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February 28, 2026Materials Today Advances0 citationsOpen Access

A dimensionless rheology-based extrusion map as a predictive tool for hot-melt extrusion processability

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DTDan TrunovUniversity of Chemistry and Technology, PragueLBLukáš BerčíkUniversity of Chemistry and Technology, PragueSKSamuel KriškaUniversity of Chemistry and Technology, Prague

Key Points

  • The aim is to create a predictive tool for assessing the processability of polymeric materials during hot-melt extrusion.
  • Evaluate powder feeding behavior using solid-state indicators like flowability index and friction ratio.
  • Characterize melt-processing using dimensionless numbers to capture viscous, elastic, and inertial effects.
  • Integrate the criteria into a first-order predictive model to identify stable extrusion processing windows.
  • Developed a processability map offering mechanistic insight into polymer behavior under various conditions.
  • Reduced reliance on empirical methods for process optimization.
  • Enhanced efficiency through early identification of processing limitations, resulting in less material waste and energy consumption.

Abstract

Predicting the processability of polymeric materials in hot-melt extrusion remains a critical challenge in continuous manufacturing due to the complex transition from powder feeding to melt flow under dynamic processing conditions. In this work, we present a unified and predictive methodology for construction an extrusion processability map based on dimensionless rheological criteria. The framework first assesses powder feeding behavior using solid-state indicators, including the flowability index ( f f c ), friction ratio ( F R ), and Froude number ( F r d ), to characterize feed consistency and uniformity. The analysis is subsequently extended to the melt-processing stage in a twin-screw extruder, where extrudability is described using dimensionless numbers that capture viscous, elastic, and inertial effects, namely the Bingham ( B n ), Deborah ( D e ), Weissenberg ( W i ), and Reynolds ( R e ) numbers, along with the die swell ( B ) parameter. These criteria are integrated into a first-order predictive model that enables identification of stable extrusion processing windows. The resulting processability map provides mechanistic insight into the behavior of polymeric systems under defined operating conditions, reducing reliance on empirical trial-and-error approaches. Early identification of processing limitations supports efficient process optimization, leading to reduced material waste, lower energy consumption, and a smaller environmental footprint. While demonstrated using pharmaceutical polymer systems, the proposed methodology is broadly applicable to extrusion-based manufacturing across materials and chemical engineering.

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Cite This Study

Trunov et al. (2026) studied this question.

synapsesocial.com/papers/69a287460a974eb0d3c02cf1https://doi.org/10.1016/j.mtadv.2026.100734
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