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.

Keywords: biodegradable composite; machine learning methods; polyglycolide; polylactic acid; relative crystallinity.

Figure 1

Histogram of experimental measurements for all of crystallization times (A), crystallization temperatures (B), PGA contents (C), and relative crystallinities (D).

Figure 2

Interdependency of relative crystallinity on time, temperature, and PGA dosage.

Figure 3

Ranking of machine learning methods during model development, model validation, and their combination.

Figure 4

Results of the iterative procedure conducted using the Levenberg–Marquardt to train the CFFNN method.

Figure 5

Predicted versus actual measured values of relative crystallinity.

Figure 6

Histogram presentation of deviation between predicted and actual values of relative crystallinity (average error = 0.398%, standard deviation = 4.72%).

Figure 7

Kernel density estimation for actual measurements and CFFNN predictions.

Figure 8

Applying leverage analysis to detect reliable as well as outlier information.

Figure 9

The effect of PGA dosage and crystallization time on rate of crystallization at 125 °C.

Figure 10

How PGA dosage affects relative crystallization of PLLA/PGA composites at 85 °C.

Figure 11

Isothermal relative crystallinity of the PLLA/PGA composite with 8 wt% of the fiber.