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.
Energy simulation of the fused deposition modeling process using machine learning approach
