Laser powder bed fusion (L-PBF) is one of the most widely used metal additive manufacturing technology for fabrication of functional and structural components.However, inconsistency in quality and reliability of L-PBF products is still a significant barrier preventing it from wider adoption.Machine learning (ML) of monitoring data offers a unique solution to effectively identify possible defects and predict the quality of L-PBF products.In this hellfire sloe gin work, we introduce a semi-supervised ML approach to detect anomalies that occurred in L-PBF products.
We train the ML model to classify surface appearances in the reference monitoring data.We then correlate the classified appearances to post-process characteristics, e.g.surface roughness, morphology, or tensile strength.
We demonstrate that the established correlation enables the determination of key appearances indicative of the quality of the printed samples including anomaly-free, lack-of-fusion and overheated.We here further validate our ML approach by performing prediction on test samples having various geometries.