Modelling a Skin-Pass Rolling Process by Means of Data Mining Techniques and Finite Element Method

Escribano, R.; Lostado, R.; Martínez-de-Pisón, Francisco Javier; Pernía, A.; Vergara, Eliseo P.

doi:10.1016/s1006-706x(12)60098-3

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…It is noteworthy that despite using the flow stress curve obtained following to the procedure in RAMBERG and OSGOOD 25 , SALF simulation presented force values below those in FEM from ESCRIBANO, et al 24 shown in Table 2. Because of the procedural uncertainties, a set of new simulations using SALF were performed.…”

Section: Force Resultsmentioning

confidence: 89%

“…Neither SALF nor FEM from ESCRIBANO, et al 24 presented a relevant approximation between their results and the experimental data. The analytical homogeneous deformation method underestimated the experimental data while the slab method presented data close to the experimental data.…”

Section: Force Resultsmentioning

confidence: 92%

“…Considering the conditions of simulations 1 and 2, Table 2 presents the force results from the analytical methods, SALF, FEM from ESCRIBANO, et al 24 , and the experimental data. Neither SALF nor FEM from ESCRIBANO, et al 24 presented a relevant approximation between their results and the experimental data.…”

Section: Force Resultsmentioning

confidence: 99%

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The Analysis of the Cold Flat Rolling Process by Salf Program

Costa

Santos

2017

Mat. Res.

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SALF program is a Brazilian program developed to simulate the cold flat rolling process by the finite element method. The program uses the implicit and rigid-plastic approaches to perform numerical analyses. This article presents the cold flat rolling simulations performed by SALF, where the variables of rolling force, effective strain rate, and effective strain were analyzed. The simulations were chosen from the literature and involved materials such as steel and aluminum, with thicknesses of 1.0 mm and 3.0 mm, reductions of 5%, 16.67%, and 30%, and friction coefficients of 0.08, 0.1, and 0.3. The results showed that SALF describes the above variables appropriately in qualitative and quantitative terms. However, based on the research results, domestic steel industries may now use a national program to simulate the cold flat rolling process to improve their processes and products.

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Section: Force Resultsmentioning

confidence: 89%

Section: Force Resultsmentioning

confidence: 92%

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The Analysis of the Cold Flat Rolling Process by Salf Program

Costa

Santos

2017

Mat. Res.

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“…2,3,24 Lostado et al 25 combined ANN, genetic algorithm (GA), regression trees and FEM to study the influence of input parameters on the weld geometries, thermal cycle and distortion during GMAW for weld optimization. Escribano et al 26 combined finite element analysis and data mining techniques to develop models to predict the optimized force, pressure etc. during skin-pass rolling of steel sheets in real time applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Das

Pratihar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the present study, machine learning algorithms have been used to predict residual stress during electron beam welding of stainless steel using the information of input process parameters and natural frequency of vibrations. Accelerating voltage, beam current and welding speed have been considered as input process parameters. Both residual stress and natural frequencies of vibration of the weld obtained using each set of the input parameters are measured experimentally. A number of machine learning algorithms, namely M5 algorithm-based Model Trees Regression, Random forest, Support Vector Regression, Reduced Error Pruning Tree, Multi-layer perceptron, Instance-based k-nearest neighbor algorithm, and Locally weighted learning have been used for the said purpose. Support vector regression and Locally weighted learning are found to perform consistently good and bad, respectively. The predicted welding residual stresses have been validated experimentally through X-ray diffraction (XRD) and good agreements are obtained. In addition, statistical tests are conducted, and the estimated reliability values of the employed models are analyzed through Monte-Carlo simulations.

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“…Except for a few are submitted to the user as a finished product data, the vast majority of process data are used inadequate. In fact a lot of important information is hidden behind these massive data [4,5] . If a suitable data mining method is fund up, we can find the mill stiffness from the process data online.…”

Section: Introductionmentioning

confidence: 99%

Stiffness analysis method of rolling mill based on process data

Liu

Yan²,

Chen³

2015

Proceedings of the 2015 International Conference on Advanced Engineering Materials and Technology

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Mill stiffness is an important parameter for strip rolling mill, but it is difficult to online detect. When the wide of strip are changed, or the rollers are changed, the mill stiffness will change slightly. This change can affect the thickness precision and flatness precision significantly. Making full use of the massive process data, the mill stiffness hidden in the data was found out by data mining. Firstly, according to the structure and principle of the rolling mill, the vertical thickness error equation and transverse thick error equation were established. According the error tracing theory, the stiffness tracing formulas were established. With the experiment data of 300 experimental four roll rolling mill, the stiffness analysis of rolling mill stiffness data were reliable. Experiment results showed that the plate thickness and shape control effect were good, using the rolling stiffness values from analysis.

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Modelling a Skin-Pass Rolling Process by Means of Data Mining Techniques and Finite Element Method

Cited by 13 publications

References 8 publications

The Analysis of the Cold Flat Rolling Process by Salf Program

The Analysis of the Cold Flat Rolling Process by Salf Program

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Stiffness analysis method of rolling mill based on process data

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