Abstract
An Open-Architecture Machine Tool, based on a Sun/VMEbus/C/Real-Time Unix operating system has been constructed to provide a “machining research control platform” for the execution, sensing and gauging of precision machining. The real-time control of cutting tool performance is being monitored with dynamometers and thermocouples in order to monitor the stress and temperature acting on the tool’s cutting edge. Initially, a closed-form engineering relationship between stress-temperature and speed-feed can be developed and used to adjust feed and speed so as to keep stress and temperature within safe but productive bounds. However, the control of the system, especially with a deteriorating tool due to wear, benefits from the application of a neural network. This approach “learns and updates” the relationship between speed-feed and stress-temperature over a broad range of operating conditions. Results for the cutting of steel with carbide tools are described.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
P.K. Wright and I. Greenfeld. Rapid Prototyping in an Open-Architecture Manufacturing System, Conference on Applications of Artificial Intelligence in Manufacturing, Boston, MA, July 1990, pp.3–28.
P.K. Wright, I. Greenfeld and E. Pavlakos. Tool Wear and Failure Monitoring on an Open-Architecture Machine Tool, American Society of Mechanical Engineers, Winter Annual Meeting, PED-Vol. 43 — Fundamental Issues in Machining, pp. 211–228, 1990.
C. Ghandi and M.F. Ashby. Fracture Mechanism Maps for Materials which cleave: FCC, BCC and HCP Metals and Ceramics, Acta Met., Vol. 27, 1979, pp. 1565–1602.
James S. Albus. Brains, Behavior, and Robotics. McGraw Hill, Peterborough, New Hampshire, 1981.
G. Boothroyd. Temperatures in Metal Cutting, Fundamentals of Metal Machining, Arnold, London 1965, pp. 39–53.
E.M. Trent. Metal Cutting, Butterworth, London, 1977.
D.W. Yen and P.K. Wright. Adaptive Control in Machining: A New Approach Based on the Physical Constraints of Tool Wear Mechanisms. ASME’s Journal of Engineering for Industry, Vol. 105, 1983, pp. 31–38.
P.K. Wright. Physical Models of Tool Wear for Adaptive Control in Flexible Machining Cells. Computer Integrated Manufacturing, ASME Special Bound Volume at the Boston Winter Annual Meeting, Vol. 105, pp. 31–38.
T.N. Loladze. Nature of Brittle Failure of Cutting Tool. Annals of the CIRP, Vol. 24, No. 1, 1975, pp. 13–16.
S. Kobayashi and E.G. Thomsen. Some observations on the Shearing Process in Metal Cutting. ASME’s Journal of Engineering for Industry, Vol. 81, 1959, pp.251–259.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Computational Mechanics Publications
About this chapter
Cite this chapter
Epstein, H.A., Wright, P.K. (1991). Intelligent Machine Tools: An Application of Neural Networks to the Control of Cutting Tool Performance. In: Rzevski, G., Adey, R.A. (eds) Applications of Artificial Intelligence in Engineering VI. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3648-8_39
Download citation
DOI: https://doi.org/10.1007/978-94-011-3648-8_39
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-85166-678-2
Online ISBN: 978-94-011-3648-8
eBook Packages: Springer Book Archive