| Summary: | The digitalisation of visual tasks through imaging techniques and Computer Vision has the potential to disrupt the manner in which Advanced Manufacturing processes are deployed. In this study we collaborated with the manufacturing industry to investigate the effective usage of end-to-end convolutional neural networks (CNNs) to enable advanced manufacturing processes by inspecting the seal integrity of sterile barrier packaging in highly regulated products, such as Medical Devices. For this purpose, a novel ‘DS1’ dataset of labelled images representative of production samples was acquired in an industrial-like environment which is an open source for future research work. The core focus of this research is to address the common challenges associated with performing quality inspections in advanced manufacturing environments with the aim of detecting defects with very high impact but very low occurrence rates, by incorporating a set of pre-trained deep learning architectures. The performance of state-of-the-art CNNs is validated on unseen test data when trained in small and imbalanced datasets with low image variation and low pixel complexity. The study indicated that while CNN performance drops when datasets are imbalanced, some architectures are more resilient and capable of successfully classifying defects in small datasets in the order of a few hundred samples wherein as little as 5% of the samples are defective. Furthermore, this study also discusses the marginal impact of training with basic data augmentations and the tendency for models to overfit when trained with manufacturing datasets such as “DS1.”
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