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Roland W, Marschik C, Kommenda M, Haghofer A, Dorl S, Winkler S. Predicting the Non-Linear Conveying Behavior in Single-Screw Extrusion: A Comparison of Various Data-Based Modeling Approaches used with CFD Simulations. INT POLYM PROC 2021. [DOI: 10.1515/ipp-2020-4094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
The traditional approach to modeling the polymer melt flow in single-screw extruders is based on analytical and numerical analyses. Due to increasing computational power, data-driven modeling has grown significantly in popularity in recent years. In this study, we compared and evaluated databased modeling approaches (i. e., gradient-boosted trees, artificial neural networks, and symbolic regression models based on genetic programming) in terms of their ability to predict – within a hybrid modeling framework – the three-dimensional non-linear throughput-pressure relationship of metering channels in single-screw extruders. By applying the theory of similarity to the governing flow equations, we identified the characteristic dimensionless influencing parameters, which we then varied to create a large dataset covering a wide range of possible applications. For each single design point we conducted numerical simulations and obtained the dimensionless flow rate. The large dataset was divided into three independent sets for training, interpolation, and extrapolation, the first being used to generate and the remaining two to evaluate the models. Further, we added two features derived from expert knowledge to the models and analyzed their influence on predictive power. In addition to prediction accuracy and interpolation and extrapolation capabilities, we evaluated model complexity, interpretability, and time required to learn the models. This study provides a rigorous analysis of various data-based modeling approaches applied to simulation data in extrusion.
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Affiliation(s)
- W. Roland
- Institute of Polymer Extrusion and Compounding, Johannes Kepler University Linz , Linz , Austria
- Pro2Future GmbH , Linz , Austria
| | - C. Marschik
- Institute of Polymer Extrusion and Compounding, Johannes Kepler University Linz , Linz , Austria
- CHASE GmbH , Linz , Austria
| | - M. Kommenda
- Josef Ressel Centre for Symbolic Regression, University of Applied Sciences Upper Austria , Hagenberg , Austria
- Heuristic and Evolutionary Algorithm Laboratory, University of Applied Sciences Upper Austria , Hagenberg , Austria
| | - A. Haghofer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria , Hagenberg , Austria
| | - S. Dorl
- Bioinformatics Research Group, University of Applied Sciences Upper Austria , Hagenberg , Austria
| | - S. Winkler
- Heuristic and Evolutionary Algorithm Laboratory, University of Applied Sciences Upper Austria , Hagenberg , Austria
- Bioinformatics Research Group, University of Applied Sciences Upper Austria , Hagenberg , Austria
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