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Localised prediction accuracies of knowledge-guided symbolic regression models for melt conveying in single-screw extruders

Dataset

Description

Excel spreadsheet with numeric values and textual description.

Contains the mean absolute errors (MAE) of symbolic regression models for predicting the dimensionless melt flow rate in single-screw extruders, averaged across all repetitive runs and distinct sub-regions for the dimensionless down-channel pressure gradient Pi_p and the power-law index n of the polymer melt.

The models were created from numerical simulation data with four different cases of integrated domain knowledge:

- Case 1: theory of similarity only,
- Case 2: additional derived input features for pure pressure flow,
- Case 3: logarithmic scaling of the dimensionless flow rate with channel aspect ratio as derived input feature,
- Case 4: theoretical approximation equation for superimposed drag and pressure flow in dimensionless space.
Date made available19 Mar 2025
PublisherZenodo

UN SDGs

This dataset contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production

Fields of science

  • 205012 Polymer processing
  • 203037 Computational engineering
  • 102019 Machine learning

JKU Focus areas

  • Digital Transformation
  • Sustainable Development: Responsible Technologies and Management

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