Classifying Degraded Three-Dimensionally Printed Polylactic Acid Specimens Using Artificial Neural Networks based on Fourier Transform Infrared Spectroscopy

Abstract

Fused filament fabrication (FFF) is commonly employed in multiple domains to realize inexpensive and flexible material extrusion systems with thermoplastic materials. Among the several types of thermoplastic materials, polylactic acid (PLA), an environment-friendly bio-plastic, is commonly used for FFF for the sake of the safety of the manufacturing process. However, thermal degradation of three-dimensionally (3D)-printed PLA products is inevitable, and it is one of the failure mechanisms of thermoplastic products. The present study focuses on the thermal degradation of 3D-printed PLA specimens. A classification methodology using artificial neural networks (ANNs) based on Fourier transform infrared (FTIR) and was developed. Under the given experimental conditions, the ANN model could classify four levels of thermal degradation. Among the FTIR spectra recorded from 650 cm−1 to 4000 cm−1, the ANN model could suggest the best wavenumber ranges for classification.

Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data

“…However, FTIR spectra is hard to be interpreted so that an alternate methodology is necessary. Zhang studied the thermal degradation of 3D printed polylactic acid (PLA) and Acrylonitrile-butadiene-styrene (ABS) materials by using FTIR and analyzed the resulting spectra by using artificial neural networks (ANNs) [20,21]. Choi et al reported that the mechanical properties of one 3D printed PLA material changed with the degree of thermal degradation [22].…”

“…The decision jungle algorithm is a recent version of the decision forest algorithm, while the logistic regression algorithm is a popular statistical method to predict the probability of an outcome for classification tasks. The neural network algorithm is known as artificial neural networks and which described briefly in a previous study [20]. Each of the four algorithms used here has hyperparameters, as shown in Table 4, which require tuning for higher accuracy in the classification.…”