Prediction of surface roughness and cutting force under MQL turning of AISI 4340 with nano fluid by using response surface methodology
PB Patole, VV Kulkarni - Manufacturing Review, 2018 - mfr.edp-open.org
Manufacturing Review, 2018•mfr.edp-open.org
This paper presents an investigation into the minimum quantity lubrication mode with nano
fluid during turning of alloy steel AISI 4340 work piece material with the objective of
experimental model in order to predict surface roughness and cutting force and analyze
effect of process parameters on machinability. Full factorial design matrix was used for
experimental plan. According to design of experiment surface roughness and cutting force
were measured. The relationship between the response variables and the process …
fluid during turning of alloy steel AISI 4340 work piece material with the objective of
experimental model in order to predict surface roughness and cutting force and analyze
effect of process parameters on machinability. Full factorial design matrix was used for
experimental plan. According to design of experiment surface roughness and cutting force
were measured. The relationship between the response variables and the process …
This paper presents an investigation into the minimum quantity lubrication mode with nano fluid during turning of alloy steel AISI 4340 work piece material with the objective of experimental model in order to predict surface roughness and cutting force and analyze effect of process parameters on machinability. Full factorial design matrix was used for experimental plan. According to design of experiment surface roughness and cutting force were measured. The relationship between the response variables and the process parameters is determined through the response surface methodology, using a quadratic regression model. Results show how much surface roughness is mainly influenced by feed rate and cutting speed. The depth of cut exhibits maximum influence on cutting force components as compared to the feed rate and cutting speed. The values predicted from the model and experimental values are very close to each other.
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