Name
Abstract | ||
|---|---|---|
In this work, a back propagation neural network model has been developed for the prediction of surface roughness in turning operation. A large number of experiments were performed on mild steel work-pieces using high speed steel as the cutting tool. Process parametric conditions including speed, feed, depth of cut, and the measured parameters such as feed and the cutting forces are used as inputs to the neural network model. Roughness of the machined surface corresponding to these conditions is the output of the neural network. The convergence of the mean square error both in training and testing came out very well. The performance of the trained neural network has been tested with experimental data, and found to be in good agreement. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s00521-005-0468-x | Neural Computing and Applications |
Keywords | DocType | Volume |
Turning,Surface roughness,Artificial neural network | Journal | 14 |
Issue | ISSN | Citations |
4 | 0941-0643 | 9 |
PageRank | References | Authors |
0.90 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Surjya K. Pal | 1 | 58 | 9.70 |
| Debabrata Chakraborty | 2 | 27 | 3.55 |