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On-line 학습 신경회로망을 이용한 열간 압연하중 예측

Prediction for Rolling Force in Hot-rolling Mill Using On-line learning Neural Network


In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, an on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

참고문헌 (11)

  1. Pican, N., and Alexander, F., 1996, 'Artificial Neural Networks for the Presetting of a Steel Temper Mill,' IEEE Expert, pp. 22-27 
  2. Larkiola, J., Myllyoski, P., Korhonen A. S., and Cser, L., 1998, 'The role of neural networks in the optimization of rolling processes,' Journal of Materials Processing Technology, Vol. 80, No. 81, pp. 16-23 
  3. Son, J. S., Kim, I. S., Kwon, Q. H., Choi, S. G., Park, C. J., and Lee, D. M., 2001, 'A study on development of setup model for thickness control in tandem cold rolling mill,' Journal of the Korean Society of Machine Tool Engineers, Vol. 10, No. 5, pp. 96-103 
  4. Hagan, M. T., and Menhaj, M. B., 1994, 'Training feedforward networks with marquardt algorithm,' IEEE Transaction on Neural Networks, Vol. 5, No. 6, pp. 989-993 
  5. Jeon, E. C., and Kim, S. K., 2000, 'A Study on the Texturing of Work Roll for Temper Rolling,' Journal of the Korean Society of Machine Tool Engineers, Vol. 9, No. 4, pp. 7-16 
  6. Yao, X., 1996, 'Application of artificial intelligence for quality control at hot strip mills,' Ph.D. Thesis, The University of Wollongong 
  7. Schlang, M., Lang, B., Poppe, T., Runkler T., and Weinzierl, K., 2001, 'Current and future development in neural computation in steel processing,' Control Engineering Practice, Vol. 9, pp. 975-986 
  8. Portmann, N. F., 1995 'Application of neural networks in rolling mill automation,' Iron and Steel Engineer , Vol. 72, No. 2, pp. 33-36 
  9. Poliak, E. I., Shim, M. K., Kim, G. S., and Choo, W. Y., 1998, 'Application of linear regression analysis in accuracy assessment of rolling force calculations,' Metals and Materials, Vol. 4, No. 5, pp. 1047-1056 
  10. Lee, J. Y., Cho, H. S., Shim, M. S., Cho, S. J., Jang, M., Cho, Y. J. and Yoon, S. C., 1996, 'Improvement of Roll Force Precalculation Accuracy in cold Mill using a Corrective Neural Network,' Proceeding of the 11th KACC, October, pp. 1083-1086 
  11. Poppe, T., Obradovic D., and Schlang, M., 1995, 'Neural networks : Reducing energy and raw materials requirements,' Siemens Review-R&D Special, pp. 24-27 

이 논문을 인용한 문헌 (1)

  1. Lee, Jeong-Ick ; Koh, Byung-Kab 2007. "A Study on Real-time Control of Bead Height and Joint Tracking" 한국공작기계학회논문집 = Transactions of the Korean society of machine tool engineers, 16(6): 71~78 


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