In-situ monitoring systems for additive manufacturing are essential to analyse and optimise processes and are as diverse as the 3D metal printing techniques themselves. The objectives in the design, development and implementation of in-situ monitoring in this type of manufacturing should focus on determining the optimal process parameters and on the prediction and identification of the type of defects and their characteristics, allowing to get information on the results and microstructure of the produced piece. The main challenge that arises is the management of the enormous volume of data generated by the construction layer and its storage prior to its analysis.
In the particular case of additive manufacturing with the selective powder bed fusion (SLM technique), MAT4.0-CM project is developing protocols to handle, visualise and analyse the data generated by a monitoring system based on different photodiodes located coaxially with the laser. The developed protocols allow the virtual reconstruction of the piece based on the signals captured by the diodes. The ultimate goal is to provide the system with the necessary intelligence to identify possible manufacturing defects and introduce online correction strategies.
To this end, an ambitious collaboration plan has been established between the GRIAL research group and IMDEA Materials Institute, which aims to correlate the virtual reconstruction of the piece generated by the monitoring system and the defects and microstructure of the actual piece obtained through advanced characterization techniques, such as X-ray computed tomography and scanning electron microscopy (SEM).
Renishaw 500Q Machine Monitoring System, composed of three photodiodes monitoring: the laser power (photodiode 0) and the radiation emitted by the melting well in the visible range (photodiode 1) and infrared (photodiode 2) [Manual de Renishaw]