Automated Real‐Time Detection and Prediction of Interlayer Imperfections in Additive Manufacturing Processes Using Artificial Intelligence

Abstract

Although fused deposition modeling (FDM) additive manufacturing technologies have advanced in the past decade, interlayer imperfections such as delamination and warping are still dominant when printing complex parts. Herein, a self-monitoring system based on real-time camera images and deep learning algorithms is developed to classify the various extents of delamination in a printed part. In addition, a novel method incorporating strain measurements is established to measure and predict the onset of warping. Results show that the machine-learning model is capable of detecting different levels of delamination conditions, and the strain measurements setup successfully reflects and determines the extent and tendency of warping before it actually occurs in the print job. This multifunctional system can be applied to assess other manufacturing processes to realize autocalibration and prediagnosis of imperfections without human interaction.