Adaptive neuro-fuzzy inference systems (ANFIS) are utilized to identify and control the clad height in the laser cladding process. The scanning speed of the substrate is used as the control action in the controller. A feedback signal is obtained using a CCD camera. First, the process is identified by means of an ANFIS network through a hybrid learning algorithm. The inverse dynamics of the ANFIS plant is later obtained in an ANFIS inverse learning scheme. The inverse dynamics is used in a neuro-fuzzy structure to obtain an ANFIS controller for the process. A complete control system is designed by tuning the ANFIS controller as a combined unit. Satisfactory results are obtained both in process modeling and process control.
REFERENCES
1.
2.
Sjoberg
, J.
Zhang
, Q.
Ljung
, L.
Benveniste
, A.
Delyon
, B.
, Glorennec
, P.Y.
, Hjalmarsson
, H.
, Juditsky
, A.
, 1995
. Nonlinearblack-boxmodelinginsystem identification: a unified overview
. Automatica (Journal of IFAC)
31
(12
, Special issue on trends in system identification), 1691
–1724
.3.
R. J.
Jang
, 1993
, ANFIS: Adaptive network-based fuzzy inference system, IEEE Trans
. On Syst. Man
; 23
(3
), 665
–685
.4.
R. J.
Jang
, C. T.
Sun
, 1995
, Neuro-fuzzy modeling and control
, Proc. IEEE
, 83
(3
), 378
–406
.5.
Sameni
, M.K.
, Toyserkani
, Khajepour
, E.A.
, 2004
. Fuzzy model and compact fuzzy model identification of laser cladding process
. in: Proceeding of the International Congress on the Applications of Lasers and Electro-Optics (ICALEO’ 2004)
, LIA Publication # 597.6.
Alimardani
, M.
, Toyserkani
, E.
, 2008
. Prediction of laser solid freeform fabrication using a neuro-fuzzy approach
, J. Appl. Soft Comput
. 8
(1
), 316
–323
.7.
Perez
A.J.
, Gonzalez
M.
, Dopico
D.
, 2009
, Adaptive neuro-fuzzy ANFIS modeling of laser surface treatments Neural Comput
. Appl..
8.
M.
Zeinali
, A.
Khajepour
, Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process
.9.
D.
Hu
, R.
Kovacevic
, Sensing, modeling and control for laser-based additive manufacturing
, Int. J. Mach. Tool Manuf.
43
(2003
) 51
–60
.10.
A.
Fathi
, A.
Khajepour
, E.
Toyserkani
, M.
Durali
, 2007
, “Clad height control in laser solid freeform fabrication using a feedforward PID controller
”, Int. J Adv Manuf Technol
, 35
(3-4
): 280
–292
.11.
J.-S.R.
Jang
, C.-T.
Sun
, E.
Mizutani
, 1997
, Neuro-Fuzzy and Soft Computing, PTR Prentice Hall
, Upper Saddle River, NJ
.12.
Mamdani
, E.H.
, 1974
. Application of fuzzy algorithms for control of simple dynamic plant
. Proc. IEEE
121
(12
), 1585
–1588
.13.
D.
Psaltis
, A.
Sideris
and A.
Yamamura
, 1988
, A multi-layered neural network controller
, IEEE Control Systems Magazine
, 8
(4
), 17
–21
.14.
B.
Widrow
and D.
Stearns
, 1985
, Adaptive signal processing, Prentice Hall
, Upper Saddle River, NJ
.15.
V. C.
Chen
and Y. H.
Pao
, 1989
, Learning control with neural network
, Proceedings of International Conference on Robotics and Automation
, 1448
–1453
.16.
M.
Kawato
, K.
Furukawa
and R.
Suzuki
, 1987
, A hierichal neural network model for control learning of voluntary ovement
, Biological Cybernetics
, 57
, 169
–185
17.
K. P.
Venugopal.
R.
Sudhakar
, and A. S.
Pandya
, 1994
, An improved scheme for direct adaptive control of dynamical systems using backpropagation neural networks
, Journal of Cicuits, Systems and Signal Processing
.18.
Sugeno
, M.
, Yasukawa
, T.
, 1993
. A fuzzy logic based approach to qualitative modelling
. IEEE Trans. Fuzzy Syst.
1
, 7
–31
.
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