| Abstract Scope |
In powder bed additive manufacturing, many complex process outcomes, such as part density, mechanical strength, and surface finish can be strongly correlated to the dimensions of individual melt pools. For a given set of input parameters, we can measure these dimensions by conducting single bead experiments and imaging cross-sections. However, complex laser-material interactions, degrading machines, and heat buildup lead to significant variability in melt pool sizes when fabricating real components. Thus, in situ characterization is an avenue toward localized defect detection and uncertainty quantification. High-speed video cameras can capture these dimensions accurately, but prohibitive data requirements and a limited field-of-view present scalability challenges for such a system. In this preliminary work, we use high-speed videos as the “ground truth” dataset for a regression model that predicts melt pool dimensions from registered acoustic signals, which can be used to monitor the entire build. Both LSTM’s and CNN’s show promising early results. |