Abstract
AbstractThis contribution aims at developing scaling algorithms for planetary roller extruders (PREs). Laboratory‐ and production‐scale experiments were carried out, using thermoplastic polymers according to a statistical design of experiments (DOE). By comparing plant size, spindle configuration, operating parameters, and material properties, their influence on pressure build‐up capacity, process temperatures, and residence time distribution is analyzed. All data generated are used to train MATLAB‐based machine learning models. First indications hint at Gaussian processes and artificial neural networks, predicting operating parameters with high accuracy.
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