Proactive quality paint thickness control using ANFIS

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2010, Pages 5-10

  Radfar, Reza ^a   Ghoreishi, Felora ^a   Jassbi, Javad ^a   Khanmohammadi, Sohrab ^a   Alborzi, Mahmood ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

ANFIS; Paint thickness; Proactive quality control

Indexed keywords

AIR LAYERS; ANFIS; AUTOMOBILE PAINTS; DRY FILM THICKNESS; EFFECTIVE FACTORS; INPUT VARIABLES; PAINT SHOPS; PAINT SPRAY; PROACTIVE QUALITY CONTROL; STANDARD DEVIATION; THIN LAYERS; THREE-LAYER;

CYBERNETICS; INSPECTION; QUALITY ASSURANCE; SHEET METAL;

PAINT;

EID: 78751512042     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2010.5642189     Document Type: Conference Paper

References (19)

1. H. Lou , Integrated Modeling and Optimization for Robust Production and Profitable Pollution Prevention In complex Process System, Ph D Thesis, Wayne State University, 2001.
2. Koleske, J. V. (ed.),paint and coating testing manual, 14th Edition , ASTM, Philadelphia, PA, 1995.
3. Wu,S.M.,J.Ni, and S.Hu ,"Next Generation quality control in of the ASME winter manufacturing - real time defect prevention "proceeding annual meeting,1989.
4. Bodizs, A.,Szeifert, F., &Chovan, T. ,Convolution model based predictive controller for a nonlinear process. Industrial Engineering, 22, 1113-1127, 1999.
5. Noriega, J.R., &Wang, H.,A adaptive neural-network control for unknown nonlinear systems and its application. IEEE Transaction on neural Network,9,27-34.
6. Lou. H. and Y. Huang Hierarchical decision making for proactive quality control: system development for defect reduction in automotive operation, Engineering Application Of Artificial Intelligence, 16(3),237-250,April 2003.
7. Gut off, E B., Cohen, E.D., Coating and Drying Defects. Wiley, New York, NY, 1995
8. Filev, Applied Intelligent Control-Control of Automotive Paint Process, Ford motor Company, IEEE, 2002.
9. Robert D, Grear, Cxfge "liquid Paint finishing defects" 1961.
10. J. Li and Y. Huang, Bayesian-based on-line applicability evaluation of neural network models in modeling automotive paint spray operations, Volume 30, Issue 9, 15 July 2006, Pages 1392-1399
11. DuPont automotive finishing, troubleshooting, 2000.
12. Stauffer, P.E., Finishing Systems Design and Implementation, society of manufacturing Engineers Dearborn, Michigan 48121, 1993.
13. S. Horiawa, T. Furuhashi, S. Ouma, and Y. Uchikawa. A fuzzy controller using a neural network and its capability to learn expert's control rules. In Proc. of the International Conference on Fuzzy Logic and Neural Networks, pages 103-106, Japan, 1990.
14. H.Takagi and I. Hayashi. NN-driven fuzzy reasoning. International Journal of Approximate Reasoning,5(3):191-212,1991.
15. Jang JSR (1993) ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665-685
16. Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator's control action. Proceedings of the IFAC Symposium on
17. Fuzzy Information, Knowledge Representation and Decision Analysis, July 1983, pp 55-60
18. Rarnamurthy, A. C. and M. Uriquidi - Macdonald, Stone Impact Damage to Automotive Paint Finishes - A Neural net Analysis of Electrochemical Impedance Data , IEEE International (1993) .
19. J. Li, Adaptive molding Integration Optimization , and Control Automotive Paint Spray Process , MS Thesis, Wayne State University,2004.