DOI QR코드

DOI QR Code

A Concept of Self-Optimizing Forming System

자율 최적 성형 공정 시스템 개발

  • Received : 2013.03.08
  • Accepted : 2013.03.29
  • Published : 2013.04.15

Abstract

Nowadays, a strategy of the self-optimizing machining process is an imperative approach to improve the product quality and increase productivity of manufacturing systems. This paper presents a concept of self-optimizing forming system that allows the forming system automatically to adjust the forming parameters online for guarantee the product quality and avoiding the machine stop. An intelligent monitoring system that has the functions of observation, evaluation and diagnostic is developed to evaluate the pully quality during forming process. Any abnormal variation of forming machining parameters could be detected and adjusted by an intelligent control system aiming to maintain the machining stability and the desired product quality. This approach is being practiced on the pully forming machine for evaluating the efficiency of the proposed strategy.

Keywords

References

  1. Prasopchaichana, K., Kwon, O. Y., 2008, "Sensor Fusion and Neural Network Analysis for Drill-Wear Monitoring," Transactions of the Korean Society of Machine Tool Engineers, Vol. 17, No. 1, pp. 77-85.
  2. Klocke, F., Sheet Metal Forming I, Lecture 3, Fraunhofer IPT.
  3. Abellan, J. V., Romero, F., Siller, H. R., Estruch, A., Vila, C., 2008, Adaptive control optimization of cutting parameters for high quality machining operations based on neural networks and search algorithm, I-Tech, Vienna, Austria, pp. 472.
  4. Chien, W. T., Chou, C. Y., 2001, "The predictive model for machinability of 304 stainless steel," Journal of Materials Processing Technology, Vol. 118, No. 1-3, pp. 442-447. https://doi.org/10.1016/S0924-0136(01)00875-5
  5. Stella, H., Alena, V., 2012, "Application of Fuzzy Principles in Evaluating Quality of Manufacturing Process," Word Scientific and Engineering Academy and Society, Vol. 7, No. 2, pp. 50-59.
  6. Cus, F., Zuperl, U., 2006, "Approach to optimization of cutting conditions by using artificial neural networks," Journal of Materials Processing Technology, Vol. 173, No. 3, pp. 281-290. https://doi.org/10.1016/j.jmatprotec.2005.04.123
  7. Liu, Y., Zuo, L., Cheng, T., 2000, "A neural network based fuzzy learning controller and its experimental application to milling," International Journal of Computer Integrated Manufacturing, Vol. 13, No. 5, pp. 461-466. https://doi.org/10.1080/09511920050117946
  8. Xi, J., Liao, G., 2009, "Cutting parameter optimization based on particle swarm optimization," International Conference on Intelligent Computation Technology and Automation, Vol. 01, pp. 255-258.
  9. Cruz, E. D., Aguiar, P. R., Machado, A. R., Bianchi, E. C., 2012, "Monitoring in precision metal drilling process using multi-sensors and neural network," The International Journal of Advanced Manufacturing Technology, Vol. 66, No. 1-4, pp. 151-158.
  10. Cus, F., Zuperl, U., Kiker, E., Milfelner, M., 2008, "Adaptive self-learning controller design for federate maximization of machining process," Journal of Achivements in Materials and Manufacturing, Vol.31, No. 2, pp. 469-476.
  11. Cus, F., Zuperl, U., Kiker, E., MIIfelner, M., 2006, "Adaptive controller design for feedrate maximization of machining process," Journal of Achievements in Materials and Manufacturing Engineering, Vol. 17, No. 1-2, pp.237-240.
  12. Chen, M. D., Hsu, R. Q., Fuh, K. H., 2005, "An analysis of force distribution in shear spinning of cone," International Journal of Mechanical Sciences, Vol. 47, No. 6, pp. 902-921. https://doi.org/10.1016/j.ijmecsci.2005.01.010
  13. Yau, H. T., 2006, "Nonlinear rule-based controller for chaos synchronization of two gyros with linear-pluscubic damping," Chaos - Solitons and Fractals, Vol. 34, No. 4, pp. 1357-1365.
  14. Kratmüller, M., 2009, "The Adaptive Control of Nonlinear Systems Using the T-S-K Fuzzy Logic," Acta Polytechnica Hungarica, Vol. 6, No. 2, pp. 5-16.
  15. Quigley, E., Monaghan, J., 2000, "Metal forming: an analysis of spinning processes," Journal of Materials Processing Technology, Vol. 103, No. 1, pp.114-119. https://doi.org/10.1016/S0924-0136(00)00394-0
  16. Sadler, J. P., Jawahir, I. S., Zhongjie Da, Seog S. Lee, 1999, Optimization of machining with progressively worn cutting tool, US Patent: 5903474,
  17. Wu, C. L., Haboush, R. K., 1990, Artificial intelligence for adaptive machining control of surface finish, US Patent: 4926309.
  18. Patel, S., Wirral (GB), 2004, Method for optimizing formulations, US Patent: 6757667.
  19. Colding, B., Novak, A., Sandstrom, U., Jakobsson, G., 1977, Adaptive control of cutting machining operations, US Patent: 4031368.

Cited by

  1. 폐비닐 재활용을 위한 재생원료 분석 및 배합비율에 따른 특성 평가 vol.30, pp.1, 2021, https://doi.org/10.7844/kirr.2021.30.1.53