This paper presents a complete concept and validation scheme for potential inter-layer flaw detection from in-situ process monitoring for powder bed fusion additive manufacturing (PBFAM) using supervised machine learning. Specifically, the presented work establishes a meaningful statistical correlation between (i) the multi-modal sensor footprint acquired during the build process, and (ii) the existence of flaws as indicated by post-build X-ray Computed Tomography (CT) scans. Multiple sensor modalities, such as layerwise imagery (both pre and post laser scan), acoustic and multi-spectral emissions, and information derived from the scan vector trajectories, contribute to the process footprint. Data registration techniques to properly merge spatial and temporal information are presented in detail. As a proof-of-concept, a neural network is used to fuse all available modalities, and discriminate flaws from nominal build conditions using only in-situ data. Experimental validation was carried out using a PBFAM sensor testbed available at PSU/ARL. Using four-fold cross-validation on a voxel-by-voxel basis, the null hypothesis, i.e. absence of a defect, was rejected at a rate corresponding to 98.5% accuracy for binary classification. Additionally, a sensitivity study was conducted to assess the information content contributed by the individual sensor modalities. Information content was assessed by evaluating the resulting correlation as classification performance when using only a single modality or a subset of modalities. Although optical imagery contains the highest amount of information for flaw detection, additional information content observed in other modalities significantly improved classification performance.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Materials Science(all)
- Engineering (miscellaneous)
- Industrial and Manufacturing Engineering