| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Roland, Wolfgang
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2023Journal of Applied Polymer Scienc / Investigation of interdiffusion between compatible polymers under static and co‐extrusion processing conditionscitations
- 2022Materials Today: Proceedings / Application of a novel testing device to characterize the layer adhesion of co-extruded polymer sheetscitations
- 2022Polymers / Multi-dimensional regression models for predicting the wall thickness distribution of corrugated pipescitations
- 2022Polymers / Improving layer adhesion of co-extruded polymer sheets by inducing interfacial flow instabilitiescitations
- 2022Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipescitations
- 2019Selected Topics of Modeling Transport Phenomena in Single-Screw Extrusion : Viscous Dissipation, Melt-Conveying, and Mixing
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article
Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes
Abstract
<jats:p>Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimizing wall thickness distribution include adaptation of the mold block geometry and structure optimization. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally—an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modeling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimizing the wall thickness distribution.</jats:p>