Abstract
Biodegradable 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 (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, 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 regressions, 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.
Highlights
Biodegradable materials have recently grown in popularity due to their wide applicability in different practices, including for clinical purposes [1,2,3], drug delivery [4,5], and waste remediation [6,7,8]
This model accurately predicts 431 experimentally measured datasets with an impressive average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R2 ) of 0.999008. This model confirms that the relative crystallinity of pure PLLA and PLLA/PGA composites increases by increasing the crystallization time and reduces by reducing the crystallization temperature
This study used six different machine learning categories to correlate the relative crystallinity of pure PLLA and PLLA/PGA composites to crystallization time, crystallization temperature, and PGA dosage in composites
Summary
Biodegradable materials have recently grown in popularity due to their wide applicability in different practices, including for clinical purposes [1,2,3], drug delivery [4,5], and waste remediation [6,7,8]. Systematic comparison analyses using four statistical indices confirmed that the cascade feedforward neural network provides the most reliable estimations for the relative crystallinity of PLLA/PGA composites. This model accurately predicts 431 experimentally measured datasets with an impressive average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R2 ) of 0.999008. This model confirms that the relative crystallinity of pure PLLA and PLLA/PGA composites increases by increasing the crystallization time and reduces by reducing the crystallization temperature.
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