The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform
Juha Roning - One of the best experts on this subject based on the ideXlab platform.
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an adaptive neural network model for predicting the post Roughing Mill temperature of steel slabs in the reheating furnace
Journal of Materials Processing Technology, 2005Co-Authors: Perttu Laurinen, Juha RoningAbstract:Abstract The walking beam furnace and Roughing Mill of a hot strip Mill were studied. A novel control method using measurement data gathered from the production line is proposed. The model uses adaptive neural networks to predict the post Roughing Mill temperature of steel slabs while the slabs are still in the reheating furnace. It is possible to use this prediction as a feedback value to adjust the furnace parameters for heating the steel slabs more accurately to their pre-set temperatures. More accurate heating enables savings in the heating costs and better treatments at rolling Mills. The mean error of the model was 5.6 °C, which is good enough for a tentative production line implementation. For 5% of the observations the prediction error was large (>15 °C), and these errors are likely to be due to the cooling of the transfer bar following unexpected delay in entry into the Roughing Mill.
S M Hwang - One of the best experts on this subject based on the ideXlab platform.
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A New Model for the Prediction of Width Spread in Roughing Mills
Journal of Manufacturing Science and Engineering-transactions of The Asme, 2014Co-Authors: Tae Jin Shin, S M HwangAbstract:Precision control of the slab width is crucial for product quality and production economy in Roughing Mill. In this paper, we present a new model for the prediction of the width spread of a slab during rolling in the Roughing train of a hot strip Mill. The approach is based on the extremum principle for rigid plastic materials, and applicable to horizontal rolling of a slab with either a dog-bone shaped cross section or a rectangular cross section. Also, the upper bound theorem is used for calculating the width spread of a slab. The prediction accuracy of the proposed model is examined through comparison with the predictions from the 3D finite element (FE) process simulations.
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a new model for predicting width spread in a Roughing Mill part ii application to flat rolling
Transactions of materials processing, 2014Co-Authors: S M HwangAbstract:Abstract Precision control of the slab is crucial for product quality and production economy in hot strip Mills. The current study presents a new model for predicting width spread of a slab with a rectangular cross section during Roughing. The model is developed on the basis of the extremum principle for a rigid plastic material and a three dimensional admissible velocity field. This model incorporates the effect of process variables such as the shape factor and the ratio of width to thickness. We compare the results of this model to 3-D finite element (FE) process simulations and also to results from a previous study. Key Words : Width Spread, Roughing Mill, Extremum Principle, Admissible Velocity Field, Finite Element Method, Flat Rolling, Dog-bone Model 1. 서 론 열간 압연은 슬라브(slab)를 가열로에서 가열하여 조압연과 사상압연을 통해 원하는 폭과 두께로 압연하여 코일(coil) 형태의 최종 제품을 생산하는 공정이다. 그 중 조압연은 마무리 압연에서의 목표 판형상을 얻기 위해 슬라브를 중간 크기의 두께와 폭을 가지는 바(bar)로 가공하는 열간 압연의 중간 공정이다. 근래 실수율 향상과 자원 절감 차원에서 폭 정밀도의 향상이 요구되고, 특히 조압연에서의 정확한 폭퍼짐 예측에 관한 관심이 높아지고 있다. 조압연 pass schedule 에 따르면 도그 본(dog-bone) 형상 뿐만 아니라 평판 압연도 행해지므로 평판에 대한 폭 퍼짐 예측 또한 필수적이다. 과거 여러 연구자들에 의해 폭 퍼짐에 관한 모델이 제안되었으나 대부분 경험에 의한 실험식이었다[1~6]. 다른 접근 방법으로 Oh and Kobayashi 모델[7]은 가용 속도장(admissible velocity filed)을 이용하여 경계치 문제를 푸는 것으로 근사해를 구하였다[8]. 본 연구에서는 extremum principle 을 기초로 하여 근사해를 구하기 위해 새로운 가용 속도장을 적용하였다. 제시된 폭 퍼짐 예측모델로부터 얻은 결과는 3 차원 비정상상태 유한요소해석(FE simulation)을 이용해 검증하였다.
