This paper presents an investigation into the MQL (minimum quantity lubrication) and wet turning processes of AISI 1045 work material with the objective of suggesting the experimental model in order to predict the cutting force and surface roughness, to select the optimal cutting parameters, and to analyze the effects of cutting parameters on machinability. Fractional factorial design and central composite design were used for the experiment plan. Cutting force and surface roughness according to cutting parameters were measured through the external cylindrical turning based on the experiment plan. The measured data were analyzed by regression analysis and verification experiments were conducted to confirm the results. From the experimental results and regression analysis, this research project suggested the experimental equations, proposed the optimal cutting parameters, and analyzed the effects of cutting parameters on surface roughness and cutting force in the MQL and wet turning processes.
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This paper was recommended for publication in revised form by Associate Editor Dae-Eun Kim
Young-Kug Hwang received his Ph.D. in Mechanical Design and Manufacturing Engineering from Changwon National University in 2010. His research interests include high speed spindle design, variable preload technology of machine tool and environmental conscious machining.
Choon-Man Lee received his Ph.D. degree in Production Engineering from KAIST in 1989. Dr. Lee is currently a Professor at the Department of Mechanical Design and Manufacturing Engineering at Changwon National University. His research interests include plastic working, computer aided manufacturing, machine tool and production engineering.
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Hwang, Y.K., Lee, C.M. Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments. J Mech Sci Technol 24, 1669–1677 (2010). https://doi.org/10.1007/s12206-010-0522-1
- MQL turning
- Optimal cutting parameters
- Fractional factorial design
- Central composite design
- Surface roughness and cutting force prediction