Metal laser powder bed fusion: which defects matter?
Funding: Impact Acceleration
Duration: May 2021 – March 2022
Team: Richard Leach, Afaf Remani, Mingyu Liu
One of the key barriers to the industrial adoption of additive manufacturing (AM) technologies is limited trust in the parts that AM processes produce. Defects in parts remain a common concern, and our understanding of the correlations between manufacturing process phenomena, production defects and ultimate part function is poor.
In this project, we intend to conduct experiments in partnership with Renishaw, Imperial College, the Manufacturing Technology Centre and the National Institute of Standards and Technology (USA), aimed at identifying correlations between metal laser powder bed fusion (MLPBF) process phenomena and manufacturing defects. These correlations will then allow us to build a defect prediction model that can be used to conduct abortive, or even corrective, manufacturing operations, as or before defects arise during the manufacturing process.
This project will entail integration of our existing in-process surface measurement technology into MLPBF machines, to gain the data for correlations and demonstrate the measurement technology’s application for integration into commercial AM machines. When combined with the defect detection model developed as part of the project, we intend to create a product (plus product pipeline) and procedures that can deliver step changes in the capability of AM processes, focussed on instilling trust in AM parts through a better understanding of the manufacturing process.