Online Parameter Estimation for Adaptive Feedforward Control of the Strip Thickness in a Hot Strip Rolling Mill

Prinz, Katharina; Steinboeck, Andreas; Müller, Martin; Ettl, Andreas; Schausberger, Florian; Kugi, Andreas

doi:10.1115/1.4043575

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…The number of neurons in the hidden layer of the model created for the determination of UTS is an important criterion in determining the error. 28,29…”

Section: Methodsmentioning

confidence: 99%

“…The number of neurons in the hidden layer of the model created for the determination of UTS is an important criterion in determining the error. 28,29 For this, after input and target values are introduced to the system, the prediction success starting from 1 neuron to 20 neurons has been tested. As a result, it was decided to have 10 neurons in the hidden layer and modeling and estimation study was made on it.…”

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

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A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

Güvenç

Eren

Başar

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Joining of aluminum (Al) and copper (Cu) materials used in aerospace, automotive, chemistry, nuclear, electrical-electronic and energy sector with traditional fusion welding methods has some negative consequences. One of the major negativities is the serious decrease in the mechanical properties of the materials as a result of the high temperature that occurs during welding. Therefore, Friction Stir Welding (FSW) is of great importance in joining materials which are dissimilar and have low melting temperatures. In this study, a hybrid artificial neural network (ANN) model has been developed to predict the relationship between FSW parameters and ultimate tensile strength and to increase the prediction performance of ANN using heuristic algorithms by welding Al and Cu plates with FSW. The used hybrid approach consists of ANN and Particle Swarm Optimization/Teaching Learning Based Optimization techniques. The welding applications conducted with three different welding parameters. These parameters, rotation speed (525, 1025, 1525 rpm), welding speed (50, 75, 100 mm/min) and tool offset (0–0.75–1.5 mm) were determined as input parameters of the developed ANN model. Tensile and microhardness tests were applied to determine the mechanical properties in the experimental studies after welding process. In addition, Scanning Electron Microscope analysis and X-ray diffraction were used for microstructure and characterization of the fracture surface and the results obtained were interpreted and examined.

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“…The number of neurons in the hidden layer of the model created for the determination of UTS is an important criterion in determining the error. 28,29…”

Section: Methodsmentioning

confidence: 99%

Section: Artificial Intelligence Techniquesmentioning

confidence: 99%

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

Güvenç

Eren

Başar

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…These quality issues contribute a lot to establish product demand in the completive market. Prinz et al [26] presented online parameter estimation and strip thickness control methodology in a hot strip rolling mill, which avoids the nonuniform strip exit thickness and improves the product's quality.…”

Section: C: Quality Managementmentioning

confidence: 99%

IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review

Akarte

Khanzode

Iqbal

et al. 2021

Trans Indian Inst Met

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Integrated steelmaking operations, from ironmaking to final steel categories/forms, are a complex industrial system. Decision-makers, in addition to the domain knowledge, need to understand many facets related to operations management, process execution, involved risk factors and material handling aspects, etc. The solution approach must address trade-offs, uncertainties, multiple stages, multiple objectives, many decision variables, and constraints. Literature has presented several Industrial Engineering approaches like multi-objective optimisation, heuristics, evolutionary algorithm, Simulations, lean sixsigma, etc., to address the iron and steel industry's complex problems like integrated production planning. The manuscript reviews recent application of these IE tools/solution approaches in iron and steel industries to enhanced productivity/competitiveness. A Systematic Literature Review (SLR) methodology has been adopted. The review will help the practitioners and academicians to know the different problem areas about iron and steel industries discussed in the literature, and the solution approaches used to solve the problem. Keywords Industrial Engineering Á Optimization Á Iron and steel industry Á Productivity improvement Á Solution approaches 1 Introduction Industrial Engineering (IE), also referred to as Industrial and Systems Engineering or Industrial Engineering and Management or Management Science is a multidisciplinary branch that covers several productivity improvement techniques. The branch of knowledge started developing in the early twentieth century when F. W. Taylor invented principles of scientific management, and Henry Ford implemented assembly lines to improve productivity at reduced costs in manufacturing processes. Over a period, developments like Industry 4.0, predictive analytics, Operations Research, Toyota Production Systems, TQM & Six Sigma, Ergonomics, and Human Factors Engineering, created a vast body of knowledge creation/dissemination. Institute of Industrial Engineers in January 2019 created a document representing a repository of essential knowledge areas for Industrial Engineering [1].

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“…In order to improve the quality of steel products, one key indicator is to control the thickness of the strip exit. Highprecision control of it not only saves raw materials and increases utilization rate, but also reduces unnecessary carbon emissions [1], [2], [3]. However, the real-time changes in various state parameters during the rolling process affect the thickness of the strip exit.…”

Section: Introductionmentioning

confidence: 99%

“…It can fully utilize context information in sequences, acquire more comprehensive feature representations, and better capture long-term dependencies and complex patterns in sequences. Furthermore, compared to BiLSTM, BiGRU has a simpler unit structure, lower model complexity and faster response time, making it highly suitable for predictive problem research [16], [17], [18]. However, the setting of hyper-parameters profoundly influences the performance of the model.…”

Section: Introductionmentioning

confidence: 99%

Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

Luo,

Chen,

Sun

et al. 2024

IEEE Access

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The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.INDEX TERMS BiGRU, parameter optimization, feature selection, marine predators algorithm, heuristic algorithm, strip thickness.

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Online Parameter Estimation for Adaptive Feedforward Control of the Strip Thickness in a Hot Strip Rolling Mill

Cited by 12 publications

References 30 publications

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

IE Tools for Boosting Competitiveness in Iron and Steel Industry: A Review

Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

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