| description abstract | In this work, we used in situ acoustic emission (AE) sensors for online monitoring of part quality in a laser powder bed fusion (LPBF) additive manufacturing process. Currently, sensors such as thermo-optical imaging cameras and photodiodes are used to observe the laser–material interactions on the top surface of the powder bed. Data from these sensors are subsequently analyzed to detect the onset of incipient flaws, e.g., porosity. However, a drawback of these existing sensing modalities is that they are unable to penetrate beyond the top surface of the powder bed. It is important to detect process phenomena within the bulk volume of the part buried under the powder, because these subsurface phenomena are linked to such flaws as support failures, poor surface finish, and microstructure heterogeneity, among others. To address this existing gap, four passive AE sensors were installed in the build plate of an EOS M290 LPBF system. Acoustic emission data were acquired during the processing of stainless steel 316L samples under differing parameter settings and part design variations. The AE signals were decomposed using wavelet transforms. Subsequently, to localize the origin of AE signals to specific part features, they were spatially synchronized with infrared thermal images. The resulting spatially localized AE signatures were statistically correlated (R2 > 85%) to multiscale aspects of part quality, such as thermal-induced part failures, surface roughness, and solidified microstructure (primary dendritic arm spacing). This work takes a critical step toward in situ, nondestructive evaluation of multiscale part quality aspects using AE sensors. | |
