Polymers are being used in a wide range of Additive Manufacturing (AM) applications and have been shown to have tremendous potential for producing complex, individually customized parts. In order to improve part quality, it is essential to identify and monitor the process malfunctions of polymer-based AM. The present work endeavored to develop an alternative method for filament breakage identification in the Fused Deposition Modeling (FDM) AM process. The Acoustic Emission (AE) technique was applied due to the fact that it had the capability of detecting bursting and weak signals, especially from complex background noises. The mechanism of filament breakage was depicted thoroughly. The relationship between the process parameters and critical feed rate was obtained. In addition, the framework of filament breakage detection based on the instantaneous skewness and relative similarity of the AE raw waveform was illustrated. Afterwards, we conducted several filament breakage tests to validate their feasibility and effectiveness. Results revealed that the breakage could be successfully identified. Achievements of the present work could be further used to develop a comprehensive in situ FDM monitoring system with moderate cost.

Fused deposition modeling is a popular technique for three-dimensional prototyping since it is cost-effective, convenient to operate, and environment-friendly. However, the low quality of its printed products jeopardizes its future development. Distortion, also known as warping deformation, which is caused by many factors such as inappropriate process parameters and process drifts, is one of the most common defects in the fused deposition modeling process. Rapid detection of such part distortion during the printing process is beneficial for improving the production efficiency and saving materials. In this article, a real-time part distortion monitoring method based on acoustic emission is presented. Our work is to identify distortion defects and understand the condition of the distortion area through sensing and digital signal processing techniques. In our experiments, both the acoustic emission hits and original signals were acquired during the fused deposition modeling process. Then, the acoustic emission hits were analyzed. Ensemble empirical mode decomposition was utilized to eliminate noise and extract features from the original acoustic emission signal to further analyze the acoustic emission signal in the case of part distortion. Furthermore, the root mean square of the reconstructed signals was calculated, and the prediction results are strongly correlated with the ground truth printing states. This work provides a promising method for the quality diagnosis of printing parts.