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Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used...

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Autores principales: Munir, Nimra, McMorrow, Ross, Mulrennan, Konrad, Whitaker, Darren, McLoone, Seán, Kellomäki, Minna, Talvitie, Elina, Lyyra, Inari, McAfee, Marion
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10489772/
https://www.ncbi.nlm.nih.gov/pubmed/37688192
http://dx.doi.org/10.3390/polym15173566
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author Munir, Nimra
McMorrow, Ross
Mulrennan, Konrad
Whitaker, Darren
McLoone, Seán
Kellomäki, Minna
Talvitie, Elina
Lyyra, Inari
McAfee, Marion
author_facet Munir, Nimra
McMorrow, Ross
Mulrennan, Konrad
Whitaker, Darren
McLoone, Seán
Kellomäki, Minna
Talvitie, Elina
Lyyra, Inari
McAfee, Marion
author_sort Munir, Nimra
collection PubMed
description This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially ‘black-box’ in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings.
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spelling pubmed-104897722023-09-09 Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid Munir, Nimra McMorrow, Ross Mulrennan, Konrad Whitaker, Darren McLoone, Seán Kellomäki, Minna Talvitie, Elina Lyyra, Inari McAfee, Marion Polymers (Basel) Article This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially ‘black-box’ in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings. MDPI 2023-08-28 /pmc/articles/PMC10489772/ /pubmed/37688192 http://dx.doi.org/10.3390/polym15173566 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Munir, Nimra
McMorrow, Ross
Mulrennan, Konrad
Whitaker, Darren
McLoone, Seán
Kellomäki, Minna
Talvitie, Elina
Lyyra, Inari
McAfee, Marion
Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
title Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
title_full Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
title_fullStr Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
title_full_unstemmed Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
title_short Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
title_sort interpretable machine learning methods for monitoring polymer degradation in extrusion of polylactic acid
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10489772/
https://www.ncbi.nlm.nih.gov/pubmed/37688192
http://dx.doi.org/10.3390/polym15173566
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