Considerable developments have taken place in recent years in the versatility and sophistication of computer numerical control (CNC) controllers. Machine tool control schemes have not, however, advanced to the same extent. Control of the metal cutting process has long been advocated as a goal to pursue to improve productivity, but progress has been slow.
This paper introduces a simplified generalized minimum variance approach to self-tuning which shows promise for the adaptive control of metal cutting. An experimental rig is described and the results of tests to adaptively control the vertical component of cutting force during rough turning operations by manipulating the feedrate are presented. A simulation of the rig was also developed and used as an initial testbed for adaptive control techniques.
References
1.
BillettR. A.Studies of cutting temperature control applied to the lathe spindle speed. Proceedings of Ninth International MTDR Conference, 1968, pp. 1273–1281.
2.
ShillamN. F.The on-line control of cutting conditions using direct feedback. Proceedings of Fourteenth International MTDR Conference, September 1973, pp. 15–22.
3.
ChenBor-SenChangYih-FangConstant turning force adaptive controller design. Trans. ASME, J. Engng Industry, May 1989, 111, 125–132.
4.
TomizukaM.ZhangS.Modelling and conventional/adaptive PI control of a lathe cutting process. Trans. ASME, J. DSMC, December 1988, 110, 350–354.
5.
DaneshmendL. K.PakH. A.Model reference adaptive control of feed force in turning. Trans. ASME, J. DSMC, September 1986, 108, 215–222.
6.
MasoryO.KorenY.Variable gain adaptive control system for turning. J. Mfg Systems, 1983, 2(2), 165–173.
7.
JonaM. G.Contribution to the development of geometrical adaptive control in turning. AMTDR, 1970, 11, 429–445.
8.
BeadleB. R.BollingerJ. G.Computer adaptive control of a machine tool. Ann. CIRP, 1971, 19, 61–65.
9.
ConstantinidesN.Adaptive control of the milling process and tool wear measurements. PhD thesis, Control Engineering Department, University of Sheffield, April 1986.
10.
FassoisS. D.EmanK. F.WuS. M.A fast algorithm for on-line machining process modelling and adaptive control. Trans. ASME, J. Engng Industry, May 1989, 111, 133–139.
11.
UlsoyA. G.KorenY.RasmussenF.Principal developments in the adaptive control of machine tools. Trans. ASME, J. DSMC, June 1983, 105, 107–112.
12.
SöderströmT.SloicaP.System identification, 1989 (Prentice-Hall International (UK), Hemel Hempstead).
13.
NortonJ. P.An introduction to identification, 1986 (Academic Press, London).
14.
BoxG. E. P.JenkinsG. M.Time series analysis, forecasting and control, 1976 (Holden-Day, San Francisco, Calif.).
