Modeling of surface roughness in precision machining of metal matrix composites using ANN

By Abeesh C. Basheer, U. Dabade, S. Joshi, V. Bhanuprasad, V. Gadre. 2008

152 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/0b96e05ba430e4ea2eb5740f7b31e8a9714b0c78

How researchers see this paper

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

In Mechanical Engineering

1. Safian Sharif
2. T. Rajmohan
3. Azlan Mohd Zain
4. Habibollah Haron
5. S. Ranganathan
6. P. Suresh
7. K. Marimuthu

Citations in the corpus

• Prediction of surface roughness in the end milling machining using Artificial Neural Network (2010, methodology): “1 Abeesh et al. (2008) Turning Feed rate, particle size, tool nose radius 2 Erzurumlu and Oktem (2007) Milling Feed rate, cutting speed, axial–radial depth of cut, machining tolerance 3 Davim et al.”
• Optimization of machining parameters in turning of Al−SiC−Gr hybrid metal matrix composites using grey-fuzzy algorithm (2014, background): “BASHEER et al [7] reported that in precision machining of MMCs, the roughness of the machined surface is significantly influenced by the size of particles.”
• 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, unknown): “[44],”