Jonal Laboratories Inc. is a custom manufacturing company that primarily produces elastomeric components for the aerospace industry. Currently, Jonal uses a team of quality inspectors to manually examine each part for defects including chips, contaminations, flashes, and more. However, this is a tedious, non-value-added process subject to human error. Introducing automation would remove pressure from the inspectors, provide them more time to focus on value-added tasks, and increase the speed and efficiency of the defect detection process. Thus, this project entails creating such an automated system to classify a part image as either defective or not defective. To achieve this goal, a convolutional neural network was trained using images of both defective and non-defective parts collected onsite at Jonal in order to perform the necessary binary classification. Once an acceptable accuracy is achieved, a method to integrate the trained model into the currently inspection process, thus creating the desired automated system, is generated.