Qualitative evaluation of 3D printed materials' structure using active infrared thermography and signal analysis based on LSTM neural networks

In this article we present an approach to the quantitative evaluation of the 3D printed sample made of polyethylene terephthalate glycol (PETG) using the active infrared thermography (AIT) method with halogen lamps excitation. For this purpose, numerical and experimental studies were carried out. The numerical model solved with finite element method (FEM) was used first to create a database of signals and further to train neural networks. The networks were trained to detect the heterogeneity of the internal structure of the tested printed sample and to estimate the defects position. After training, the performance of the network was validated with the data obtained in the experiment carried out with the active thermography regime on a real 3D print identical to the modelled one.

Automated summary

This article presents an approach to quantitatively evaluate 3D printed samples made of polyethylene terephthalate glycol (PETG) using active infrared thermography (AIT) with halogen lamps excitation. Numerical and experimental studies were conducted, where a numerical model solved with finite element method (FEM) was used to create a signal database and train neural networks. The trained networks successfully detected the heterogeneity of the internal structure and estimated defect positions in the tested printed samples.