Abstract
Machine learning methods were applied to investigate changes in material properties during degradation, focusing on bio-based PLA and a PLA/native potato starch compound (50 wt%). Sixteen aging conditions involving various temperatures and humidity levels, and aging durations were examined. Characterization of aged samples involved tensile tests, FTIR analysis, weight or density measurements, and injection molding data. These data served as inputs to develop and compare predictive models of mechanical properties like Young’s modulus and elongation at break. Linear and polynomial regression, as well as multilayer perceptron (MLP) models were employed to evaluate their prediction accuracy. The best model accuracy (RMSE = 0.33) was achieved by segregating the dataset by material type and employing linear regression. Notably, employing two independent variables such as temperature and humidity led to high model quality (RMSE = 0.35). Effect diagrams revealed strong alignment between actual and modeled data, highlighting the comparative strengths of each modeling approach.
Acknowledgments
The authors would like to thank the companies Emsland Stärke, TechnoCompound GmbH and TotalEnergies Corbion for providing the materials for this investigation.
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Research ethics: No ethical approval was required for the investigations carried out, as no experiments were carried out with human tissue.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Machine learning was used to generate the results and analyze the data.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This research was funded by the Federal Ministry of Food and Agriculture (BMEL) and the Fachagentur Nachwachsende Rohstoffe e.V. (FNR) [Grant No. 2220NR089E].
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Review Article
- Epoxy vitrimers: from essence to utility
- Research Articles
- Tamarind seed powder as filler in polypropylene and its impact on the mechanical and biodegradability of the composites
- Development and characterization of glass fiber composites impregnated with limestone powder and bagasse fiber
- Hyaluronic acid/κ-carrageenan films for mupirocin-controlled delivery
- Temperature field study and numerical computation of carbon fiber epoxy composite materials under unilateral thermal radiation
- 2D dendritic thermal growth pulsations: diffusion field associated with the transport of heat for application in organic-based systems
- Influence of different surface textures on wettability of UHMWPE and POM- an experimental study
- Use of machine learning methods for modelling mechanical parameters of PLA and PLA/native potato starch compound using aging data
- Influence of the viscosity of polymer melts on the coextrusion process based on wall slip conditions
Articles in the same Issue
- Frontmatter
- Review Article
- Epoxy vitrimers: from essence to utility
- Research Articles
- Tamarind seed powder as filler in polypropylene and its impact on the mechanical and biodegradability of the composites
- Development and characterization of glass fiber composites impregnated with limestone powder and bagasse fiber
- Hyaluronic acid/κ-carrageenan films for mupirocin-controlled delivery
- Temperature field study and numerical computation of carbon fiber epoxy composite materials under unilateral thermal radiation
- 2D dendritic thermal growth pulsations: diffusion field associated with the transport of heat for application in organic-based systems
- Influence of different surface textures on wettability of UHMWPE and POM- an experimental study
- Use of machine learning methods for modelling mechanical parameters of PLA and PLA/native potato starch compound using aging data
- Influence of the viscosity of polymer melts on the coextrusion process based on wall slip conditions