pittsburgh, 2018; M.S., Industrial Engineering, University of Pittsburgh, 2016; B.E., Electronic Science and Technology, Nankai University, 2014.

Journal Article, Academic Journal (11)

• Geng, Z., Sabbaghi, A., and Bidanda, B. (2023) Automated variance modeling for three-dimensional point cloud data via Bayesian neural networks. IISE Transactions, 55:9, 912-925, DOI: 10.1080/24725854.2022.2106389
• Geng, Z., Sabbaghi, A., and Bidanda, B. (2023) Reconstructing original design: Process planning for reverse engineering. IISE Transactions, 55:5, 509-522, DOI: 10.1080/24725854.2022.2040761
• Geng, Z., Sabbaghi, A., and Bidanda, B. (2022) A framework of tolerance specification for freeform point clouds and capability analysis for reverse engineering processes, International Journal of Production Research, 60:24, 7475-7491, DOI: 10.1080/00207543.2022.2086083
• Geng, Z., and Bidanda, B. (2022) Automated posture positioning for high precision 3D scanning of a freeform design using Bayesian optimization. Manufacturing Letters, 33, 802-807, DOI: 10.1016/j.mfglet.2022.07.099
• Geng, Z., Garcia, M. E., and Bidanda, B. (2022) Minimax registration for point cloud alignment. Manufacturing Letters, 33, 872-879, DOI: 10.1016/j.mfglet.2022.07.108
• Geng, Z., and Bidanda, B. (2022) Tolerance estimation and metrology for reverse engineering based remanufacturing systems. International Journal of Production Research, 60:9, 2802-2815, DOI: 10.1080/00207543.2021.1904158
• Geng, Z., and Bidanda, B. (2021) Geometric precision analysis for additive manufacturing processes: A comparative study. Precision Engineering, 69, 68-76, DOI: 10.1016/j.precisioneng.2020.12.022
• Geng, Z., and Bidanda, B. (2017) Review of reverse engineering systems – current state of the art. Virtual and Physical Prototyping, 12:2, 161-172, DOI: 10.1080/17452759.2017.1302787
• Wang, P., Chen, Y., Geng, Z., Zhang, Z., Zheng, Y., Zhang, Z., Zhou, T., and Cai, D. (2015) Electronic and magnetic properties of 5d atoms doped aluminum nitride nanotubes: A first-principles calculation. Journal of Atomic and Molecular Physics, 32:5, 783-790.
• Zhang, Z., Geng, Z., Cai, D., Pan, T., Chen, Y., Dong, Y., and Zhou, T. (2015) Structure, electronic and magnetic properties of hexagonal boron nitride sheets doped by 5d transition metal atoms: First-principles calculations and molecular orbital analysis. Physica E: Low-Dimensional Systems and Nanostructures, 65, 24-29.
• Zhang, Z., Geng, Z., Wang, P., Hu, Y., Zheng, Y., and Zhou, T. (2013) Properties of 5d atoms doped boron nitride nanotubes: A first-principles calculation and molecular orbital analysis. Acta Physica Sinica, 62:24, 246301.

Book, Chapter in in Scholarly Book (4)

• Geng, Z., and Bidanda, B. (2022) The dual functionality of reverse engineering for additive manufacturing. In: Shamsaei, N., Hrabe, N., and Seifi, M. (eds), Progress in Additive Manufacturing 2021, 29-36. ASTM International.
• Tahmina, T., Garcia, M., Geng, Z., and Bidanda, B. (2022) A survey of smart manufacturing for high-mix low-volume production in defense and aerospace industries. In: Kim, K. Y., Monplasir, L., and Rickli, J. (eds), Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus (Lecture Notes on Mechanical Engineering), 237-245. Springer, Cham.
• Geng, Z., and Bidanda, B. (2022) Additive manufacturing of dentures, crowns, and bridges. In: Narayan, R. J. (eds), The ASM Handbook, Vol. 23A, Additive Manufacturing in Medicine, 97-110. Springer, Cham.
• Geng, Z., and Bidanda, B. (2021) Medical applications of additive manufacturing. In: Bártolo, P. J. and Bidanda, B. (eds), Bio-Materials and Prototyping Applications in Medicine, 97-110. Springer, Cham.

Conference Proceeding (7)

• Geng, Z., Bidanda, B., and Karunathilake, S. (2023) Reverse engineering: The other side of additive manufacturing. Cluj-Napoca: 27th International Conference on Production Research.
• Asare-Manu, V., Karunathilake, S., and Geng, Z. (2023) Image segmentation with human-in-the-loop in automated de-caking process for powder bed additive manufacturing. Boston: ASME 2023 International Design Engineering Technical Conference & Computers and Information in Engineering Conference (IDETC/CIE 2023).
• Geng, Z., and Bidanda, B. (2019) Volumetric data analysis: Inspection and experimental design for additive manufacturing. Pittsburgh: 34th American Society for Precision Engineering (ASPE) Annual Meeting.
• Bidanda, B., Geng, Z., and Motavalli, S. (2017) Modeling techniques in reverse engineering. Poznan: 24th International Conference on Production Research.
• Bidanda, R., Winakor, J., Geng, Z., and Vidic, N. (2017) A review of optimization models for boarding a commercial airplane. Poznan: 24th International Conference on Production Research.
• Bidanda, B., and Geng, Z. (2016) Emerging trends in reverse engineering. Singapore: 2nd Conference on Progress in Additive Manufacturing.
• Geng, Z., Haight, J., and Schwaderer, W. (2016) Current research – Safety & health management system performance measurement. Atlanta: The ASSE Safety 2016 Professional Development Conference & Exposition 2016.