DEPARTMENT.FACULTY
- DEPARTMENT_STAFF.QUALIFICATION
Ph.D., M.Tech., B.Tech.
- DEPARTMENT_STAFF.DESIGNATION
Assistant Professor
- DEPARTMENT_STAFF.THRUST_AREA
Manufacturing Automation, Adaptive control of manufacturing processes, Metal Cutting Operations, CNC Milling, Robotics and Automation, Minimum Quantity Lubrication, FEM of machining process, Machine Learning
- DEPARTMENT_STAFF.ADDRESS
Dept. of Mech. Engg. ZHCET, AMU, Aligarh
- DEPARTMENT_STAFF.MOBILE
9557103410
- DEPARTMENT_STAFF.EMAIL
muaziitk@gmail.com
- DEPARTMENT_STAFF.TIME_TABLE
Time Table 2024-25Time Table 2023-24 (Autumn Sem)Time Table 2022-23
Dr. Muhammed Muaz is working as an Assistant Professor in the department. His research areas include metal cutting operations, friction stir welding, manufacturing automation, adaptive control of manufacturing processes, robotics and automation, cutting fluids, minimum quantity lubrication, FEM of machining, powder metallurgy, materials characterization, optimization of manufacturing processes and Machine Learning.
He has published several research articles including journal papers, conference papers, and book chapters.
One of his patents is GRANTED and two are published.
His edited book entitled 'Smart Systems: Methodological Approaches and Applications' has been published by CRC Press, Taylor & Francis Group.
- Smart Systems: Methodological Approaches and Applications, Edited Book
- Thermal Analysis of Micro-Channel Internal Cooling in Cutting Tools: A Machine Learning Approach
- Enhancing tribo-rheological performance of solid lubricants mixed bio-based emulsions applied through minimum quantity cooling lubrication technique.
- Failure mechanics analysis of AISI 4340 steel using finite element modeling of the milling process
The Journal of Strain Analysis for Engineering Design, 57 (7), 2022.
- Experimental investigations and multi-objective optimization of MQL-assisted milling process for finishing of AISI 4340 steel
- A realistic 3D finite element model for simulating multiple rotations of modified milling inserts using coupled temperature-displacement analysis
Int. J. Adv. Manuf. Technol. (2020). 107, pp-343-354.
https://link.springer.com/article/10.1007/s00170-020-05085-4
- Enhancing the tribological aspects of machining operation by hybrid lubrication assisted side flank face laser textured milling insert
J. Brazilian Soc. Mech. Sci. Eng. 41 (2019) 1–11.
- Machine learning enabled prediction of tribological properties of Cu-TiC-GNP nanocomposites synthesized by electric resistance sintering: A comparison with RSM