In-process monitoring for Electron Beam Additive Manufacturing using an infrared camera system

Abstract: Additive Manufacturing (AM) is being embraced at a rapid rate, mainly due to its advantages over conventional machining. These include the possibility to create parts with complex geometries, while minimizing waste. The exponential growth of the technology has brought about challenges in quality assurance, which has proved a key barrier to large scale adoption. Developing in-process monitoring techniques for AM is an ongoing challenge, and is still a long way off from the more established techniques developed for conventional machining. Previous research has brought about instances, where the technology has been implemented, with the focus on titanium alloys. This study aims to contribute to the research being carried out within in-process monitoring, and focusses on the Electron Beam Melting (EBM) process. The material being monitored is Inconel 625, to increase the scope of research to higher temperature ranges. The most suitable monitoring technology and vendor for the equipment, is narrowed down through a review of previous literature and market research. Experimental trials to analyze the performance of the monitoring technique with Inconel 625 are carried out. The extracted data is then analyzed using image processing, which gives interesting results with regards to temperature fluctuations over successive layers of the build. The events within the build process for a layer, show interesting deviations in temperature, which are mapped and presented as graphs. The after-rake event, shows a particularly large deviation, which is then attributed to differential heating of the metal powder during the rake phase. This observation is supported by noticing ‘cold-spots’ in extracted images of the build. The results are discussed, and future scope for the study is conveyed. The intention of this study is to provide a base for further research into in-process monitoring for higher temperature ranges and contribute to the development of real-time process monitoring for AM.