Optimization of cutting Parameters and Prediction of Ra & MRR for machining of P20 Steel on CNC milling using Artificial Neural Networks
| Autor: | G. Sankaraiah, M. Vishnu Vardhan, M. Yohan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Materials science Artificial neural network business.industry 02 engineering and technology Structural engineering 021001 nanoscience & nanotechnology Nonlinear system Taguchi methods 020901 industrial engineering & automation Machining Multilayer perceptron Linear regression Surface roughness Orthogonal array 0210 nano-technology business |
| Zdroj: | Materials Today: Proceedings. 5:27058-27064 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2018.09.010 |
| Popis: | In this paper an attempt is made to predict Material Removal Rate and Surface roughness in CNC milling of P20 steel using Artificial Neural Networks (ANN). Taguchi’s L50 orthogonal array is used to design the experiments. The cutting parameters cutting speed, feed, axial depth of cut, radial depth of cut and nose radius is taken as input parameters and Material removal rate and Surface roughness are taken as output parameters. The ANN model is modelled using Multilayer Perceptron Network for nonlinear mapping between the input and output parameters. The developed model is verified using Regression coefficient(R) and it is found that R2 value is 1. From the results it is seen that ANN predicted values are close to the experimental values indicates that the developed model can be effectively used to predict the Material Removal Rate and Surface Roughness of P20 steel. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect