A surface roughness prediction model for hard turning process

315 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Optimization of tool geometry parameters for turning operations based on the response surface methodology (2011, background): “Singh and Rao [19] studied the effects of cutting conditions and tool geometry on the surface roughness in the finish hard turning.”
Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool (2010, methodology): “The RSM was also adopted by Singh and Rao [16] whose studied the effects of cutting conditions and tool geometry on the surface roughness in the finish hard turning of the bearing steel (AISI 52100) with mixed ceramic tool.”
Heat transfer enhancement using nanofluids in an automotive cooling system (2014, unknown): “FM is a collection of mathematical and statistical techniques that are useful for the modeling and analysis of problems in which the response of interest is influenced by several variables and the objective is to optimize the response [26–29].”
Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method (2011, background): “Since the hard turning process contains many parameters, it is complex and difficult to select the appropriate cutting conditions and tool geometry for achieving the required surface quality (Singh & Rao, 2007).”