A deep learning approach to automate human-visual inspections in the medical-devices manufacturing industry

This thesis investigates how deep learning can be used to automate visual inspection in medical device manufacturing, a domain where extremely high accuracy is required and manual inspection remains prevalent despite its susceptibility to fatigue, variability, and error. Traditional rule-based machine vision performs well for dimensional checks but is poorly suited to attribute inspections that depend on subtle, context-dependent visual cues. To address this gap, the research focuses on methods that can be trained predominantly on defect-free samples, reflecting the rarity and cost of obtaining representative defective parts in highly regulated environments. The work makes four main contributions. First, it introduces and publicly releases a dataset of 1,200 annotated images of sterile barrier packaging with realistic seal defects, enabling reproducible benchmarking. Second, it reports a large-scale evaluation of 27 convolutional neural network architectures across multiple small and imbalanced dataset regimes, and proposes the median distance to the boundary as a robustness metric for detecting overfitting when test accuracies approach 100%. Third, it develops two attention-guided autoencoder architectures for deep anomaly detection: a structural-similarity variant tailored for lightweight, real-time inspection on production lines, and a feature-distance variant designed for lifecycle monitoring in supervisory systems; both are validated on an external dataset and shown to be competitive with state-of-the-art anomaly-detection approaches. Finally, the thesis provides a technical and regulatory analysis of the EU Artificial Intelligence Act and related medical-device frameworks, highlighting challenges in data governance, explainability, validation, and post-deployment monitoring. Overall, the findings demonstrate that deep learning models trained mainly on defect-free data can deliver scalable, high-performing and more easily qualifiable automated inspection systems, while also identifying open issues that motivate future research at the intersection of advanced manufacturing and AI regulation.

Publisher

University of Galway

Rights

CC BY-NC-ND

cbn

Collections

University of Galway Theses (PhD Theses)