ArticleScientific reports2025
Machine learning enhanced grey box soft sensor for melt viscosity prediction in polymer extrusion processes.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Review
- Rheology for Wood Plastic Composite Extrusion Part 2: Process Simulation and Experimental Verification.Polymers · 2026Article
- Multi-Material Extrusion-Based 3D Printing of Hybrid Scaffolds for Tissue Engineering Application.Gels (Basel, Switzerland) · 2026Article
- An Artificial Intelligence-Based Melt Flow Rate Prediction Method for Analyzing Polymer Properties.Polymers · 2025Article
- Suppressing mechanical property variability in recycled plastics via bioinspired design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Melt viscosity is regarded as a key quality indicator of the polymer melt in polymer extrusion processes. However, limitations such as disturbances to the melt flow and measurement delays of the existing in-line and side-stream rheometers prevent the monitoring and controlling of this key parameter in real time. Soft sensors can be employed to monitor physical parameters that are difficult to measure using hardware sensing instruments. This study presents a grey-box soft sensing solution to predict the melt viscosity in real time, which combines physics-based knowledge with machine learning. A fine-tuned physics-based mathematical model is used to make melt viscosity predictions, and a deep neural network is employed to compensate for its prediction errors. The proposed soft sensor model reported a normalised root mean square error of 2.2[Formula: see text]10
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Registered trials
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