Details
| Original language | English |
|---|---|
| Pages (from-to) | 11132-11137 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 53 |
| Issue number | 2 |
| Publication status | Published - 2020 |
Abstract
Manifold engineering applications are directly affected by temperature. For rubber or composite curing processes, temperature distributions over time inside the compounds are crucial for chemical cross-linking reactions. Most of these reactions occur subsequently to a heating process during product cool down. Online prediction of cooling phases is performed during the actual heating process and hence, final cure status can be estimated before the actual process finishes. Therefore, mold temperatures and heating duration can be adapted in regard to current ambient conditions, and hence product quality is increased. In order to achieve longterm thermal predictions for complex product geometries, simulating nonlinear thermal finite element models is unfeasible, due to high computational effort. Hence, a prediction-model is derived from finite element analysis using matrix export, linearization, model order reduction algorithms such as rational Krylov or iterative rational Krylov and correction of operating point deviation. A special remark is given to temperature dependent boundary conditions, choice of time discretization and choice of solving algorithm, to address arising conflicting goals between execution time and simulation accuracy. Eventually, a complete process simulation is performed during the task-cycle time on a PLC control with a sufficiently high accuracy.
Keywords
- Model Reduction, Prediction, Process Control, Simulation, Temperature Distributions
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 53, No. 2, 2020, p. 11132-11137.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Real-Time Prediction of Curing Processes using Model Order Reduction
AU - Frank, Tobias
AU - Zeipel, Henrik
AU - Wielitzka, Mark
AU - Bosselmann, Steffen
AU - Ortmaier, Tobias
N1 - Funding Information: This work was supported by the German Federal Ministry for Economic Affairs and Energy.
PY - 2020
Y1 - 2020
N2 - Manifold engineering applications are directly affected by temperature. For rubber or composite curing processes, temperature distributions over time inside the compounds are crucial for chemical cross-linking reactions. Most of these reactions occur subsequently to a heating process during product cool down. Online prediction of cooling phases is performed during the actual heating process and hence, final cure status can be estimated before the actual process finishes. Therefore, mold temperatures and heating duration can be adapted in regard to current ambient conditions, and hence product quality is increased. In order to achieve longterm thermal predictions for complex product geometries, simulating nonlinear thermal finite element models is unfeasible, due to high computational effort. Hence, a prediction-model is derived from finite element analysis using matrix export, linearization, model order reduction algorithms such as rational Krylov or iterative rational Krylov and correction of operating point deviation. A special remark is given to temperature dependent boundary conditions, choice of time discretization and choice of solving algorithm, to address arising conflicting goals between execution time and simulation accuracy. Eventually, a complete process simulation is performed during the task-cycle time on a PLC control with a sufficiently high accuracy.
AB - Manifold engineering applications are directly affected by temperature. For rubber or composite curing processes, temperature distributions over time inside the compounds are crucial for chemical cross-linking reactions. Most of these reactions occur subsequently to a heating process during product cool down. Online prediction of cooling phases is performed during the actual heating process and hence, final cure status can be estimated before the actual process finishes. Therefore, mold temperatures and heating duration can be adapted in regard to current ambient conditions, and hence product quality is increased. In order to achieve longterm thermal predictions for complex product geometries, simulating nonlinear thermal finite element models is unfeasible, due to high computational effort. Hence, a prediction-model is derived from finite element analysis using matrix export, linearization, model order reduction algorithms such as rational Krylov or iterative rational Krylov and correction of operating point deviation. A special remark is given to temperature dependent boundary conditions, choice of time discretization and choice of solving algorithm, to address arising conflicting goals between execution time and simulation accuracy. Eventually, a complete process simulation is performed during the task-cycle time on a PLC control with a sufficiently high accuracy.
KW - Model Reduction
KW - Prediction
KW - Process Control
KW - Simulation
KW - Temperature Distributions
UR - http://www.scopus.com/inward/record.url?scp=85096534423&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.273
DO - 10.1016/j.ifacol.2020.12.273
M3 - Conference article
VL - 53
SP - 11132
EP - 11137
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 2
ER -