Image Classification of Clogs in Direct Ink Write Additive Manufacturing

For our direct ink write (DIW) setup, hours of video are collected of fibrous ink that is printed from a translucent nozzle while parts are made. Due to sporadic misalignment of the fibers, clogs may arise that disrupt ink flow, resulting in a failed part. Without on-line monitoring, defective parts can only be identified by operators who witness clogs as or after they occur, requiring operators to continuously monitor the process to eliminate defects. In order to alleviate this, we aim to minimize the effect of clogging via automated process monitoring and rapid detection, thereby reducing labor costs, material loss, and proper identification of defective parts. In this paper, we propose applying a convolutional neural network (CNN) for single frame classification on images gathered from our DIW setup. We report a class average recall of 99.85% across clogged and unclogged classes, and average error of 1.64% when evaluated on new test video sequences, with a processing rate of 27.58 fps. Using class activation mapping, we can visualize image regions the CNN model identifies as salient for each class in performing its discriminative classification task.