| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Mcafee, Marion
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2024Embedding a surface acoustic wave sensor and venting into a metal additively manufactured injection mould tool for targeted temperature monitoringcitations
- 2024Sensorised metal AM injection mould tools for in-process monitoring of cooling performance with conventional and conformal cooling channel designscitations
- 2024Investigation of the effect of Graphene oxide concentration on the final properties of Aspirin loaded PLA filaments for drug delivery systems
- 2023Enhancement of biodegradability of polylactides by γ-ray irradiation
- 2023Interpretable machine learning methods for monitoring polymer degradation in extrusion of polylactic acidcitations
- 2021Comparison of data summarization and feature selection techniques for in-process spectral datacitations
- 2018A soft sensor for prediction of mechanical properties of extruded PLA sheet using an instrumented slit die and machine learning algorithmscitations
- 2014The application of computational chemistry and chemometrics to developing a method for online monitoring of polymer degradation in the manufacture of bioresorbable medical implants
- 2012Water spray cooling of polymerscitations
- 2012Dynamic grey-box modeling for online monitoring of extrusion viscositycitations
- 2011The inferential monitoring of screw load torque to predict process fluctuations in polymer extrusioncitations
- 2011The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusioncitations
- 2011Internal cooling in rotational molding-A reviewcitations
- 2011Quantitative characterization of clay dispersion in polymer-clay nanocompositescitations
- 2010Quantitative characterization of clay dispersion in polypropylene-clay nanocomposites by combined transmission electron microscopy and optical microscopy
- 2010Quantitative characterization of clay dispersion in polypropylene-clay nanocomposites by combined transmission electron microscopy and optical microscopycitations
- 2010Structure-property relationships in biaxially deformed polypropylene nanocompositescitations
- 2007Enhancing process insight in polymer extrusion by grey box modellingcitations
- 2007A novel approach to dynamic modelling of polymer extrusion for improved process controlcitations
- 2007A Soft Sensor for viscosity control of polymer extrusioncitations
- 2006Energy efficient extrusion
- 2003Design of a soft sensor for polymer extrusion
Places of action
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document
A Soft Sensor for viscosity control of polymer extrusion
Abstract
Viscosity represents a key indicator of product quality in polymer extrusion but has traditionally been difficult to measure in-process in real-time. An innovative, yet simple, solution to this problem is proposed by a Prediction-Feedback observer mechanism. A `Prediction' model based on the operating conditions generates an open-loop estimate of the melt viscosity; this estimate is used as an input to a second, `Feedback' model to predict the pressure of the system. The pressure value is compared to the actual measured melt pressure and the error used to correct the viscosity estimate. The Prediction model captures the relationship between the operating conditions and the resulting melt viscosity and as such describes the specific material behavior. The Feedback model on the other hand describes the fundamental physical relationship between viscosity and extruder pressure and is a function of the machine geometry. The resulting system yields viscosity estimates within 1% error, shows excellent disturbance rejection properties and can be directly applied to model-based control. This is of major significance to achieving higher quality and reducing waste and set-up times in the polymer extrusion industry.