Analysis and design of an Extended Kalman Filter for the plate temperature in heavy plate rolling

Speicher, Katrin; Steinboeck, Andreas; Kugi, Andreas; Wild, Daniel; Kiefer, Thomas

doi:10.1016/j.jprocont.2014.06.004

Cited by 13 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The validation of the method with numerical simulation resulted in an accurate temperature estimation with EKF. Speicher et al [68] used full and reduced EKF to estimate plate temperature in heavy plate rolling based on a few thermocouple measurements. They used a similar approach to discretise a partial differential equation model of thermodynamics using a finite difference method.…”

Section: Other Forming Processesmentioning

confidence: 99%

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

View full text Add to dashboard Cite

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (`soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (`sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.

show abstract

Section: Other Forming Processesmentioning

confidence: 99%

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The calculation of the material parameters using material models as well as the calculation of their effective (averaged) values requires the strip temperature (and its through-thickness distribution) as an input. For application in a rolling plant, this temperature input can be provided by using temperature measurements, models, or observers (see, e.g., [42][43][44]).…”

Section: Rolling Forcementioning

confidence: 99%

Asymmetric hydrodynamic roll gap model and its experimental validation

Müller

Steinboeck

Prinz

et al. 2018

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In tandem hot strip rolling mills, different friction between the rolls and the strip material on the upper and lower strip surface can occur due to asymmetric surface temperatures or different conditions of oil lubrication. To capture these effects, this paper presents a hydrodynamic roll gap model with asymmetric friction. Based on similarities between the rolled material and viscous fluids, fluid mechanics theory is used to derive this model. Due to the nature of this model, the influence of the rolling speed is inherently taken into account, which allows an accurate prediction of the rolling force and the forward slip. As an analytic solution for the hydrodynamic roll gap model is available, it is well suited for online applications in rolling plants. For validation of the proposed model, an experiment with asymmetric work roll roughness was performed. A specimen of steel strip with copper pins inserted was repeatedly rolled to visualize the material flow inside the roll gap for multiple passes. The resulting deformed copper pins were cut out of the strip and show good agreement with the deformation profiles calculated by the developed model.

show abstract

“…In order to reconstruct the spatial-temporal temperature distribution from sparsely available measurement data such as pyrometers, model-based state estimation methods are inevitable. Speicher et al (2014) designed an extended Kalman filter (EKF) for the plate rolling process and Zheng and Li (2011) for the strip rolling, respectively. Force measurements are used in Havinga and Mandal (2017) to estimate product properties by exploiting an interpolation process model obtained from a finite element model.…”

Section: Introductionmentioning

confidence: 99%

Reduced order modelling for spatial-temporal temperature and property estimation in a multi-stage hot sheet metal forming process

Kloeser¹,

Martschin²,

Meurer³

et al. 2021

Preprint

View full text Add to dashboard Cite

A concise approach is proposed to determine a reduced order control design oriented dynamical model of a multi-stage hot sheet metal forming process starting from a high-dimensional coupled thermo-mechanical model. The obtained reduced order nonlinear parametric model serves as basis for the design of an Extended Kalman filter to estimate the spatial-temporal temperature distribution in the sheet metal blank during the forming process based on sparse local temperature measurements. To address modeling and approximation errors and to capture physical effects neglected during the approximation such as phase transformation from austenite to martensite a disturbance model is integrated into the Kalman filter to achieve joint state and disturbance estimation. The extension to spatial-temporal property estimation is introduced. The approach is evaluated for a hole-flanging process using a thermo-mechanical simulation model evaluated using LS-DYNA. Here, the number of states is reduced from approximately 17 000 to 30 while preserving the relevant dynamics and the computational time is 1000 times shorter. The performance of the combined temperature and disturbance estimation is validated in different simulation scenarios with three spatially fixed temperature measurements.

show abstract

Analysis and design of an Extended Kalman Filter for the plate temperature in heavy plate rolling

Cited by 13 publications

References 15 publications

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

Asymmetric hydrodynamic roll gap model and its experimental validation

Reduced order modelling for spatial-temporal temperature and property estimation in a multi-stage hot sheet metal forming process

Contact Info

Product

Resources

About