Development of Empirical Models for Surface Roughness Prediction in Finish Turning

By Xiaowen Wang, C. Feng. 2002

394 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a5846d50072907d82ecebd798521f9a280a9ca91

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In Industrial and Manufacturing Engineering

In Mechanical Engineering

Citations in the corpus

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization (2012, background): “Feng and Wang [15] presented an investigation for the prediction of surface roughness in finish turning operations by developing an empirical model considering various parameters: workpiece hardness, feed rate, cutting tool point angle, depth of cut, spindle speed, and cutting time.”
Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels (2009, methodology): “Feng and Wang (2002) used regression analysis to develop a complete empirical model of sur ace roughness, using feed rate, workpiece hardness, tool point angle, depth of cut, spindle sp ed, and cutting time.”
Taguchi method based optimisation of drilling parameters in drilling of AISI 316 steel with PVD monolayer and multilayer coated HSS drills (2012, background): “Surface roughness is a critical quality indicator for a machined surface and has a bearing on many properties such as: wear resistance, fatigue strength, coefficient of friction, lubrication, wear rate and the corrosion resistance of the machined parts [13].”
The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks (2009, result): “Similar investigations have also been made by some researchers [15–20].”
Predicting surface roughness in machining: a review (2003, background): “Feng and Wang [27] included a total of six parameters, namely the workpiece hardness, feed, tool point angle, depth of cut, spindle speed and cutting time to build a model for finish turning operations.”
Optimization of turning parameters for surface roughness (2015, background): “It also depends on the several other exogenous factors such as: work piece and tool material combination and their mechanical properties, quality and type of the machine tool used, auxiliary tooling, lubricant used and vibrations between the work piece, machine tool and cutting tool [2-8].”
Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks (2005, methodology): “Feng and Wang [19] conducted testing and used regression analysis to develop a complete empirical model of surface roughness for traditional turning.”