ABSTRACTNOURELISLAM ZOUAR. ANOMALY DETECTION FOR MANUFACTURING APPLICATIONS USING CONVOLUTIONAL AUTOENCODERS. (Under the direction of DR. MIN SHIN) Anomaly Detection in manufacturing environments is increasingly gaining popularity among companies and researchers. Computer based visual inspection systems are at the core of this interest. In the last few years, computer vision has made immense advancement thanks to deep learning. More specifically, unsupervised learning has proven its strength in this area, mainly due to its ability to deal with all kinds of anomalies and this is due to the philosophy used in this type of machine learning. This research work was motivated by the availability of what is arguably the most comprehensive public anomaly detection dataset, the MVTec dataset [1]. We implemented a pipeline that starts by preprocessing different categories of the MVTec dataset and ends by calculating the prediction accuracies and the inference times. This pipeline includes a convolutional autoencoder. We started by implementing a CAE that follows the description provided by [1]. Then, we have run many experiments using different CAE architectures found in recently published papers. In order to evaluate the performance of all the experimented CAEs, we used a brute force logic to find the best threshold. We used several portions of the calculated accuracies to find the best threshold. These portions were made using different percentages of the calculated accuracies, ranging from 70% to 100%.