CT based process planning and build preparation for AM

The research targets autonomously correlating in-line CT data with AM process parameters which are decided on during build preparation. Many experiments will perform on the MCP Laser Sintering machine, varying the environmental and AM machine settings such as temperature, humidity in the building chamber and the machine settings such as scanning pattern, laser power, and laser scan velocity. To quantify variations in the process, all experiments will be monitored in-line using thermal or optical imaging. The monitoring data will be stored in a database according to an appropriate data model, and the manufactured parts will be CT scanned. The candidate will concentrate on identifying correlations between the input process parameters (machine settings) and the CT data by developing AM simulation techniques. The research will start from the Energy Density (ED) model co-developed by Materialise and KU Leuven. This model calculates local energy densities as a function of laser parameters in the manufacturing process and has already shown correlations between high ED and porosity. A Quality control will be perform using CT scans to interpret the experimental parts, combine the CT results with the monitoring data, and develop predictive models to be used in build preparation.