Main Content

Mozammel Mia

Mozammel Mia, Assistant Professor

Dr. Mozammel Mia is an Assistant Professor in the School of Applied Engineering & Technology, primarily serving the Industrial Management & Applied Engineering (IMAE) and Quality Engineering & Management (QEM) programs. His research is centered on developing and deploying intelligent systems for Smart Manufacturing and Service Operations focusing on the synergistic integration of digital technologies with complex managerial/decision challenges.

Research

The core objective of Dr. Mia’s research is to bridge the gap between advanced analytics and real-world managerial/decision systems in both production and service environments. His work is driven by the aim of achieving system-level optimization, enhanced resilience, and sustainable quality. 

His research expertise is concentrated in the following key areas: (1) Manufacturing and Service Optimization: Developing models and analytical frameworks for improving efficiency, resource allocation, and operational performance across both physical production and service delivery systems, (2) Predictive and Prescriptive Analytics: Leveraging techniques like Artificial Intelligence (AI) and Machine Learning (ML) to forecast system behavior (e.g., quality defects, demand fluctuations) and prescribe optimal managerial actions, (3) Supply Chain and Logistics Management: Analyzing, modeling, and optimizing complex global and regional supply networks, with a focus on risk management, resilience modeling, and the impact of digitalization on sustainability and logistics, (4) Quality Management: Advancing the field of quality through digital quality protocols and zero-defect management, applying advanced data-driven methods to ensure robust quality in both manufactured products (e.g., aerospace) and service experiences (e.g., airlines).

The research methodology integrates computational modeling, simulative analysis, and the application of data-driven analytical techniques - that draw multi-disciplinary concentrations of data science, systems engineering, operations research, and behavioral economics.

Learn more about Lab of MIA

Mozammel Mia

Office: Engineering D0121
Phone: 419-494-8376
mozammel.mia@siu.edu
Google Scholar

Education

Ph.D., Imperial College London, UK, 2023

M.Sc., Industrial and Production Engineering, Bangladesh University of Engineering and Technology, 2015

B.Sc., Industrial and Production Engineering, Bangladesh University of Engineering and Technology, 2012

Teaching

  • IMAE 480: Six Sigma Black Belt
  • QEM 515: Six Sigma Black Belt
  • QEM 535: Service Quality
  • QEM 510: Quality Assurance
  • QEM 530: Lean Manufacturing II

Services

  • 2025 – 2025, Postdoctoral Research Fellow, Bowling Green State University, OH
  • 2024 – 2025, Associate Professor, Brac University, Bangladesh
  • 2023 – 2024, Assistant Professor, Ahsanullah University of Science and Technology, Bangladesh
  • 2019 – 2023, Research Postgraduate, Imperial College London, UK
  • 2015 – 2019, Assistant Professor, Ahsanullah University of Science and Technology, Bangladesh
  • 2012 – 2015, Lecturer, Ahsanullah University of Science and Technology, Bangladesh

Selected Publications

  • Mia, M., Anwar, S., & Yang, X. (2025). Finite element modeling of machining with interactive friction model based evolutionary friction. Journal of Manufacturing Processes, 148, 61-74.
  • Mia, M., Anwar, S., & Yang, X. (2023). Development of interactive friction model for machining considering the instantaneous interfacial characteristics. Journal of Materials Processing Technology, 322, 118203.
  • Mia, M., Zhang, L., Anwar, S., & Liu, H. (2023). Development of digital characteristics of machining based on physics-guided data. Journal of Manufacturing Systems, 71, 438-450.
  • Pimenov, D. Y., Mia, M., Gupta, M. K., Machado, Á. R., Pintaude, G., Unune, D. R., ... & Kuntoğlu, M. (2022). Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects. Renewable and Sustainable Energy Reviews, 166, 112660.
  • Sen, B., Hussain, S. A. I., Gupta, M. K., Mia, M., & Mandal, U. K. (2021). Swarm intelligence based selection of optimal end-milling parameters under minimum quantity nano-green lubricating environment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235(23), 6969-6983.
  • Sato, B. K., Lopes, J. C., Rodriguez, R. L., Garcia, M. V., Mia, M., Ribeiro, F. S. F., ... & Bianchi, E. C. (2022). Novel comparison concept between CBN and Al2O3 grinding process for eco-friendly production. Journal of Cleaner Production, 330, 129673.
  • Ross, N. S., Mia, M., Anwar, S., Saleh, M., & Ahmad, S. (2021). A hybrid approach of cooling lubrication for sustainable and optimized machining of Ni-based industrial alloy. Journal of Cleaner Production, 321, 128987.
  • Gupta, M. K., Song, Q., Liu, Z., Sarikaya, M., Mia, M., Jamil, M., ... & Kuntoğlu, M. (2021). Tribological performance based machinability investigations in cryogenic cooling assisted turning of α-β titanium alloy. Tribology International, 160, 107032.
  • Rahman, M. A., Rahman, M., Woon, K. S., & Mia, M. (2021). Episodes of chip formation in micro-to-nanoscale cutting of Inconel 625. International Journal of Mechanical Sciences, 199, 106407.
  • Giasin, K., Dad, A., Brousseau, E., Pimenov, D., Mia, M., Morkavuk, S., & Koklu, U. (2021). The effects of through tool cryogenic machining on the hole quality in GLARE® fibre metal laminates. Journal of Manufacturing Processes, 64, 996-1012.
  • Khan, A. M., Liang, L., Mia, M., Gupta, M. K., Wei, Z., Jamil, M., & Ning, H. (2021). Development of process performance simulator (PPS) and parametric optimization for sustainable machining considering carbon emission, cost and energy aspects. Renewable and Sustainable Energy Reviews, 139, 110738.
  • Bustillo, A., Pimenov, D. Y., Mia, M., & Kapłonek, W. (2021). Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth. Journal of Intelligent Manufacturing, 32(3), 895-912.
  • Manimaran, G., Anwar, S., Rahman, M. A., Korkmaz, M. E., Gupta, M. K., Alfaify, A., & Mia, M. (2021). Investigation of surface modification and tool wear on milling Nimonic 80A under hybrid lubrication. Tribology International, 155, 106762.
  • Mia, M., & Dhar, N. R. (2018). Effects of duplex jets high-pressure coolant on machining temperature and machinability of Ti-6Al-4V superalloy. Journal of Materials Processing Technology, 252, 688-696.
  • Khan, A. M., Gupta, M. K., Hegab, H., Jamil, M., Mia, M., He, N., ... & Pruncu, C. I. (2020). Energy-based cost integrated modelling and sustainability assessment of Al-GnP hybrid nanofluid assisted turning of AISI52100 steel. Journal of Cleaner Production, 257, 120
  • Mia, M., Gupta, M. K., Lozano, J. A., Carou, D., Pimenov, D. Y., Królczyk, G., ... & Dhar, N. R. (2019). Multi-objective optimization and life cycle assessment of eco-friendly cryogenic N2 assisted turning of Ti-6Al-4V. Journal of cleaner production, 210, 121-133.
  • Mia, M., Gupta, M. K., Singh, G., Królczyk, G., & Pimenov, D. Y. (2018). An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions. Journal of Cleaner Production, 187, 1069-1081.