Publications

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.

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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.