Process parameter selection in laser powder bed fusion (LPBF) controls the as-printed dimensional tolerances, pore formation, surface quality and microstructure of printed metallic structures. Measuring the stochastic mechanical performance for a wide range of process parameters is cumbersome both in time and cost. In this study, we overcome these hurdles by using high-throughput tensile (HTT) testing of over 250 dogbone samples to examine process-driven performance of strut-like small features, ~1 mm2 in austenitic stainless steel (316 L). The output mechanical properties, porosity, surface roughness and dimensional accuracy were mapped across the printable range of laser powers and scan speeds using a continuous wave laser LPBF machine. Tradeoffs between ductility and strength are shown across the process space and their implications are discussed. While volumetric energy density deposited onto a substrate to create a melt-pool can be a useful metric for determining bulk properties, it was not found to directly correlate with output small feature performance.
In the pursuit of improving additively manufactured (AM) component quality and reliability, fine-tuning critical process parameters such as laser power and scan speed is a great first step toward limiting defect formation and optimizing the microstructure. However, the synergistic effects between these process parameters, layer thickness, and feedstock attributes (e.g. powder size distribution) on part characteristics such as microstructure, density, hardness, and surface roughness are not as well-studied. In this work, we investigate 316L stainless steel density cubes built via laser powder bed fusion (L-PBF), emphasizing the significant microstructural changes that occur due to altering the volumetric energy density (VED) via laser power, scan speed, and layer thickness changes, coupled with different starting powder size distributions. This study demonstrates that there is not one ideal process set and powder size distribution for each machine. Instead, there are several combinations or feedstock/process parameter ‘recipes’ to achieve similar goals. This study also establishes that for equivalent VEDs, changing powder size can significantly alter part density, GND density, and hardness. Through proper parameter and feedstock control, part attributes such as density, grain size, texture, dislocation density, hardness, and surface roughness can be customized, thereby creating multiple high-performance regions in the AM process space.
In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.
Stockpile stewardship requires accurate and predictive models relying on the generation of extreme environments which is both incredibly difficult and profoundly necessary. Next generation pulsed power facilities (NGPPF), where these environments are created, may require a paradigm shift in equipment engineering/manufacture to fulfill this need. Therefore, this research aims to investigate the limitations, capabilities and efficacy of leveraging advancements in the field of additive manufacturing (AM) in order to produce novel power flow components for NGPPFs. This work focused on commercial 3D metal AM equipment producing several prototypes addressing prescient needs/shortcomings, and a technique wherein a lightweight polymer core is metalized. Ultimately, commercial 3D metal AM is considered a viable path forward but would require a sizeable investment and does not currently support the scale and complexity necessary for NGPPFs. Moreover, initial results from our composite technique are promising and is considered a realizable path forward given further investigation.
The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. It is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.
Some of the primary barriers to widespread adoption of metal additive manufacturing (AM) are persistent defect formation in built components, high material costs, and lack of consistency in powder feedstock. To generate more reliable, complex-shaped metal parts, it is crucial to understand how feedstock properties change with reuse and how that affects build mechanical performance. Powder particles interacting with the energy source, yet not consolidated into an AM part can undergo a range of dynamic thermal interactions, resulting in variable particle behavior if reused. In this work, we present a systematic study of 316L powder properties from the virgin state through thirty powder reuses in the laser powder bed fusion process. Thirteen powder characteristics and the resulting AM build mechanical properties were investigated for both powder states. Results show greater variability in part ductility for the virgin state. The feedstock exhibited minor changes to size distribution, bulk composition, and hardness with reuse, but significant changes to particle morphology, microstructure, magnetic properties, surface composition, and oxide thickness. Additionally, sieved powder, along with resulting fume/condensate and recoil ejecta (spatter) properties were characterized. Formation mechanisms are proposed. It was discovered that spatter leads to formation of single crystal ferrite through large degrees of supercooling and massive solidification. Ferrite content and consequently magnetic susceptibility of the powder also increases with reuse, suggesting potential for magnetic separation as a refining technique for altered feedstock.