Image analytics and machine learning for in-situ defects detection in Additive Manufacturing
Davide Cannizzaro, Antonio Giuseppe Varrella, Stefano Paradiso, Roberta Sampieri, Enrico Macii, Edoardo Patti, Santa Di Cataldo
Abstract
In the context of Industry 4.0, metal Additive Manufacturing (AM) is considered a promising technology for medical, aerospace and automotive fields. However, the lack of assurance of the quality of the printed parts can be an obstacle for a larger diffusion in industry. To this date, AM is most of the times a trial-and-error process, where the faulty artefacts are detected only after the end of part production. This impacts on the processing time and overall costs of the process. A possible solution to this problem is the in-situ monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build. In this paper, we describe a system for in-situ defects monitoring and detection for metal Powder Bed Fusion (PBF), that leverages an off-axis camera mounted on top of the machine. A set of fully automated algorithms based on Computer Vision and Machine Learning allow the timely detection of a number of powder bed defects and the monitoring of the object's profile for the entire duration of the build.