Prediction of Microstructure and Mechanical Properties of Ultrasonically Treated PLA Materials Using Convolutional Neural Networks

Abstract

Fused deposition modeling (FDM) 3D printing with polymeric materials has the advantage of producing products of various shapes; however, it has limitations in the mechanical properties of the output. Therefore, post-processing processes must be applied to the output, and research must be conducted to improve the mechanical properties. The first objective of this study was to compare the mechanical properties of FDM 3D printed parts made of polylactic acid (PLA) with and without ultrasonic post-processing. The mechanical properties of the PLA prints were compared using tensile tests before and after ultrasonic treatment, and the mechanical properties of the PLA prints were compared with ultrasonic treatment at the glass transition temperature. Consequently, the tensile strength of the ultrasonically treated PLA output improved by approximately 38.8%. The second objective of this study was to apply a machine learning algorithm based on convolutional neural networks to extract the image pattern observed in the output before and after ultrasonic treatment and to predict the mechanical properties. A machine learning algorithm, consisting of feature extraction and classification, was applied to develop a pretrained model to detect whether the output was sonicated and to predict the mechanical properties accordingly. Furthermore, the PLA output, whose reliability was verified by the pretrained model, was expected to be used as a structural material element in various industrial fields.