In-process volumetric sensing of defects in multiple parts during powder bed fusion using ultrasound.

This letter reports on the integration of eight ultrasonic transducers into a build substrate for individual in-process monitoring of eight parts fabricated using powder bed fusion additive manufacturing. Ultrasound is shown to be able to sense poor fusion of parts to the substrate and also sensitivity to porosity. This technique demonstrates the utility of ultrasound as one of a few techniques able to interrogate the volume of additively manufactured parts during the process. Additionally, the ability to measure several parts during a single build can be used for efficient process parameter development studies, as the ultrasonic measurements can offer rapid information about part quality and integrity.

Six-Sigma Quality Management of Additive Manufacturing.

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

Model-Based Feedforward Control of Part Height in Directed Energy Deposition.

Control of the geometric accuracy of a metal deposit is critical in the repair and fabrication of complex components through Directed Energy Deposition (DED). This paper developed and experimentally evaluated a model-based feedforward control of laser power with the objective of achieving the targeted part height in DED. Specifically, based on the dynamic model of melt-pool geometry derived from our prior work, a nonlinear inverse-dynamics controller was derived in a hatch-by-hatch, layer-by-layer manner to modulate the laser power such that the melt-pool height was regulated during the simulated build process. Then, the laser power trajectory from the simulated closed-loop control under the nonlinear inverse-dynamics controller was implemented as a feedforward control in an Optomec Laser-Engineered Net Shape (LENS) MR-7 system. This paper considered the deposition of L-shaped structures of Ti-6AL-4V as a case study to illustrate the proposed model-based controller. Experimental validation showed that by applying the proposed model-based feed-forward control for laser power, the resulting build had 24-42% reduction in the average build height error with respect to the target build height compared to applying a constant laser power through the entire build or applying a hatch-dependent laser power strategy, for which the laser power values were obtained from experimental trial and error.

Formation processes for large ejecta and interactions with melt pool formation in powder bed fusion additive manufacturing.

Ejecta with a size much larger than the mean particle size of feedstock powder have been observed in powder bed fusion additive manufacturing, both during post-process sieving and embedded within built components. However, their origin has not been adequately explained. Here, we test a hypothesis on the origin of large (much larger than the mass-median-diameter of feedstock powder) ejecta-that, in part, they result from stochastic, inelastic collisions of ejecta and coalescence of partially-sintered agglomerates. The hypothesis is tested using direct observation of ejecta behavior, via high-speed imaging, to identify interactions between ejecta and consequences on melt pool formation. We show that stochastic collisions occur both between particles which are nearly-simultaneously expelled from the laser interaction zone and between particles ejected from distant locations. Ejecta are also shown to perturb melt pool geometry, which is argued to be a potential cause of lack-of-fusion flaws.