Application of Fuzzy Inference Method in Printing Pressure State Expectation System
Jianping Jing*, Yasufumi Takama**, and Toru Yamaguchi***
*R&D Division, Iinuma Gauge MFG. CO. LTD., 11400-1078 Azaharayama, Tamagawa, Chino, Nagano 391-8550, Japan
**Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
***Department of Information and Communication Systems Engineer, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
To solve a problem in determining the printing pressure of printing machine for real-world liquid crystal display panel (LCD) production, a Printing Pressure expectation system is proposed based on a fuzzy inference method. In real-world LCD panel production, the recognition of printing pressure conditions and its control is a very important and difficult factor that influences the product quality. It is usually performed by skilled engineers, whose performance highly depends on his tacit knowledge. In the proposed system, a fuzzy inference method is employed to solve the problem. Images of the printing area are observed with cameras, from which abstract features are extracted with image processing. The output of the system is the state of printing pressure, which is divided into 3 states: EXCESSIVE PRESSURE (EP), GOOD PRESSURE (GP), and LOW PRESSURE (LP). Based on the abstract features, the state is estimated with fuzzy membership functions. The shapes of membership functions are determined based on the sampled glasses obtained in actual LCD production line. The experiments are performed with the 2000 glasses that are also printed with actual printing machines, of which the result is compared with that of skilled engineers. It is shown that the proposed system outperforms the quality of skilled engineers. The developed system is installed in actual production line, and it is expected to increase the product quality and production speed, as well as to cut off production costs.
-  J. Jing, Y. Takama, and T. Yamaguchi, “A Proposal of Printing Pressure Control System based on fuzzy Inference Method,” 6th International Symposium on Advanced Intelligent System (ISIS2005), Yosu, Korea, pp. 355-360.
-  K. Hirota, N. Iwamatsu, and Y. Takama, “Proposal of an Internal-State Inference System based on Multimodal Sensory Fusion Method,” IEEE Transactions on Instrumentation and Measurement, Vol.51, No.2, pp. 347-352, 2002/4.
-  K. Hirota, N. Iwamatsu, and Y. Takama, “Inspection System based on Fuzzy Multi-modal Sensory Integration,” 2nd Int. Symposium on Advanced Intelligent System Conference (ISIS2001), Daejeon, Korea, pp. 109-113, 2001/8.
-  K. Hirota, N. Iwamatsu, and Y. Takama, “Toward the Realization of Multimedia Instrumentation based on Soft ComputingTechnology,” Japan-Tunisia Workshop of Informatics (JTWIN2001), Tsukuba, Japan, pp. 90-95, 2001/10.
-  K. Hirota, Y. Takama, and N. Iwamatsu, “Multimedia Nondestructive Inspection based on Computational Intelligence Technology (Opening Lecture),” 2nd Int. Conf. on Intelligent Technologies (INTech2001), Bangkok, Thailand, pp. 14-20, 2001/11.
-  K. Hirota, “Fuzziness Intelligence Robot Control,” Chapter 2, McGraw-Hill Book (Tokyo), 1985.
-  T. Terano, K. Asayi, and M. Kanno, “Fuzzy Systems Theory and Its Applications,” Ohm Publish, ISBN4-274-02124-6, 1987 (in Japanese).
-  W. E. Snyder, and H. Qi, “Machine Vision,” Cambridge University Press, ISBN 0-521-83046-X, 2004.
-  R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” New Technology Communucations Co. Ltd., ISBN 0-471-05669-3, 2001.
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