Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method
This study analyzes a variety of significant drilling conditions on aluminum oxide (with L18 orthogonal array) using a diamond drill. The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size. An orthogonal array, signal-to-noise (S/N) ratio, and analysi...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/304691 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551454935613440 |
|---|---|
| author | Kingsun Lee |
| author_facet | Kingsun Lee |
| author_sort | Kingsun Lee |
| collection | DOAJ |
| description | This study analyzes a variety of significant drilling conditions on aluminum oxide (with L18 orthogonal array) using a diamond drill. The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size. An orthogonal array, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to analyze the effects of these drilling parameters. The results were confirmed by experiments, which indicated that the selected drilling parameters effectively reduce the crack. The neural network is applied to establish a model based on the relationship between input parameters (spindle speed, feed rate, depth of cut, and diamond abrasive size) and output parameter (cracking area percentage). The neural network can predict individual crack in terms of input parameters, which provides faster and more automated model synthesis. Accurate prediction of crack ensures that poor drilling parameters are not suitable for machining products, avoiding the fabrication of poor-quality products. Confirmation experiments showed that neural network precisely predicted the cracking area percentage in drilling of alumina. |
| format | Article |
| id | doaj-art-3c395644b2ce43768f68cb3bc821842f |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-3c395644b2ce43768f68cb3bc821842f2025-02-03T06:01:27ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422015-01-01201510.1155/2015/304691304691Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi MethodKingsun Lee0Department of Mechanical Engineering, ChienKuo Technology University, Changhua 500, TaiwanThis study analyzes a variety of significant drilling conditions on aluminum oxide (with L18 orthogonal array) using a diamond drill. The drilling parameters evaluated are spindle speed, feed rate, depth of cut, and diamond abrasive size. An orthogonal array, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to analyze the effects of these drilling parameters. The results were confirmed by experiments, which indicated that the selected drilling parameters effectively reduce the crack. The neural network is applied to establish a model based on the relationship between input parameters (spindle speed, feed rate, depth of cut, and diamond abrasive size) and output parameter (cracking area percentage). The neural network can predict individual crack in terms of input parameters, which provides faster and more automated model synthesis. Accurate prediction of crack ensures that poor drilling parameters are not suitable for machining products, avoiding the fabrication of poor-quality products. Confirmation experiments showed that neural network precisely predicted the cracking area percentage in drilling of alumina.http://dx.doi.org/10.1155/2015/304691 |
| spellingShingle | Kingsun Lee Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method Advances in Materials Science and Engineering |
| title | Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method |
| title_full | Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method |
| title_fullStr | Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method |
| title_full_unstemmed | Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method |
| title_short | Prediction of Crack for Drilling Process on Alumina Using Neural Network and Taguchi Method |
| title_sort | prediction of crack for drilling process on alumina using neural network and taguchi method |
| url | http://dx.doi.org/10.1155/2015/304691 |
| work_keys_str_mv | AT kingsunlee predictionofcrackfordrillingprocessonaluminausingneuralnetworkandtaguchimethod |