Defect Free Hybrid Additive Manufacturing

Additive Manufacturing allows the creation of highly customised and personalised products, however, this advantage also creates challenges with inspecting and qualifying parts. Compared with traditional mass manufacturing, additive manufacturing does not allow destructive testing and due to the inherent differences between parts, it struggles to accommodate statistical process control methods. Defects from additional, incorrect or missing material placement can result in parts that do not have the required mechanical integrity, performance and reliability. Ultimately this yield loss restricts the advantages of Additive Manufacturing by causing wastage alongside increased production cost and time. This studentship seeks to overcome this issue to enable defect-free production through the use of computer-controlled hybrid 3D Printing which combines additive deposition and subtracting processes. Monitoring the process in real-time will allow the identification of defects when creating a layer. This layer can then be re-worked through a subtractive operation and redeposited with the correct processing conditions. Due to the potential widespread beneficiaries and challenges with producing engineered ceramics, this studentship will focus on this application area.

The objectives of this studentship are:
1) Examination of different imaging techniques to determine the optimal inspection methods per layer.
2) Using image analysis methods to quantify defects.
3) Develop machine learning to improve defect identification for bespoke parts.
4) Generation of toolpath planning operations that allow reworking of a specific layer.
5) Demonstrating the production of defect-free ceramic parts.