Intelligent tool wear estimation system through artificial neural networks and fuzzy modeling

Artificial Intelligence in Engineering

Volumn 12, Issue 3, 1998, Pages 229-242

  Kuo, R J ^a   Cohen, P H ^b  

^a NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Artificial neural networks; Fuzzy logic; Fuzzy neural network; Tool wear

Indexed keywords

ALGORITHMS; BACKPROPAGATION; CUTTING TOOLS; DATA ACQUISITION; FEATURE EXTRACTION; FUZZY SETS; MATHEMATICAL MODELS; METAL CUTTING; ONLINE SYSTEMS; PATTERN RECOGNITION; SENSOR DATA FUSION; WEAR OF MATERIALS;

FUZZY NEURAL NETWORKS; TOOL WEAR;

NEURAL NETWORKS;

EID: 0032122963     PISSN: 09541810     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0954-1810(97)00027-7     Document Type: Article

References (25)

1. Tansel, I. N., Neural Network approach for representation and simulation of 3D-cutting dynamics. In: Transactions of NAMRI/SME, 1990, pp. 193-200.
2. Tansel, I. N. & Laughlin, C. M., On-line monitoring of tool breakage with unsupervised neural networks. In: Transactions of NAMRO/SME, 1991, pp. 364-370.
3. Guillot, M. & Ouafi, A. E., On-line identification of tool breakage in metal cutting processes by use of neural networks. In: Proc. of ANNIE'91, pp. 701-709.
4. Malakooti, B. & Zhou, Y., An applications of adaptive neural networks for an in-process monitoring and supervising system. In: Proc. of IJCNN'92, pp. II-534-II-539.
5. Khanchustambham, R. G. & Zhang, G. M., A neural network approach to on-line monitoring of a turning process. In: Proc. of IJCNN'92, pp. II-889-II-894.
6. Elanayar, S. & Shin, Y. C., Robust tool wear estimation via radial basis function neural networks. In: Proc. of The Winter Annual Meeting of the American Society of Machanical Engineers, 1992, pp. 37-47.
7. Rangwala, S. & Dornfeld, D., Integration of sensors via neural networks for detection of tool wear states. In: Proc. of the Winter Annual Meeting of the ASME, PED 25, 1987, pp. 109-120.
8. Rangwala, S. & Dornfeld, D., Operations using computing abilities of neural networks, IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(2), 299-314.
9. Rangwala, S. & Dornfeld, D. Sensor integration using neural networks for intelligent tool condition monitoring. Journal of Engineering for Industry, 1990, 112, 219-228.
10. Chryssolouris, G. & Domroese, M., Sensor integration for tool wear estimation in machining. In: Proc. of the Winter Annual Meeting of the ASME, Symposium on Sensors and Controls for Manufacturing, 1988, pp. 115-123.
11. Chryssolouris, G. & Domroese, M. An experimental study of strategies for integrating sensor information in machining. Annals of the CIRP, 1989, 38, 425-428.
12. Chryssolouris, G., Domroese, M. & Beaulieu, P., A statistical approach to sensor synthesis. In: Transactions of the North American Manufacturing Research Institution of SME, May 1991, pp. 333-337.
13. Chryssolouris, G., Domroese, M. & Beaulieu, P. Sensor synthesis for control of manufacturing processes. ASME Journal of Engineering for Industry, 1992, 114, 158-174.
14. Masory, O., Detection of tool wear using multi-sensor readings defined by artificial neural network. In: Proc. of SPIE: Applications of Artificial Neural Networks II, 1991, Vol. 1469, pp. 515-520.
15. Kamarthi, S. V., Sankar, G. S., Cohen, P. H. & Kumara, S. R. T., On-line tool wear monitoring using a kohonen's feature map. In: Proc. of ANNIE'91, pp. 639-644.
16. Leem, C. S. & Dreyfus, S. E., Learning input feature selection for sensor fusion in tool wear monitoring. In: Proc. of ANNIE'92, pp. 815-820.
17. Tansel, I. N., Identification of the pre-failure phase in microdrilling operations with multiple sensors. In: Proc. of The Winte Annual Meeting of the american Society of Mechanical Engineers, 1992, pp. 23-36.
18. Ko, T. J. & Cho, D. W. Tool wear monitoring in diamond turning by fuzzy pattern recognition. ASME Journal of Engineering for Industry, 1994, 116, 225-232.
19. Kuo, R. J., Chen, Y. T., Cohen, P. H. & Kumara, S., Fast convergence of error backpropagation algorithm through fuzzy modeling. In: Proc. of ANNIE'93, pp. 239-244.
20. Luo, R. C. & Kay, M. G., Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(5), 901-931.
21. Kuo, R. J., Cohen, P. H. & Kumara, S. R. T., Neural network driven fuzzy inference system. In: Proc. of IEEE International Conference on Neural Networks, 1994, pp. 2234-2238.
22. Wang, L.-X. & Mendel, J. M., Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: IEEE International Conference on Fuzzy Systems, 1992, pp. 1409-1418.
23. Takagi, T. & Sugeno, M., Derivation of fuzzy control rules from human operator's control actions. In: Proc. of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, Marseilles, France, 1983, pp. 55-60.
24. Kendall, S. M. & Ord, K., Time Series, Oxford University Press, New York, 3rd Edition, 1990.
25. Kuo, R. J., Multi-sensor integration for intelligent control of machining through artificial neural networks and fuzzy modelling. Ph.D. Dissertation, The Pennsylvania State University, 1994.