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Drill wear prediction using artificial neural network
| Content Provider | NIT Rourkela |
|---|---|
| Author | Singh, A. K. Panda, S. S. Chakraborty, D. Pal, S. K. |
| Description | The International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462 |
| Abstract | The present work deals with drill wear monitoring using artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high speed steel (HSS) drill bit for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feeed-rate, spindle speed, drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data and found to be satisfactory. |
| File Size | 1245184 |
| File Format | DOC / DOT |
| ISSN | 02683768 14333015 |
| DOI | 10.1007/s00170-004-2376-0 |
| Language | English |
| Publisher | Springer |
| Access Restriction | Open |
| Subject Keyword | Flant Wear Artificial Neural Network Drilling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software |
