Surface roughness prediction using artificial neural networks when drilling UDIMET 720

Procedia Engineering

Volumn 48, Issue , 2012, Pages 693-700

  Vrabel, Marek ^a   Mankova, Ildiko ^a   Beno, Jozef ^a   Tuharský, Jaroslav ^a  

^a TECHNICAL UNIVERSITY OF KO ICE   (Slovakia)

Author keywords

Drilling; Monitoring strategy; Neural networks; Surface roughness

Indexed keywords

EID: 84888318510     PISSN: 18777058     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.proeng.2012.09.572     Document Type: Conference Paper

References (14)

1. Eckstein, M, Mankova, I., 2012. Monitoring of Drilling Process for Highly Stressed Aeroengine Components. 5th CIRP Conference on High Performance Cutting (HPC 2012) Zurich Procedia CIRP 1, pp. 604-609.
2. Klocke F, Gierlings S, Brockmann M, Sage C, Veselovac D., 2010 Adaptive Control of Manufacturing Processes for a New Generation of Jet Engine Components.7th CIRP - ICME Capri, pp.321
3. Masory Y., 1991. Monitoring machining processes using multisensor readings fused by artificial neural networks, in: 7th International Conference on Computer aided Production Engineering, vol. 28, pp. 231-240.
4. Choi Y. J., Park M. S., Chu C. N., 2008. Prediction of drill failure using feature extraction in time and frequency domain of feed muter current, Int. J. Machine Tool Manuf. 48, 29-39
5. Wang Z., Dornfeld D., 1992. In process tool wear monitoring using neural networks, in: Proceedings of the Japan-USA Symposium on Flexible Automation, pp. 263-270
6. Rahaman M., Zhou Q., 1995. Online cutting state recognition using a neural network, Int. J. Adv. Manuf. Technol. 2, p.87-92.
7. Choudhury S.K., Jain V.K., 1999. Online monitoring of tool wear in turning using a neural network, Int. J.Machine Tools Manuf. 39, p. 489.
8. Lin S.C. Ting C.J., 1995. Tool wear monitoring in drilling using force signals, Wear 180, p. 53-60
9. Abbu-Mahfouz I., 2003. Drilling wear detection and classification using vibration signals and artificial neural network, Int. J. Machine Tool Manuf. 43, p. 707-720.
10. Singh A.K., Panada S.S., Pal S.K., Chakraborty D., 2006. Predicting drill wear using an artificial neural network, Int. J. Adv. Manuf. Technol. 28, p. 456-462.
11. Panada S.S., Singh A.K., Chakraborty D., Pal S.K., 2006. Drill wear monitoring using back propagation neural network, J. Mater. Process. Technol. 172, p. 283-290.
12. Asilturk, I., 2012. Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural network and multiple regression, International Journal of Advanced Manuf. Technol. Vol 32 Accepted in press.
13. Dutilh V., Dessein G., Alexis J., Perrin G., 2010. Links between machining parameters and surface integrity in drilling Ni-superalloy, International Journal of Advanced Materials Research, vol.112, pp171-178.
14. Popa A., Dessein G., Baili M., Dutilh V., 2012. Investigation of Tool Failure Modes and Machining Disturbances Using Monitoring Signals, International Journal of Advanced Materials Research, vol. 423, pp128-142.