Metal additive manufacturing (AM) has grown in recent years to supplement or even replace traditional fabrication methods. Specifically, the laser powder bed fusion (LPBF) process has been used to manufacture components in support of sustainment issues, where obsolete components are hard to procure. While LPBF can be used to solve these issues, much work is still required to fully understand the metal AM technology to determine its usefulness as a reliable manufacturing process. Due to the complex physical mechanisms involved in the multiscale problem of LPBF, repeatability is often difficult to achieve and consequently makes meeting qualification requirements challenging. The purpose of this work is to provide a review of the physics of metal AM at the melt pool and part scales, thermomechanical simulation methods, as well as the available commercial software used for finite element analysis and computational fluid dynamics modeling. In addition, metal AM process qualification frameworks are briefly discussed in the context of the computational basis established in this work.

In this work, we used in-situ acoustic emission sensors for online monitoring of part quality in 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 is subsequently analyzed to detect onset of incipient flaws, e.g., porosity. However, these existing sensing modalities are unable to penetrate beyond the top surface of the powder bed. Consequently, there is a burgeoning need to detect thermal phenomena in the bulk volume of the part buried under the powder, as they are linked to such flaws as support failures, poor surface finish, microstructure heterogeneity, among others. Herein, four passive acoustic emission sensors were installed in the build plate of an EOS M290 LPBF system. Acoustic emission data was acquired during processing of stainless steel 316L samples under differing parameter settings and part design variations. The acoustic emission 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 acoustic emission signatures were statistically correlated (R² > 85%) to multi-scale 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, non-destructive evaluation of multi-scale part quality aspects using acoustic emission sensors.