High speed in-process defect detection in metal additive manufacturing
Funding: H2020 Marie Skłodowska-Curie Actions - Innovative Training Networks (ITN)
Duration: June 2017 – June 2020
PhD Student: Siwen Chen
Supervisors: Simon Lawes, Richard Leach
Website: https://www.pam2.eu/
This project aims to advance the precision of laser powder bed fusion additive manufacturing through improved in-process surface texture detection with the support of Marie-Curie Innovation Training Network. A number of methods exist for in-situ inspection of AM processes, e.g. thermal imaging, high speed imaging with cameras and co-axial melt pool imaging; however, no system to date has been able to balance the requirements of fast inspection with total inspection. For this reason, a novel vision system will be produced, which allows detection of different types of defects or significant features such as porosity, cracks, etc. via visual identification in a way that is portable and self-calibrating for easy installation in-line in the manufacturing process.
