Artificial Neural Network Modeling for Prediction of Roll Force During Plate Rolling Process

Rath, Sushant; Singh, Alankrita; Bhaskar, Umesh Kumar; Krishna, B.; Santra, B.; Rai, DC; Neogi, Nirbhar

doi:10.1080/10426910903158249

Cited by 29 publications

(10 citation statements)

References 9 publications

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“…Some steel products are produced by VGR but very few researches have surveyed the rolling force wave motion in VGR processing. An accurate prediction of the rolling force is very important for the successful operation of the mathematical models used to calculate the roll gaps of each pass to achieve the final desired gauge . The finite element method (FEM) has been widely used to analyze rolling processes such as the plate view shape, rolling force, thermal field, strain−stress field, microstructure, etc., but the use of FEM to analyze VGR processing has hardly ever been attempted.…”

Section: Introductionmentioning

confidence: 99%

The Wave Motion of the Rolling Force during Variable Gauge Rolling

Lü

Zhu

et al. 2013

steel research int.

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The variable gauge rolling process is a new technology designed to produce flat products of different thicknesses such as longitudinal profile (LP) plates and tailor rolled blanks (TRB), which are used in bridge building, shipbuilding, and automobile manufacturing, etc., to decrease the weight and the welding times, etc. In this paper, a series of numerical studies on the wave motion of rolling force in variable gauge rolling (in three stages: down rolling, flat rolling, and upper rolling) were carried out using the finite element method to obtain the deformation of the work piece at different stages of rolling. The rolling force and its wave motion during rolling were analyzed under a variety of reduction ratios, including the coefficient of friction, initial thickness of the plates, and diameters of the rolls. The rolling force obviously increases as the reduction in the down rolling process increases, and then decreases during upper rolling. The rolling force consists of two abrupt wave motions in the transitional zones during the down to flat rolling stage, and the flat to upper rolling stage. These results are significant with regards to precisely controlling the deformation of workpiece in the transition zone during variable gauge rolling.

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Section: Introductionmentioning

confidence: 99%

The Wave Motion of the Rolling Force during Variable Gauge Rolling

Lü

Zhu

et al. 2013

steel research int.

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“…AziGuLi et al used the extreme learning machine and self-learning model to predict the rolling force in [16]. The artificial neural network model was used to predict the rolling force and acquired a high precision in [18]. Mahmoodkhani et al developed an online rolling force prediction tool by combining the finite element model with the artificial neural network in [19].…”

Section: The Rolling Force Modelmentioning

confidence: 99%

Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill

Wang

Yin

et al. 2020

IEEE Access

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The optimization of rolling schedule is the main content of tandem cold rolling which will affect the quality of products directly. A rolling schedule with the objectives of minimum energy consumption, relative power margin and slippage preventing is established. First, in order to make the rolling schedule more accurate in the calculation process, a mathematical model combines with deep neural network is proposed to calculate the rolling force. Second, a multi-objective particle swarm optimizer with dynamic opposition-based learning is proposed to optimize the rolling schedule. It has a new particle learning strategy to update the moving position of particles. Moreover, opposition-based learning is proposed to make the particles jump out of local optima. Finally, the experiments are carried out based on the field data. Simulation results demonstrate that the accuracy of the rolling force is greatly improved. The proposed algorithm has a promising performance on both diversity and convergence. At the same time, the optimized rolling schedule can well balance the rolling power and prevent slipping between five stands comparing with the original rolling schedule. INDEX TERMS Multi-objective optimization, tandem cold rolling, deep neural network, rolling force, rolling schedule.

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“…The prediction error of the proposed approach was less than 5%. Rath et al [25] applied ANN for prediction of roll force. They used a feed forward network as an ANN architecture and a back propagation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

Park¹,

Kim²,

Kim

et al. 2020

IEEE Access

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A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is important to precisely detect and sense values of manufacturing factors including plate thickness and roll force in each rolling pass. For example, the estimation or prediction of the in-process thickness is utilized to select the control values (e.g., roll gap) in the next pass of rolling. However, adverse manufacturing conditions can interfere with accurate detection for such manufacturing factors. Although the state-of-the-art gamma-ray camera can be used for measuring the thickness, the outputs from it are influenced by adverse manufacturing conditions such as the high temperature of plates, followed by the evaporation of lubricant water. Thus, it is inevitable that there is noise in the thickness estimation. Furthermore, installing such thickness measurements for each passing step is costly. The precision of the thickness estimation, therefore, significantly affects the cost and quality of the final product. In this paper, we present machine learning (ML) technologies and models that can be used to predict the in-process thickness in the SPM operation, so that the measurement cost for the inprocess thickness can be significantly reduced and high-quality steel plate production can be possible. To do so, we investigate most-known technologies in this application. In particular, Data Clustering based Machine Learning (DC-ML), combining clustering algorithms and supervised learning algorithms, is introduced. To evaluate DC-ML, two experiments are conducted and show that DC-ML is well suited to the prediction problems in the SPM operation. In addition, the source code of DC-ML is provided for the future study of machine learning researchers. INDEX TERMS Intelligent manufacturing systems, machine learning, regression analysis, steel industry, thickness control.

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Artificial Neural Network Modeling for Prediction of Roll Force During Plate Rolling Process

Cited by 29 publications

References 9 publications

The Wave Motion of the Rolling Force during Variable Gauge Rolling

The Wave Motion of the Rolling Force during Variable Gauge Rolling

Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill

Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning

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