Modeling and exploiting the strip tension influence on surface imprinting during temper rolling of cold-rolled steel

Li, Xinyang; Schulte, Christopher; Abel, Dirk; Teller, Marco; Lohmar, Johannes

doi:10.1016/j.aime.2021.100045

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Furthermore, according to industrial practice lower strip tension in skin-pass rolling causes higher imprinting [19]. This effect was also investigated in preliminary work by Li et al [20] and validated using multi-scale finite element (FE) simulations. Along with a higher thickness reduction, decreased strip tension causes higher average roll pressure and consequently increases the transfer ratio.…”

Section: Fig 1 Stribeck Curve With the Lubricant's Dynamic Viscosity ...mentioning

confidence: 93%

Modeling and control of tribological properties for subsequent forming process in skin-pass rolling

2023

Materials Research Proceedings

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Abstract. The surface roughness including average roughness (Ra) and peak number (RPc) after skin-pass rolling influences the strip’s product properties, such as its friction coefficient in the subsequent sheet metal forming process. In order to both ensure an optimal tribological behavior and fulfill the process requirements of the following forming steps, predefined roughness combinations of Ra and RPc should be attained in skin-pass rolling. In this work, a preliminary friction model relating friction and combination of Ra and RPc is determined using flat die drawing tests for DC04 steel. The model is validated by deep drawing tests and embedded in a roughness control system for skin-pass rolling. To show a proof of concept for independent control of tribological properties from strip thickness, a simulation framework is implemented in Matlab/Simulink. The results indicate that the friction coefficient can be controlled between 0.0924 and 0.1003 for the considered test scenario (which is) characterized by a normal pressure of 75kN and a relative speed of 10mm/s.

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Section: Fig 1 Stribeck Curve With the Lubricant's Dynamic Viscosity ...mentioning

confidence: 93%

Modeling and control of tribological properties for subsequent forming process in skin-pass rolling

2023

Materials Research Proceedings

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“…When the strip steel is immersed or sprayed with acid on its surface, the iron oxides are removed by a chemical reaction with the hydrochloric acid. At the moment, the amount of ferrous ion (Fe 2+ ) in the solution increases, and the concentration of hydrogen chloride (HCl) decreases accordingly [3]. During the production process, it is necessary to supplement acid to the solution at the right time according to the speed of chemical reaction.…”

Section: Introductionmentioning

confidence: 99%

A multi-task prediction method for acid concentration based on attention-CLSTM

Zhu

Liu

Cao

et al. 2022

Preprint

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The accurate measurement of acid concentration, including hydrogen chloride (HCl) and ferrous chloride concentrations (FeCl2), is a critical part of ensuring the quality of strip steel pickling. In this study, a multi-task attention convolutional long short-term memory (MACL) neural network model was proposed to predict hydrogen ion and ferrous ion concentrations simultaneously. Firstly, in order to extract significant information from the input sequence, an attention mechanism was added to the model to calculate the correlation between the input features and the acid concentration at each time step. Next, multi-task learning explores the connections between the two tasks and exploits hidden constraints to improve prediction accuracy. Finally, deep features were extracted through convolutional long short-term memory (CLSTM) neural network, and the acid concentration was predicted. The proposed MACL model was compared with other popular prediction models. The experimental results show that proposed MACL model generally outperforms other models, indicating that proposed model has excellent predictive performance and effectiveness.

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Reduced Three-Dimensional Model for Cold Rolling Simulation of Aluminium Alloys

Pachnek,

Simon,

Zeman

2024

Lecture Notes in Mechanical Engineering

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Mathematical modelling and computational analyses allow to deepen the understanding of manufacturing processes, to ensure high product quality and to reduce experimental efforts for the optimization of product properties and production processes. In order to predict the evolution of flat rolled aluminium product properties and their interdependence with process parameters, virtual representations of the entire process chain including process models of hot rolling, coiling, and cold rolling operations are highly beneficial. Starting from the solid foundation of a comprehensively validated 3D model of the hot rolling process of various aluminium alloys, the present work presents a 3D thermo-mechanical cold rolling simulation model using the commercial finite element code LS-DYNA. The material behaviour is considered by means of a user defined material model according to a modified dislocation density based flow model. The calculated microstructural and deformation history of the hot rolled strip is considered as input for the developed cold rolling model allowing for seamless prediction of product properties. For numerical studies, a modular modelling approach is pursued, comprising tailor-made simplifications depending on actual applications and research problems. For time efficient multi-pass simulations in an industrial environment, a reduced model with plane strain conditions is derived that extends the predictability of product properties and process parameters from hot rolling over coiling to cold rolling. Simulation results and computational costs are compared with detailed 3D models. Strip tensions can be adjusted showing the expected effects on roll separating forces and minimum rolled thickness. To ensure the desired strip tensions in the numerical model, a sensor-based control of strip tensions is implemented. For validation purposes, measured rolling forces from industrial pass schedules for 6xxx series alloys are compared against simulation results.

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Modeling and exploiting the strip tension influence on surface imprinting during temper rolling of cold-rolled steel

Cited by 5 publications

References 21 publications

Modeling and control of tribological properties for subsequent forming process in skin-pass rolling

Modeling and control of tribological properties for subsequent forming process in skin-pass rolling

A multi-task prediction method for acid concentration based on attention-CLSTM

Reduced Three-Dimensional Model for Cold Rolling Simulation of Aluminium Alloys

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