When machining conditions change significantly, applying parameter-adaptive control to the cutting system by varying the table feedrate allows a constant cutting force to be maintained. Although several controller schemes have been proposed, their cutting control performance is limited especially when the cutting conditions vary significantly. This paper presents an adaptive fuzzy logic control (FLC) developed for cutting processes under various cutting conditions. The controller adopts on-line scaling factors for cases with varied cutting parameters. In addition, a reliable self-learning (SL) algorithm is proposed to achieve even better cutting performance by modifying the adaptive FLC rule base according to properly weighted performance measurements. Both simulation and experimental results show that given a sufficient number of learning cases, the adaptive SL-FLC is effective for a wide range of applications. The successful implementation of the proposed adaptive SL-FLC algorithm on an industrial heavy-duty machining center indicates that the proposed adaptive SL-FLC is feasible for use in manufacturing industries.

Issue Section:
Research Papers
Topics:
Adaptive control, Algorithms, Machining, Cutting, Control equipment, Fuzzy logic, Machining centers, Manufacturing industry, Simulation
1.
Altintas
Y.
,
Sassani
F.
, and
Ordubadi
F.
,
1990
, “
Design and Analysis of Adaptive Controllers for Milling Process
,”
Trans. CSME
, Vol.
14
, No.
1/2
, pp.
17
–
25
.
2.
Bernard
J. A.
,
1988
, “
Use of a Rule-Based System for Process Control
,”
IEEE Control Systems Magazine
, Vol.
8
, No.
3
, pp.
3
–
13
.
3.
Cheng, W. M., Huang, S. Y., and Ho, Y. K., 1984, “Model Reference Fuzzy Adaptive Control,” IFAC 9th Triennial World Congress, Budapest, Hungary, pp. 889–892.
4.
Elbestawi
M. A.
,
Mohammed
Y.
, and
Liu
L.
,
1990
, “
Application of Some Parameter Adaptive Control Algorithm in Machining
,”
ASME Journal of Dynamic Systems, Measurement, and Control
, Vol.
112
, No.
4
, pp.
611
–
617
.
5.
Fann, W. R., and Hsu, P. L., 1992, “Fuzzy Adaptive Control of Milling Processes,” IFAC Workshop on Intelligent Manufacturing Systems, Dearborn, MI, pp. 88–92.
6.
Hsu
P. L.
, and
Hsieh
M. Y.
, “
Applications of Self-Tuning Control on Industrial CNC Machines
,”
International Journal of Mach. Tools Manufact.
, Vol.
34
, No.
6
, pp.
859
–
877
.
7.
Hsu
C. S.
,
1985
, “
A Discrete Method of Optimal Control Based upon the Cell State Space Concept
,”
Journal of Optimization Theory and Application
, Vol.
46
, No.
4
, pp.
547
–
569
.
8.
Huang
L. J.
, and
Tomizuka
M.
,
1990
, “
A Self-Paced Fuzzy Tracking Controller for Two-Dimensional Motion Control
,”
IEEE Trans. on Systems, Man and Cybernetics
, Vol.
20
, No.
5
, pp.
1115
–
1123
.
9.
Koren, Y., 1983, Computer Control of Manufacturing Systems, McGraw-Hill, New York.
10.
Lauderbaugh
L. K.
, and
Ulsoy
A. G.
,
1989
, “
Model Reference Adaptive Force Control in Milling
,”
ASME Journal of Engineering for Industry
, Vol.
3
, No.
1
, pp.
13
–
21
.
11.
Lee
C. C.
,
1990
, “
Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part I
,”
IEEE Trans. on Systems, Man and Cybernetics
, Vol.
20
, No.
2
, pp.
404
–
418
.
12.
Lee
C. C.
,
1990
, “
Fuzzy Logic in Control Systems: Fuzzy Logic Controller—Part II
,”
IEEE Trans. on Systems, Man and Cybernetics
, Vol.
20
, No.
2
, pp.
419
–
435
.
13.
Li
Y. F.
, and
Lau
C. C.
,
1989
, “
Development of Fuzzy Algorithms for Servo Systems
,”
IEEE Control Systems Magazine
, Vol.
9
, No.
2
, pp.
65
–
72
.
14.
Ljung, L., 1987, System Identification: Theory for the User, Prentice-Hall, Englewood Cliffs, New Jersey.
15.
Lingarkar
R.
,
Liu
L.
,
Elbestawi
M. A.
, and
Sinha
N. K.
,
1990
, “
Knowledge-Based Adaptive Control in Manufacturing Systems: a Case Study
,”
IEEE Trans. on Systems, Man and Cybernetics
, Vol.
20
, No.
3
, pp.
606
–
617
.
16.
Masory
O.
, and
Koren
Y.
,
1983
, “
Variable-Gain Adaptive Control Systems for Machine Tools
,”
J. Manufac. Syst.
, Vol.
2
, No.
2
, pp.
165
–
174
.
17.
Peng
X. T.
,
1990
, “
Generating Rules for Fuzzy Logic Controller by Functions
,”
Fuzzy Sets and Systems
, Vol.
36
, No.
1
, pp.
83
–
89
.
18.
Procyk
T. J.
, and
Mamdani
E. H.
,
1979
, “
A Linguistic Self-Organizing Process Controller
,”
Automatica
, Vol.
15
, No.
1
, pp.
15
–
30
.
19.
Shao
S. H.
,
1988
, “
Fuzzy Self-Organizing Controller and its Application for Dynamic Processes
,”
Fuzzy Sets and Systems
, Vol.
26
, No.
2
, pp.
151
–
164
.
20.
Smith, S. M., and Comer, D. J., 1991, “Automated Calibration of a Fuzzy Logic Controller Using a Cell State Space Algorithm,” IEEE Control Systems Magazine, pp. 18–28.
21.
Tomizuka, M., Oh, J. H., and Dornfeld, D. A., 1983, “Model Reference Adaptive Control of the Milling Process,” Proc. Manufacturing Processes and Robotics System, ASME Winter Annual Meeting, New York, pp. 55–63.
22.
Tomizuka
M.
, and
Zhang
S.
,
1988
, “
Modeling and Conventional/Adaptive PI Control of a Lathe Cutting Process
,”
ASME Journal of Dynamic Systems, Measurement, and Control
, Vol.
110
, No.
4
, pp.
350
–
354
.
23.
Ulsoy
A. G.
, and
Koren
Y.
,
1989
, “
Application of Adaptive Control to Machine Tool Process Control
,”
IEEE Control Systems Magazine
, Vol.
9
, No.
4
, pp.
33
–
37
.
This content is only available via PDF.
Copyright © 1996
 by The American Society of Mechanical Engineers
You do not currently have access to this content.