Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models
By J. Paulo Davim, V. Gaitonde, S. Karnik. 2008
283 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/dfa7cb07af71d0ed6967d06084d8e34abd0e2390
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Electrical and Electronic Engineering
In Mechanical Engineering
Citations in the corpus
- Mathematical modeling for turning on AISI 420 stainless steel using surface response methodology (2015, background): “Usually, turning operation produces the components that have critical features and that entail specific surface finish.(2) When considering product manufacturing process, both optimization and modeling are carefully taken into account as two key issues.”
- Prediction of surface roughness in the end milling machining using Artificial Neural Network (2010, background): “The limitations of ANN in the machining process modelling could be stated as follows (Davim et al., 2008; Zuperl et al., 2006): (i) Empirical experiences are necessary in creating a realistic network.”
- Comparison of tool life between ceramic and cubic boron nitride (CBN) cutting tools when machining hardened steels (2009, background): “The artificial neural network (ANN) model of surface roughness analysis by Davim et al.(2007) reveled that cutting speed and feed rate had significant effects in reducing the surface roughness of free machining steel.”