M
Mozammel Mia
Researcher at Imperial College London
Publications - 148
Citations - 6848
Mozammel Mia is an academic researcher from Imperial College London. The author has contributed to research in topics: Machining & Surface roughness. The author has an hindex of 38, co-authored 148 publications receiving 3967 citations. Previous affiliations of Mozammel Mia include Ahsanullah University of Science and Technology.
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Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool
TL;DR: In this paper, the analysis of average surface roughness, cutting force, and feed force in turning of difficult-to-machine Ti-6Al-4V alloy by experimental investigation and performance modeling is presented.
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Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method
TL;DR: In this paper, a detailed step-by-step study of Response Surface Methodology (RSM) and Taguchi based models revealed compatible results, thereby justified their acceptability.
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Performance Evaluation of Vegetable Oil-Based Nano-Cutting Fluids in Environmentally Friendly Machining of Inconel-800 Alloy.
Munish Kumar Gupta,Muhammad Jamil,Xiaojuan Wang,Qinghua Song,Zhanqiang Liu,Mozammel Mia,Hussein Hegab,Aqib Mashood Khan,Alberto Garcia Collado,Catalin I. Pruncu,G.M. Shah Imran +10 more
TL;DR: The composite desirability approach (CDA) was successfully implemented to determine the ideal machining parameters under different nano-cutting cooling conditions and demonstrates that the MoS2 and graphite-based nanofluids give promising results at high cutting speed values, but the overall performance of graphite -based nan ofluids is better in terms of good lubrication and cooling properties.
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Investigations of Machining Characteristics in the Upgraded MQL-Assisted Turning of Pure Titanium Alloys Using Evolutionary Algorithms
Gurraj Singh,Catalin I. Pruncu,Munish Kumar Gupta,Mozammel Mia,Aqib Mashood Khan,Muhammad Jamil,Danil Yurievich Pimenov,Binayak Sen,Vishal S. Sharma +8 more
TL;DR: It was shown that RHVT improved the results by nearly 15% for all of the responses, while the TLBO technique was found to be the best optimization technique, with an average time of 1.09 s and a success rate of 90%.
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Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network
Mozammel Mia,Mashrat Hasan Razi,Istiaq Ahmad,Rafid Mostafa,Sheikh M. S. Rahman,Dewan Hasan Ahmed,Prithbey Raj Dey,Nikhil Ranjan Dhar +7 more
TL;DR: In this article, the average surface roughness parameter has been investigated in turning of hardened steel of 600 BHN with uncoated carbide under the application of minimum quantity lubrication.