Mozammel Mia's headshot

Mozammel Mia

Assistant Professor
mozammel.mia@siu.edu
419-494-8376
Engineering D0121
Industrial Management and Applied Engineering, Quality Engineering and Management
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

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

Education

Ph.D., Imperial College London, 2023

 

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.