Roll cooling and its relationship to roll life

Tseng, Ampere A.; Lin, Fanjie; Gunderia, Ashutosh S.; Ni, Duan

doi:10.1007/bf02666666

Cited by 52 publications

(26 citation statements)

References 18 publications

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“…The temperature variations in work-rolls have been considered in a few papers. Sluzalec (1984) has utilized a two-dimensional finite element method to predict the temperature distribution during hot rolling and Teseng et al (1990) have estimated the temperature variations in the work-roll in order to evaluate the thermal stress distribution in the rolls. Mori et al (1982) have developed a finite element method, using the assumptions of rigid-plastic and slightly compressible material to predict the velocity field during isothermal steady and unsteady plane strain rolling conditions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of influence parameters on the hot rolling process using finite element method and neural network

Shahani

Setayeshi

Nodamaie

et al. 2009

Journal of Materials Processing Technology

117

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Section: Introductionmentioning

confidence: 99%

Prediction of influence parameters on the hot rolling process using finite element method and neural network

Shahani

Setayeshi

Nodamaie

et al. 2009

Journal of Materials Processing Technology

117

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“…For the corresponding effect on the rolls, one can deduce from figure 7 that the thermally affected zone penetrates the surface of the work-roll to a depth of about 4 mm. In the thermally affected zone, the occurrence of thermal stresses and thermal fatigue is much higher than that in the other regions [4]. Figure 9 shows the temperature changes in the work-roll of the first finishing stand.…”

Section: Simulation Resultsmentioning

confidence: 96%

“…The model was coupled with the assumption of homogenous work to estimate the steady state temperature distributions in the work-rolls and the rolled metal during the finishing stage. Teseng et al [4] combined experimental and numerical methods to predict temperature distributions in work-rolls and to evaluate roll life. In another research work, Teseng et al [5] used an analytical method to solve the heat transfer, partial differential equations and thus determine the temperature field in a work-roll for a single pass hot strip rolling process.…”

Section: Introductionmentioning

confidence: 99%

High-Power Compact Laser-Plasma Source for X-ray Lithography

Gaeta¹,

Rieger²,

Turcu³

et al. 2002

Jpn. J. Appl. Phys.

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In this paper, the unsteady state heat transfer equations with time dependent boundary conditions are coupled with a two-dimensional finite element method to predict the work-roll temperature distribution during the continuous hot slab rolling process. To achieve an accurate temperature field, the effects of various factors including the thermal relationship of the work-roll and the metal slab, the idling work-roll revolutions, the rolling speed, the slab/roll interfacial heat transfer coefficient, and the magnitude of the thickness reduction of the slab at each deformation pass are taken into account. Comparisons between the predicted and published experimental results are used to illustrate the validity of the mathematical model.

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“…After discretization, the governing equation [Eq. (1)] and the boundary conditions [Eqs. (2a-2c)] can be expressed in the following recursive forms.…”

Section: Description Of the Inverse Methodsmentioning

confidence: 99%

“…It is dif cult to install thermocoupleson the surface of heated objects. Therefore, if the temperature is measured at one or more interior locationsof the objects and is then used to predict the surface thermal behaviorof the objects, the dif culties encountered by Tseng et al 1 with the surface mounting of thermocouples can be avoided. There have been numerous applications of inverse heat conduction problems (IHCP) in various branches of science and engineering, such as the prediction of the inner wall temperature of a reactor, the determination of the heat transfer coef cients and outer surface conditions of the space vehicle, and the prediction of temperature or heat ux at the tool-workpiece interface of machine cutting.…”

mentioning

confidence: 99%

Inverse Method for Estimating Thermal Conductivity in One-Dimensional Heat Conduction Problems

Lin

Chen

Yang

2001

Journal of Thermophysics and Heat Transfer

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An inverse analysis is provided to determine the spatial-and temperature-dependent thermal conductivities in several one-dimensional heat conduction problems. A nite difference method is used to discretize the governing equations, and then a linear inverse model is constructed to identify the undetermined thermal conductivities. The present approach is to rearrange the matrix forms of the differential governing equations so that the unknown thermal conductivity can be represented explicitly. Then, the linear least-squares-error method is adopted to nd the solutions. The results show that only a few measuring points at discrete grid points are needed to estimate the unknown quantities of the thermal conductivities, even when measurement errors are considered. In contrast to the traditional approach, the advantages of this method are that no prior information is needed on the functional form of the unknown quantities, no initial guesses are required, and no iterations in the calculating process are necessary and that the inverse problem can be solved in a linear domain. Furthermore, the existence and uniqueness of the solutions can be easily identi ed. NomenclatureA = coef cient matrix of vector T B = coef cient matrix of vector C C = vector constructed from the unknown thermal conductivities D = vector constructed from the functions of the unknown thermal conductivities E = product of A ¡1 and B F = error function g = heat generation, W/m 3 k = thermal conductivity, W/m ¢ ± C q = heat ux, W/m 2 R = reverse matrix T = temperature, ± C T = temperature vector t = time, s x = spatial coordinate, m 1t = increment of time domain, s 1x = increment of spatial coordinate, m ¾ = standard deviation ! = random variable Subscripts esti = estimated data exact = exact data i = index of spatial coordinate j = index of time domain meas = measured data n = index at the boundary when x is equal to 1, m

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Roll cooling and its relationship to roll life

Cited by 52 publications

References 18 publications

Prediction of influence parameters on the hot rolling process using finite element method and neural network

Prediction of influence parameters on the hot rolling process using finite element method and neural network

High-Power Compact Laser-Plasma Source for X-ray Lithography

Inverse Method for Estimating Thermal Conductivity in One-Dimensional Heat Conduction Problems

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