The topic of this project is the visualization and analysis of two-dimensional (2D) and three-dimensional (3D) data that were acquired during the additive manufacturing process at the Federal Institute for Materials Research and Testing (BAM) . The objective is to identify and develop methods that are well suited for automatic process monitoring and quality control of additive manufacturing.

Project description

Due to the fast proliferation of additive manufacturing, especially in industry, there is an increasing demand of reliable methods for detecting defects in the material created during the production process. The Federal Institute for Materials Research and Testing (BAM) runs a project in which different methods are tested that allow one to conduct a non-destructive in-situ evaluation of the quality of manufactured components.

In particular, the detection of pores, cracks and other defects is important. Methods that are currently being tested are thermography, optical emission spectroscopy and eddy-current testing. It is expected that those methods are able to detect defects in the material due to changed observables, for example, the temperature profile in thermography.

Computer tomography scans of the constructed objects will be made to find out which defects might occur during the manufacturing process and how they show up in the monitoring methods. This way we hope to be able to identify the best-suited methods for process monitoring. For this purpose, algorithms need to be developed for the fusion, visualization and analysis of the respective 2d and 3d data.

Fig. 1: Change of temperature at a single point during the manufacturing process. The time point 0 is where the laser hits the observed spot on the material.