Surface roughness investigation in machining of Titanium alloy by round CVD coated inserts using artificial neural network |
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| S. Ramesh, L. Karunamoorthy, K. Palanikumar |
- Abstract:
- Productivity and quality in the finish turning of titanium alloys can be improved by utilizing predicted performance of the work surface. This paper combines predictive machining approach with neural network modeling of surface roughness in order to estimate performance of chemical vapor deposition (CVD) coated (TiN-TiCN-Al2O3-TiN) round carbide inserts (Grade TT3500) for a variety of cutting conditions. Machining trails were conducted in lathe. The control parameters used were cutting speed, feed, and depth of cut. Machining trails were designed using the statistical design of experiment (DoE) techniques. Surface roughness have been measured for each operation and the associated data have been used to train an artificial neural network (multi-layer perceptron) using the back-propagation algorithm. The trained neural network has been used to predict the surface quality in terms of surface roughness. The developed prediction model was found to be capable of accurate surface roughness classification for the range it had been trained.
- Keywords:
- Turning Titanium alloy; CVD coated Round insert; Surface roughness; Artificial Neural networks (ANN)
- Download:
- IMEKO-TC14-2007-09.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC14
- Event name:
- TC14 ISMQC 2007
- Title:
9th Symposium on Measurement and Quality Control in Manufacturing
- Place:
- Chennai/Madras, INDIA
- Time:
- 21 November 2007 - 24 November 2007