Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

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.

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“…The early domain contains the valid measurements, while the latter includes the suspicious ones. The mathematical form of the SR is expressed by the following equation 73 $$$SR\,=\,{\left(\frac{RE}{SD}\right)}_{k}\,k=1,2,\ldots ,N.$$$…”

“…Since the biomass heat capacity is a function of four independent variables, and there are 700 experimental data, the CL = 0.02143 (vertical green line in Figure 8). 73 $$$CL=3\times (IV+1)/N.$$$…”

“…72 The highest and smallest factors, that is, +1 and −1 are associated with the strongest direct and indirect relevancy between dependent and independent variables, respectively. 73 The zero or close value to zero implies no relationship between a pair of variables. It is possible to calculate the Pearson's coefficient using Equation ( 23).…”

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Figure 9 Differentiating between valid and suspect data collected from the literature.

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