Search for: All records

This work concerns the laser powder bed fusion (LPBF) additive manufacturing process. We developed and implemented a physics-based approach for layerwise control of the thermal history of an LPBF part. Controlling the thermal history of an LPBF part during the process is crucial as it influences critical-to-quality characteristics, such as porosity, solidified microstructure, cracking, surface finish, and geometric integrity, among others. Typically, LPBF processing parameters are optimized through exhaustive empirical build-and-test procedures. However, because thermal history varies with geometry, processing parameters seldom transfer between different part shapes. Furthermore, particularly in complex parts, the thermal history can vary significantly between layers leading to both within-part and between-part variation in properties. In this work, we devised an autonomous physics-based controller to maintain the thermal history within a desired window by optimizing the processing parameters layer by layer. This approach is a form of digital feedforward model predictive control. To demonstrate the approach, five thermal history control strategies were tested on four unique part geometries (20 total parts) made from stainless steel 316L alloy. The layerwise control of the thermal history significantly reduced variations in grain size and improved geometric accuracy and surface finish. This work provides a pathway for rapid, shape-agnostic qualification of LPBF part quality through control of the causal thermal history as opposed to expensive and cumbersome trial-and-error parameter optimization. 

Abstract 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.