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المؤلفWang, Jing
المؤلفAyari, Mohamed A.
المؤلفKhandakar, Amith
المؤلفChowdhury, Muhammad E. H.
المؤلفUz Zaman, Sm A.
المؤلفRahman, Tawsifur
المؤلفVaferi, Behzad
تاريخ الإتاحة2023-04-17T06:57:43Z
تاريخ النشر2022
اسم المنشورPolymers
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/polym14030527
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41957
الملخصBiodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (poly-glycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, tempera-ture, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector re-gressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R2) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
راعي المشروعThe Science and Technology Plan Projects of Yulin City (2019-85-1).
اللغةen
الناشرMDPI
الموضوعBiodegradable composite
Machine learning methods
Polyglycolide
Polylactic acid
Relative crystallinity
العنوانEstimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies
النوعArticle
رقم العدد3
رقم المجلد14
dc.accessType Open Access


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