Multiple regression and neural networks analyses in composites machining

Composites Science and Technology

Volumn 63, Issue 3-4, 2003, Pages 539-548

  Lin, J T ^a   Bhattacharyya, D ^a   Kecman, V ^a  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

A. Metal matrix composites; B. Wear; C. Statistics; Neural networks

Indexed keywords

ALUMINUM; MACHINING; NEURAL NETWORKS; REGRESSION ANALYSIS;

COMPOSITES MACHINING;

COMPOSITE MATERIALS;

MACHINE TOOL; NEURAL NETWORK; WEAR;

EID: 0037303115     PISSN: 02663538     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0266-3538(02)00232-4     Document Type: Article

References (24)

1. Bhattacharyya D, Bowis ME, Gregory JT. The influence of alumina microsphere reinforcement on the mechanical behaviour and weldability of a 6061 aluminium metal matrix composite. In: Srivatsan TS, Bowden DM, editors. Machining of composite materials. Materials Park, (OH, USA): ASM International; 1992. p. 49-56.
2. Couper MJ, Xia K. Development of microsphere reinforced metal matrix composites. In: Hansen N et al., editors. Proceedings 12th Riso Int. Symposium on Materials Science. Denmark:Roskilde, 1991, p. 291-8.
3. Lane C. Machinability of aluminium composite as a function of matrix alloy and heat treatment. In: Srivatsan TS, Bowden DM, editors. Machining of composite materials. Materials Park (OH, USA): ASM International; 1992. p. 3-16.
4. Lane C. The effect of different reinforcements on PCD tool life for aluminium composites. In: Srivatsan TS, Bowden DM, editors. Machining of composite materials. Materials Park, (OH, USA): ASM International; 1992. p. 17-27.
5. Tomac N, Tøonnessen K. Machinability of particulate aluminium matrix composites. Annals of the CIRP 1992;41(1):55-8.
6. Cronjäger L, Meister D. Machining of fibre and particle-reinforced aluminium. Annals of the CIRP 1992;41(1):63-6.
7. Lin JT, Bhattacharyya D, Lane C. Machinability of a silicon carbide reinforced aluminium metal matrix composite. Wear 1995;181-183:883-8.
8. Bishop CM. Neural networks and their applications. Review of Scientific Instruments 1994;65(6):1803-32.
9. Kecman V. Learning and soft computing. Cambridge (MA, USA): The MIT Press; 2000.
10. Das S, Bandyopadhyay PP, Chattopadhyay AB. Neural-networks-based tool wear monitoring in turning medium carbon steel using a coated carbide tool. Journal of Materials Processing Technology 1997;63(1-3):187-92.
11. Kuo RJ, Cohen PH. Intelligent tool wear estimation system through artificial neural networks and fuzzy modelling. Artificial Intelligence in Engineering 1998;12:229-42.
12. Kuo RJ, Cohen PH. Manufacturing process control through integration of neural networks and fuzzy model. Fuzzy Sets and Systems 1998;98:15-31.
13. Ghasempoor A, Moore TN, Jeswiet J. On-line wear estimation using neural networks. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 1998;212(B2):105-12.
14. Liu Q, Altintas Y. On-line monitoring of tool wear in turning using a neural network. International Journal of Machine Tools and Manufacture 1999;39(3):489-504.
15. Liu Q, Altintas Y. On-linemonitoring of flank wear in turning with multilayered feed-forward neural network. International Journal of Machine Tools and Manufacture 1999;39(12):1945-59.
16. Dimla Sr DE, Lister PM. On-line metal cutting tool condition monitoring II: tool-state classi.cation using multi-layer perceptron neural networks. International Journal of Machine Tools and Manufacture 2000;40(5):769-81.
17. Zawada-Tomkiewicz A. Classifying the wear of turning tools with neural networks. Journal of Materials Processing Technology 2001;109(3):300-4.
18. Sick B. Tool wear estimation in turning with process-specific preprocessing and time-delay neural networks. International Journal of Smart Engineering System Design 2001;3(1):1-14.
19. Elanayar SVT, Shin YC. Design and implementation of tool wear monitoring with radial basis function neural networks. In: Proceedings of the 1995 American Control Conference. Part 3 (of 6), 1995. p. 1722-26.
20. Elanayar SVT, Shin YC. Robust tool wear estimation via radial basis function neural networks. American Society of Mechanical Engineers, Production Engineering Division (Publication) PED: 1992. p. 37-51.
21. Kuo RJ, Cohen PH. Multi-senor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network. Neural Networks 1999;12:155-370.
22. Shah FF. Radial basis function approach to financial time series modelling. Master thesis, The University of Auckland, 1998.
23. Sarle WS ed., Neural network FAQ: Part 1 of 7-Introduction, periodic posting to the Usenet newsgroup comp.ai.neural-nets. Available from: ftp://ftp.sas.com/pub/neural/FAQ.html, 1997.
24. Swider DJ, Kecman V, Browne MW, Bansal PK. Steady-state modelling of vapour-compression liquid chillers with neural networks. Internal report. The University of Auckland, 1998.