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a new model for predicting width spread in a Roughing Mill part i application to dog bone shaped inlet cross
Transactions of materials processing, 2014Co-Authors: S M HwangAbstract:In the current study, we present a new model for predicting width spread of a slab with a dog-bone shaped cross section during rolling in the Roughing train of a hot strip Mill. The approach is based on the extremum principle for a rigid plastic material and a three dimensional admissible velocity field. The upper bound theorem is used for calculating the width spread of the slab. The prediction accuracy of the proposed model is examined through comparison with the predictions from 3-D finite element (FE) process simulations.
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an analytical model for the prediction of the transfer bar temperature in a Roughing Mill
Ironmaking & Steelmaking, 2011Co-Authors: S M HwangAbstract:An online model is presented for the prediction of temperature distributions in the bar in the Roughing Mill of a hot strip Mill. The model consists of an analytic model for the prediction of temperature distributions in the interstand zone, and a semianalytic model for the prediction of temperature distributions in the bite zone. The prediction accuracy of the model is examined through comparison with predictions from a finite element model.
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an analytic model for the prediction of the bar temperature in a Roughing Mill
NUMIFORM 2010: Proceedings of the 10th International Conference on Numerical Methods in Industrial Forming Processes Dedicated to Professor O. C. Zien, 2010Co-Authors: S M HwangAbstract:An on‐line model is presented for the prediction of temperature distributions in the bar in the Roughing Mill of a hot strip Mill. The model consists of an analytic model for the prediction of temperature distributions in the inter‐stand zone, and a semi‐analytic model for the prediction of temperature distributions in the bite zone. The prediction accuracy of the model is examined through comparison with predictions from a finite element model.
Perttu Laurinen - One of the best experts on this subject based on the ideXlab platform.
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an adaptive neural network model for predicting the post Roughing Mill temperature of steel slabs in the reheating furnace
Journal of Materials Processing Technology, 2005Co-Authors: Perttu Laurinen, Juha RoningAbstract:Abstract The walking beam furnace and Roughing Mill of a hot strip Mill were studied. A novel control method using measurement data gathered from the production line is proposed. The model uses adaptive neural networks to predict the post Roughing Mill temperature of steel slabs while the slabs are still in the reheating furnace. It is possible to use this prediction as a feedback value to adjust the furnace parameters for heating the steel slabs more accurately to their pre-set temperatures. More accurate heating enables savings in the heating costs and better treatments at rolling Mills. The mean error of the model was 5.6 °C, which is good enough for a tentative production line implementation. For 5% of the observations the prediction error was large (>15 °C), and these errors are likely to be due to the cooling of the transfer bar following unexpected delay in entry into the Roughing Mill.
Ye Guang - One of the best experts on this subject based on the ideXlab platform.
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Improvement of tension control system of universal Roughing Mill for H-beam section
Steel Rolling, 2020Co-Authors: Ye GuangAbstract:In view of speed oscillating of universal Roughing Mill,the load stability unit for edge Mill and speed self adaptation unit for later Mills were added in control system At the same time,the input time of little tension control was regulated Based on these measures,the good effects were obtained
Wuzhou Wang - One of the best experts on this subject based on the ideXlab platform.
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analysis of spalling in Roughing Mill backup rolls of wide and thin strip hot rolling process
Steel Research International, 2015Co-Authors: Qiang Dong, Hongbo Li, Yunsong Zhou, Wuzhou WangAbstract:In this paper, the spalling causes of backup rolls in a Roughing Mill of a hot strip rolling Mill were investigated. The Roughing Mill withstood extreme service conditions with long service cycles for backup and work rolls, a large variety of the same width rolling campaigns, severe and non-uniform wear contours of backup and work rolls. Analysis of cyclic stress applied on backup rolls indicated that contact stress played a dominant role in rolling contact fatigue which led to spalling on the backup roll surface. A 3D finite element model of roll system was established and contact stresses between rolls in whole service periods of backup and work rolls were calculated. The simulation results showed that roll wear had significant impact on contact stress distribution. In particular, the severe and non-uniform U-typed wear contours of work rolls led to produce huge contact stress peak in two sides of the rolls, where existed large cracks by eddy current inspection of post-service backup rolls. Backup rolls stood thousands of times stress cycle in a service period, they were liable to fatigue damage in these areas. A new backup roll contour was developed to improve contact stress distribution and no spalling accidents were happened any longer after applied to the Roughing Mill.
