scholarly journals Analysis of Roll Gap Heat Transfers in Hot Steel Strip Rolling through Roll Temperature Sensors and Heat Transfer Models

2012 ◽  
Vol 504-506 ◽  
pp. 1043-1048 ◽  
Author(s):  
Nicolas Legrand ◽  
Nathalie Labbe ◽  
Daniel Weisz-Patrault ◽  
Alain Ehrlacher ◽  
Tomasz Luks ◽  
...  

This paper presents an analysis of roll bite heat transfers during hot steel strip rolling. Two types of temperature sensors (drilled sensor /slot sensor) implemented near roll surface and heat transfer models are used to identify in the roll bite interfacial heat flux, temperature and Heat Transfer Coefficient HTCroll-bite during pilot rolling tests. It is shown that: - the slot type sensor is much more efficient than the drilled type sensor to capture correctly fast roll temperature changes in the bite during hot rolling but life’s duration of the slot sensor is shorter. - average HTCroll-bite, identified with roll sensors temperature signals is within the range 15-26 kW/m2/K: the higher the strip reduction is, the higher the HTCroll-bite is. - scale thickness at strip surface tends to decrease heat transfers from strip to roll in the roll bite. - HTCroll-bite appears not uniform along the roll-strip contact, in contrast to usual assumptions made in existing models - Heat dissipated by friction at roll-strip interface and its partitioning through roll and strip respectively seems over-estimated in the existing thermal roll gap model [1]. Modeling of interfacial friction heat dissipation should be reviewed and verified. The above results show the interest of roll temperature sensors to determine accurately roll bite heat transfers and evaluate more precisely the corresponding roll thermal fatigue degradation.

1984 ◽  
Vol 106 (3) ◽  
pp. 578-585 ◽  
Author(s):  
W. Y. D. Yuen

A series solution for the two-dimensional, steady-state temperature distribution in a rotating cylinder, subject to surface heating flux conditions that are at most linear functions of the surface temperature, is applied to strip rolling. An examination of the influence of heat input over the heating region (roll gap) on the peak cylinder (roll) temperature is made. A strip scale layer (which is present in hot rolling) is shown to have a significant effect on roll temperatures through its modification of the heat transfer between strip and roll. The present results indicate that significant errors will arise in estimating the peak roll temperature if insufficient terms are used or if the heat distribution is taken to be uniform in the heating region.


Kerntechnik ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. 237-240
Author(s):  
M. Zhao ◽  
X. Liu ◽  
A. Badea ◽  
F. Feuerstein ◽  
X. Cheng

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.


Measurement ◽  
2021 ◽  
pp. 109454
Author(s):  
Xupeng Kou ◽  
Shuaijun Liu ◽  
Kaiqiang Cheng ◽  
Ye Qian
Keyword(s):  

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Conor N. Murphy ◽  
Paul R. Eastham

Abstract Lasers, photovoltaics, and thermoelectrically-pumped light emitting diodes are thermodynamic machines which use excitons (electron-hole pairs) as the working medium. The heat transfers in such devices are highly irreversible, leading to low efficiencies. Here we predict that reversible heat transfers between a quantum-dot exciton and its phonon environment can be induced by laser pulses. We calculate the heat transfer when a quantum-dot exciton is driven by a chirped laser pulse. The reversibility of this heat transfer is quantified by the efficiency of a heat engine in which it forms the hot stroke, which we predict to reach 95% of the Carnot limit. This performance is achieved by using the time-dependent laser-dressing of the exciton to control the heat current and exciton temperature. We conclude that reversible heat transfers can be achieved in excitonic thermal machines, allowing substantial improvements in their efficiency.


2015 ◽  
Vol 96 (2) ◽  
pp. 247-260 ◽  
Author(s):  
I. Gruais ◽  
D. Poliševski

Author(s):  
Maximilian Schrittwieser ◽  
Oszkár Bíró ◽  
Ernst Farnleitner ◽  
Gebhard Kastner

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