Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm
By H. Öktem, Tuncay Erzurumlu, F. Erzincanli. 2006
282 citations recorded by the source corpus.
Subfields: Computational Mechanics, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/d1d39d44f02efeb9b71a49f6bf6d01dc4451e17b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Safian Sharif
- Azlan Mohd Zain
- P. Sahoo
- Habibollah Haron
- Bharat Chandra Routara
- Vishal S. Sharma
- Asish Bandyopadhyay
- Munish Kumar Gupta
In Mechanical Engineering
- Munish Kumar Gupta
- Safian Sharif
- Azlan Mohd Zain
- P. Sahoo
- Habibollah Haron
- Bharat Chandra Routara
- Vishal S. Sharma
- Asish Bandyopadhyay
Citations in the corpus
- Prediction of surface roughness in the end milling machining using Artificial Neural Network (2010, background): “(2006) Not stated 81 Not stated Not stated Feedforward BP logsig: logsig traingdx Not stated Not stated 13 Oktem et al. (2006) 5–42–42–1 236 7 Not stated Feedforward BP tansig: tansig traingda, g 1⁄4 0:0001 learngd MSE 1760 A.”
- Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation (2009, methodology): “Oktem and co-workers [15, 16] analyzed the optimum cutting condition leading to a minimum roughness (Ra) in end milling by combining RSM with neural network and genetic algorithm for Al and plastic mold parts.”
- Machining Parameters Optimization of Titanium Alloy using Response Surface Methodology and Particle Swarm Optimization under Minimum-Quantity Lubrication Environment (2016, methodology): “Ocktem and Erzurumlu established a model for selecting the optimal parameters of surface roughness in milling operation utilizing NNs and genetic algorithms [23].”