JACIII Vol.12 No.1 pp. 48-55
doi: 10.20965/jaciii.2008.p0048


Printing Pressure State Inspection System Based on Fuzzy Inference

Jianping Jing*, Fangyan Dong*, Yutaka Hatakeyama*,
Yasufumi Takama**, Toru Yamaguchi**, and Kaoru Hirota*

*Department of Computational Intelligence and System Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

June 5, 2007
September 28, 2007
January 20, 2008
fuzzy inference, fuzzy membership function, printing pressure, image processing, liquid crystal display panel
A Printing Pressure expectation system based on fuzzy inference has been installed in a liquid crystal display panel (LCD) production plant to solve problems in determining the printer printing pressure in real-world LCD production, in which the recognition of printing pressure conditions and control are very important and difficult, influencing product quality. This is usually done conducted by skilled engineers, whose performance depends on tacit knowledge. We propose using fuzzy inference to solve this problem. Printing area images are observed with cameras and abstract features extracted using image processing. System output is the status of printing pressure, divided into excessive pressure (EP), good pressure (GP), and low pressure (LP). Based on abstract features, the state is calculated using fuzzy membership functions. Shapes of membership functions are determined based on sampled glass obtained in actual LCD production line. Experiments are conducted using 2000 samples of glass printed using actual printers, or which results are compared to those of skilled engineers. We found that the proposed system yields quality higher than that of skilled engineers. We installed our system on an actual production line, where it is expected to increase product quality and production speed while and cutting production costs.
Cite this article as:
J. Jing, F. Dong, Y. Hatakeyama, Y. Takama, T. Yamaguchi, and K. Hirota, “Printing Pressure State Inspection System Based on Fuzzy Inference,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.1, pp. 48-55, 2008.
Data files:
  1. [1] J. Jianping, Y. Takama, and T. Yamaguchi, “A Proposal of Printing Pressure Control System based on fuzzy Inference Method,” 6th Int. Symposium on Advanced Intelligent System (ISIS2005), pp. 355-360, Yosu, Korea.
  2. [2] 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.
  3. [3] 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), pp. 109-113, 2001/8.
  4. [4] K. Hirota, N. Iwamatsu, and Y. Takama, “Toward the Realization of Multimedia Instrumentation based on Soft Computing Technology,” Japan-Tunisia Workshop of Informatics (JTWIN2001), pp. 90-95, 2001/10.
  5. [5] 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.
  6. [6] K. Hirota, “Fuzziness Intelligence Robot Control,” Chapter 2, Mc-Graw, Hill Book (Tokyo), 1985.
  7. [7] T. Terano, K. Asayi, and M. Kanno, “Fuzzy Systems Theory and Its Applications,” Ohm Publish,
  8. [8] W. E. Snyder and H. Qi, “Machine Vision,” Cambridge University Press,
    ISBN 0-521-83046-X,
  9. [9] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” New Technology Communications Co., Ltd.,
    ISBN 0-471-05669-3,

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024