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Research on Finishing Setup and Self-Adaptive of Hot Strip Mill

Author: LiuChangQing
Tutor: XuXiuMei
School: Yanshan University
Course: Mechanical Design and Theory
Keywords: Hot strip mill Finishing mill Mathematical model Pre-set Adaptive
CLC: TG335.11
Type: Master's thesis
Year: 2011
Downloads: 62
Quote: 0
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Abstract


Steel rolling , hot strip rolling production is a wider field of various high-tech applications . Rolling automation , computer control system of hot strip rolling is developing most rapidly , most mature , and achieved economic effect is most obvious in the computer control system . To some extent , reflect the level of development of a country the computer control system of hot strip rolling rolling the overall level of automation in the country . In this paper, on the basis of access to a large number of domestic and foreign literature , the system of the computer control system of hot strip mill finishing mill pre - set and adaptive function calculation process and document composition , while a steel plant in the country 1780 mm hot strip mill finishing rolling force set model added Zhou Jihua , tube grams Chi deformation resistance model , in-depth study of the calculation process of finishing setting . Validation , using the C programming language , created Zhou Jihua , tube Ke Zhi model regression coefficients file , modify the appropriate input calculation module , and then start the finishing set-up process , with Zhou Jihua tube grams Chi Regression steels to calculate the the field production steels similar thereto , comparative resistance model before and after addition of the rolling force prediction value . The verification calculation results show that the rolling force model after adding resistance model can be relatively satisfied with the accuracy . Smoothing coefficient is a very critical factor in the adaptive learning , it is too large or too small will self- learning effect adversely affected , even if set to a fixed value and unreasonable . This paper analyzes the influencing factors after smoothing coefficient , the establishment of a dynamic optimization model smoothing coefficient , using C language to write programs , read measured data validation , comparison to the rolling force errors before and after optimization , results showed that the dynamic optimization smoothing coefficient more help to improve the prediction accuracy of rolling force model .

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal pressure processing > Rolling > Rolling process > Rolling method > Hot-rolled
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