The Optimal Design for the Production of Hot Rolled Strip with “Tight Oxide Scale” by Using Multi-objective Optimization

Tao, Jia; Liu, Zhenyu; Hu, Hengfa; Wang, Guodong

doi:10.2355/isijinternational.51.1468

Cited by 21 publications

(13 citation statements)

References 14 publications

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“…The process units connected the three layers could be adjust to minimize the error between the calculated output and the target data during the training procedure. Bayesian regularization training algorithm was employed to train the network, which was a modification of the Levenberg‐Marquardt algorithm and effective to determine the optimal network parameters . By minimizing a combination of mean square error and the mean square of the network weights, the network generalization was improved.…”

Section: Mechanical Property Prediction Modelingmentioning

confidence: 99%

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Zhou

Cao

et al. 2018

steel research int.

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To solve the contradiction between consumer's personal demand and mass production of steel enterprise, the optimal design for hot strip rolling of steel attracts researcher's attention. The core of the ensemble system is the artificial neural network (ANN) model, which is based on the industrial data. However, industrial data are difficult to be used for modeling because of their high dimension, low quality, and unbalanced. In current work, the method for industrial data processing is proposed for microalloyed steel. Based on the treated data, the mechanical property prediction models are established with the relative error of ±8% for yield strength (YS), ±6% for tensile strength (TS), and the absolute error of ±4% for elongation (EL), respectively. Combined with the multi‐objective optimization algorithm, the ANN models are implemented for designing the hot rolling process for S360 grade steel to control the stability of product quality.

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Section: Mechanical Property Prediction Modelingmentioning

confidence: 99%

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Zhou

Cao

et al. 2018

steel research int.

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“…In our previous work, we optimized the hot rolling process parameters of SPA-H steel to achieve the desired mechanical properties. 10) In order to ensure the 510 L steel with tight oxide scales on the surface and the mechanical properties at the same time, we 10) applied the multi-objective particle swarm optimization to the design of hot rolling processes.…”

Section: High Dimensional Data-driven Optimal Design For Hot Strip Romentioning

confidence: 99%

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

Cao

Zhou

et al. 2017

ISIJ Int.

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Recently, hot strip rolling processes are required to be agile and accurate in order to meet more and more diverse market demands. Under such circumstances, the traditional processing optimization by timeconsuming pilot experiments becomes difficult. To realize this target, core models of processing and mechanical properties are often established by neural network methods, which are used to handle nonlinear multi-variant systems. In modeling processing and mechanical properties, we found that the abnormal values in industrial big data could result in wrong predictions for the relationships between processing and properties. In the present work, therefore, data processing has been developed to prevent misleading predictions, which was performed by eliminating the redundant, abnormal, and imbalanced data before modeling. The Bayesian neural network was used to construct the modeling of mechanical properties for hot rolled C-Mn steels, which demonstrated that the accuracies between the measured and predicted values were within ± 10% and ± 5% for strength and elongation, respectively, providing a reliable model for the optimal process design. By applying the multi-objective optimization algorithm named Strength Pareto Evolutionary Algorithm 2 (SPEA2), the hot strip rolling processes for C-Mn steels were optimized in order for either stabilizing variations of properties or upgrading mechanical properties. Industrial trials were extensively carried out, showing good agreements with the optimized processes.

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“…This is the reason why the tight oxide scale offers a great potential to further develop the pickle-free microalloyed steels. Previous studies [1,6] indicated that the tight oxide scale consists of more than 75 % magnetite and retained wustite, with the oxide-layer thickness of less than 15 μm. The deformation and tribological features of the oxide scale in cooling process after hot rolling is therefore of fundamental and great practical interest.…”

Section: Introductionmentioning

confidence: 99%

“…Promising potential for the Ni-V-Ti microalloyed steels lies in the tight oxide scale formed on the surface of hot-rolled strip due to thermal oxidation at elevated temperature [1][2][3][4]. The tight oxide scale is expected to deform with steel substrate without cracking, and may act as self-lubricant at tribological contact surfaces between the workpiece and tool during downstream processing [5,6].…”

Section: Introductionmentioning

confidence: 99%

Tribological Analysis of Oxide Scales during Cooling Process of Rolled Microalloyed Steel

Jiang

Xiang

Zhao

et al. 2014

AMR

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The composition and phase transformation of oxide scale in cooling process (after hot rolling) of rolled microalloyed steels affect tribological features of rolled strip and downstream process, and the produced steel surface quality. In this study, physical simulation of surface roughness transfer during cooling process with consideration of ultra fast cooling (UFC) was carried out in Hille 100 experimental rolling mill, the obtained oxide scale was examined with SEM to show its surface and phase features. The results indicate that the surface roughness of the oxide scale increases as the final cooling (coiling) temperature increases, and the flow rate of the introduced air decreases. The cracking of the surface oxide scale can be improved when the cooling rate is 20 °C/s, the strip reduction is less than 12%, and the thickness of oxide scale is less than 15 μm, independent of the surface roughness. A cooling rate of more than 70 °C/s can increase the formation of retained wustite and primary magnetite precipitates other than the precipitation of α-iron. This study is helpful in optimising the cooling process after hot rolling of microalloyed steels to obtain quality surface products. KeywordsOxide scale, Surface roughness, Ultra fast cooling, Microalloyed steel, Tribology Abstract. The composition and phase transformation of oxide scale in cooling process (after hot rolling) of rolled microalloyed steels affect tribological features of rolled strip and downstream process, and the produced steel surface quality. In this study, physical simulation of surface roughness transfer during cooling process with consideration of ultra fast cooling (UFC) was carried out in Hille 100 experimental rolling mill, the obtained oxide scale was examined with SEM to show its surface and phase features. The results indicate that the surface roughness of the oxide scale increases as the final cooling (coiling) temperature increases, and the flow rate of the introduced air decreases. The cracking of the surface oxide scale can be improved when the cooling rate is 20 C/s, the strip reduction is less than 12 %, and the thickness of oxide scale is less than 15 µm, independent of the surface roughness. A cooling rate of more than 70 C/s can increase the formation of retained wustite and primary magnetite precipitates other than the precipitation of α-iron. This study is helpful in optimising the cooling process after hot rolling of microalloyed steels to obtain quality surface products. IntroductionMicroalloyed steels are widely used in various structures and automotive components. Promising potential for the Ni-V-Ti microalloyed steels lies in the tight oxide scale formed on the surface of hot-rolled strip due to thermal oxidation at elevated temperature [1][2][3][4]. The tight oxide scale is expected to deform with steel substrate without cracking, and may act as self-lubricant at tribological contact surfaces between the workpiece and tool during downstream processing [5,6]. Thus lower shear strength, good adhesion and den...

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The Optimal Design for the Production of Hot Rolled Strip with “Tight Oxide Scale” by Using Multi-objective Optimization

Cited by 21 publications

References 14 publications

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

Tribological Analysis of Oxide Scales during Cooling Process of Rolled Microalloyed Steel

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