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.