Additive manufacturing has established itself to be advantageous beyond small-scale prototyping, now supporting full-scale production of components for a variety of applications. Despite its integration across industries, marine renewable energy technology is one largely untapped application with potential to bolster clean energy production on the global scale. Wave energy converters (WEC) are one specific facet within this realm that could benefit from AM. As such, wire arc additive manufacturing (WAAM) has been identified as a practical method to produce larger scale marine energy components by leveraging cost-effective and readily available A36 steel feedstock material. The flexibility associated with WAAM can benefit production of WEC by producing more complex structural geometries that are challenging to produce traditionally. Additionally, for large components where fine details are less critical, the high deposition rate of WAAM in comparison to traditional wrought techniques could reduce build times by an order of magnitude. In this context of building and supporting WEC, which experience harsh marine environments, an understanding of performance under large loads and corrosive environments must be understood. Hence, WAAM and wrought A36 steel tensile samples were manufactured, and mechanical properties compared under both dry and corroded conditions. The unique microstructure created via the WAAM process was found to directly correlate to the increased ultimate tensile and yield strength compared to the wrought condition. Static corrosion testing in a simulated saltwater environment in parallel with electrochemical testing highlighted an outperformance of corroded WAAM A36 steel than wrought, despite having a slighter higher corrosion rate. Ultimately, this study shows how marine energy systems may benefit from additive manufacturing components and provides a foundation for future applications of WAAM A36 steel.
The development of additively-manufactured (AM) 316L stainless steel (SS) using laser powder bed fusion (LPBF) has enabled near net shape components from a corrosion-resistant structural material. In this article, we present a multiscale study on the effects of processing parameters on the corrosion behavior of as-printed surfaces of AM 316L SS formed via LPBF. Laser power and scan speed of the LPBF process were varied across the instrument range known to produce parts with >99 % density, and the macroscale corrosion trends were interpreted via microscale and nanoscale measurements of porosity, roughness, microstructure, and chemistry. Porosity and roughness data showed that porosity φ decreased as volumetric energy density Ev increased due to a shift in the pore formation mechanism and that roughness Sq was due to melt track morphology and partially fused powder features. Cross-sectional and plan-view maps of chemistry and work function ϕs revealed an amorphous Mn-silicate phase enriched with Cr and Al that varied in both thickness and density depending on Ev. Finally, the macroscale potentiodynamic polarization experiments under full immersion in quiescent 0.6 M NaCl showed significant differences in breakdown potential Eb and metastable pitting. In general, samples with smaller φ and Sq values and larger ϕs values and homogeneity in the Mn-silicate exhibited larger Eb. The porosity and roughness effects stemmed from an increase to the overall number of initiation sites for pitting, and the oxide phase contributed to passive film breakdown by acting as a crevice former or creating a galvanic couple with the SS.
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.
Additively manufactured metamaterials such as lattices offer unique physical properties such as high specific strengths and stiffnesses. However, additively manufactured parts, including lattices, exhibit a higher variability in their mechanical properties than wrought materials, placing more stringent demands on inspection, part quality verification, and product qualification. Previous research on anomaly detection has primarily focused on using in-situ monitoring of the additive manufacturing process or post-process (ex-situ) x-ray computed tomography. In this work, we show that convolutional neural networks (CNN), a machine learning algorithm, can directly predict the energy required to compressively deform gyroid and octet truss metamaterials using only optical images. Using the tiled nature of engineered lattices, the relatively small data set (43 to 48 lattices) can be augmented by systematically subdividing the original image into many smaller sub-images. During testing of the CNN, the prediction from these sub-images can be combined using an ensemble-like technique to predict the deformation work of the entire lattice. This approach provides a fast and inexpensive screening tool for predicting properties of 3D printed lattices. Importantly, this artificial intelligence strategy goes beyond ‘inspection’, since it accurately estimates product performance metrics, not just the existence of defects.