| 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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article
A soft sensor for prediction of mechanical properties of extruded PLA sheet using an instrumented slit die and machine learning algorithms
Abstract
<p>A soft sensor has been designed to accurately predict the yield stress of extruded Polylactide (PLA) sheet inline, during extrusion processing using an instrumented slit die. A number of experiments over a wide range of processing conditions have been carried out to develop the soft sensor model. The instrumented slit die had a number of embedded sensors monitoring pressure and temperature. The data collected from the slit die sensors was then used to predict the yield stress of the extruded PLA sheet using machine learning algorithms. The yield stress of the extruded sheet, which was measured offline, is compared to the model predictions to check the performance of the model. The soft sensor has the potential to provide real time feedback into the process and become a Quality Assurance (QA) tool which indicates if a product is going out of specification. This model can lead to reduced scrap rates and lower manufacturing costs by reducing machine downtime and making the process more energy efficient. Soft sensors have the potential to be introduced as part of a smart manufacturing process in keeping with the developments of Industry 4.0.</p>