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

ABSTRACT: 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 com

SUBMITTER: Munir N 

PROVIDER: S-EPMC10489772 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature