3D printing has become a substantial part of the engineering toolbox. The technology offers vast opportunities across fields because it can make almost any geometry out of almost any material. However, many applications remain at the research-stage because printers are vulnerable to errors. Currently, expert operators must recognise errors and repeat the print with manually updated settings. Existing error detection solutions often only work for a single machine, material, geometry, and setup, whilst primarily focussing on the detection of a single error modality. Furthermore, little work exists on how to correct these errors once manifested.

This thesis presents a number of deep learning enabled computer vision based approaches to detect and correct a wide range of errors both in real time and for the subsequent print. The developed models outperform existing approaches at error detection in performance and generality, whilst further advancing the state-of-the-art by autonomously controlling and updating printing parameters to correct errors. However, the training of these modern deep learning architectures requires significant quantities of high quality training data.

Therefore, a unique data collection and labelling engine is developed to generate process monitoring data from a fleet of 3D printers. With this tool, three large datasets are created and used to trained deep learning models for different applications: (i) generalisable real-time error detection and multi-parameter correction; (ii) real-time quantitative prediction of flow rate and few-shot correction; and (iii) recognition and correction of long-term thermal deformation. These models are then used for parameter discovery to autonomously learn how to print unseen and novel materials. Additionally, ideas from explainable AI are introduced to create visualisations which shed light on how the deep learning models make their decisions.