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

Wang, Yunlong; Wang, Jinkuan; Yin, Chunhui; Zhao, Qiang

doi:10.1109/access.2020.2990904

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…More recently, Wang et al [21] used deep neural networks combined with a mathematical model to predict the rolling force for a tandem cold rolling process. We adopted a similar approach to their work, but a more difficult problem arises for hot mills operating above the recrystallization temperature.…”

Section: B Related Workmentioning

confidence: 99%

Hybrid Model of Mathematical and Neural Network Formulations for Rolling Force and Temperature Prediction in Hot Rolling Processes

Hwang

Kim

et al. 2020

IEEE Access

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Steelmaking requires precise calculation at several steps of the manufacturing processes. We focus on the hot rolling process using Steckel mills, almost the end step in steel coil manufacturing. The rolling process is a type of plastic working in which a slab passes between two rolls and is stretched to reach the target thickness. It is necessary to predetermine the exact rolling force to obtain a coil with an accurate thickness after the rolling process. First, we introduced a machine learning model for calculating the rolling force, which can be used in-line in real plants. However, a direct calculation of the rolling force can cause stability problems, because the model output directly affects the process. In order to avoid such a problem, we determined a special temperature of the coil by inverse calculation of the classical mechanical model of hot rolling and set it as the model output value. As learning models, deep neural networks (DNN) and gradient boosting-based decision tree models were used. We preprocessed the collected process history data and added artificial features to the model input by creating physical variables used in the classical models. Moreover, to supplement the black-box nature of DNN, feature importance was analyzed from the decision tree model, and utilization and interpretation of each feature in the process are presented. Thus, our methods take advantage of both the classical mathematical model and the deep neural network model.

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Section: B Related Workmentioning

confidence: 99%

Hybrid Model of Mathematical and Neural Network Formulations for Rolling Force and Temperature Prediction in Hot Rolling Processes

Hwang

Kim

et al. 2020

IEEE Access

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show abstract

Section: Introductionmentioning

confidence: 99%

“…Additionally, researchers have also tried to use various existing or improved optimisation algorithms applied in various research areas to optimise and improve the performance of learning models. [13][14][15][16][17] The shape control of the cross-section of the steel has the characteristics of high dimension and strong nonlinearity, and it is difficult to directly analyse. A deep neural network (DNN) has a powerful nonlinear fitting ability and can be freely approximated by any nonlinear continuous function.…”

Section: Introductionmentioning

confidence: 99%

Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Wang,

Song,

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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In this article, a high-precision edge drop prediction model for hot-rolled silicon steel based on a rolling mechanism and deep neural network (DNN) is proposed. Considering the initial roll shape, roll wear and roll thermal expansion, a mechanism model of the roll system is established according to the hot rolling 4-high finishing mill. In order to reduce the data dimension and effectively improve the prediction accuracy, the random forest algorithm is employed to analyse the feature parameters that affect the edge drop of hot-rolled silicon steel. Finally, the DNN is introduced to establish an accurate silicon steel edge drop prediction model based on mechanism data and rolling data. The experimental results demonstrated that the proposed DNN model has strong generalisation and reliability with a squared correlation coefficient ( R2) of 0.9656, and it is suitable for application in hot-rolled silicon steel lines for predicting edge drop. Furthermore, the proposed model has a certain reference value for the hot rolling process.

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“…Wireless Communications and Mobile Computing multiobjective particle swarm algorithm to optimize the objective functions of equal relative load and slip rate, and the method was applied to a five-stand tandem mill [15]. Wang et al proposed a multiobjective particle swarm optimizer with dynamic opposition-based learning to optimize the rolling schedule with the objectives of minimum energy consumption, relative power margin, and slippage preventing [16]. Hu et al selected five objectives as optimization objectives and used a multiobjective evolutionary algorithm based on decomposition and Gaussian mixture model to design the rolling schedule [17].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

Yang

Liu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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The single-stand reversible cold rolling mill is important equipment in the production of steel strips. The rolling schedule is the core technological content in the strip production of the single-stand reversible cold rolling mill. The scientific rolling schedule is the fundamental guarantee for the production capacity of the rolling mill, product quality, accuracy, shape quality, energy saving, and consumption reduction. This paper takes the dynamic rolling process of single-stand cold rolling as the research object, the purposes of increasing production capacity, saving energy, and reducing consumption are achieved by optimizing the rolling schedule. Based on the study of the mechanism model and the analysis of a large number of field measured data, a slice of mathematical models of the rolling process suitable for engineering calculation are proposed, and a few objective functions suitable for the single-stand reversible cold rolling process are designed. On this basis, the artificial fish swarm algorithm is improved into a multiobjective optimization algorithm for the optimization of rolling schedule, and the optimal rolling load distribution scheme is obtained. Finally, the optimization method of rolling schedule proposed in this paper is applied to the actual rolling production. The results show that the proposed method can improve productivity and save energy compared with the empirical rolling schedule, and the feasibility and validity of the proposed algorithm are verified.

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

Cited by 10 publications

References 26 publications

Hybrid Model of Mathematical and Neural Network Formulations for Rolling Force and Temperature Prediction in Hot Rolling Processes

Hybrid Model of Mathematical and Neural Network Formulations for Rolling Force and Temperature Prediction in Hot Rolling Processes

Edge drop prediction of hot-rolled silicon steel based on a mechanism and data model

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

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