Roberts, Matthew and Kennedy, Andrew (2024) Process parameter optimisation and thermal characterisation of coaxial multi-laser metal wire additive manufacturing with 316L stainless steel. PhD thesis, Lancaster University.
![[thumbnail of 2024RobertsPhD.pdf]](https://eprints.lancs.ac.uk/style/images/fileicons/text.png)
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.
Download (0B)
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.
Download (0B)
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.
Download (0B)
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.
Download (0B)
2024RobertsPhD.pdf - Published Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.
Download (21MB)
Abstract
Laser Metal Wire Additive Manufacturing systems (LWAM), with coaxially mounted lasers, are emerging onto the additive manufacturing market. They use relatively fine wire feedstock and have high efficiency in terms of energy and deposition speeds compared to other metal wire technologies, making a credible technology to fill the gap between slower, precise metal powder technologies and faster, lower-fidelity wire-arc technologies. Limited research is available for this technology. Commercial slicing software, based on more forgiving polymers, is also limited in its ability to accommodate more complex dynamics of metal wire additive manufacturing. This thesis uses the Meltio M450 to investigate the relationship between laser power, extrusion rate and head speed and the resulting track geometry and layer quality. The research identifies the process parameters required to produce high-quality tracks repeatedly. New insights are developed into the use of machine learning to predict quality and geometry of beads, showing how these tools reduce the need for experimental trials. Using Machine Learning to address uncertainty in the prediction of track widths adds new insights to the application of machine learning in the field of metal additive manufacturing. A single-layer model is developed to predict layer height, adding to the existing body of work by quantifying the effect of the number of tracks and track separation on the resulting layer height. Experiments to measure interlayer temperature and bulk heating during the deposition process are used with modelling to show a new method for using the base substrate temperature to infer interlayer temperatures when interlayer pauses of sixty seconds or longer are used. This thesis has developed novel methods for process optimisation, geometry prediction and interlayer temperature control, which address some of the key research gaps for coaxial LWAM and add to the body of knowledge in this area to support improved slicing tools.