Denny John

Cold spray technology has become essential for industries requiring efficient material deposition, yet achieving optimal deposition efficiency (DE) presents challenges due to complex interactions among process parameters. This study developed a two-stage machine learning (ML) framework incorporating Bayesian optimization to address these challenges. In the first stage, a classification model predicted the occurrence of deposition, while the second stage used a regression model to forecast DE values given deposition presence. The approach was validated on Aluminum 6061 data, demonstrating its capability to accurately predict DE and identify optimal process parameters for target efficiencies. Model interpretability was enhanced with SHAP analysis, which identified gas temperature and gas type as primary factors affecting DE. Scenario-based inverse analysis further validated the framework by comparing model-predicted parameters to literature data, revealing high accuracy in replicating real-world conditions. Notably, substituting hydrogen as the gas carrier reduced the required gas temperature and pressure for high DE values, suggesting economic and operational benefits over helium and nitrogen. This study demonstrates the effectiveness of AI-driven solutions in optimizing cold spray processes, contributing to more efficient and practical approaches in material deposition.

In this study, the influence of cold‐spray processing on the integration of 1D boron nitride nanotubes (BNNTs) into aluminum (Al) matrix composite deposits is delved into. Successful deposition of Al‐BNNT composite is achieved by pre‐spray dispersion of BNNTs in Al powder via wet mixing aided by ultrasonication. Pure Al powder deposition at 380 °C is limited by nozzle clogging, restricting the deposit thickness to 0.2 mm, while the presence of BNNTs enables longer spraying due to nozzle cleaning and damping by hard BNNTs, allowing thicker Al‐BNNT deposits of about 2 mm. Microstructural analysis reveals severely deformed Al splats decorated with a uniformly distributed network of BNNTs at the intersplat boundaries, reducing the porosity from 8% to 4% and increasing the splat flattening by 40%. Tribological testing demonstrates resistance of BNNTs to normal force, resulting in a 21% improvement in coefficient of friction and a 65% reduction in wear volume in the Al‐BNNT composite. Additionally, incorporating 3 vol.% BNNTs enhances the neutron radiation shielding by 35%. These improvements in the composites are attributed to reduced porosity and the inherent properties of BNNTs, such as high strength, the ability to produce tribofilm lubrication during dry sliding wear, and their high neutron absorption cross‐sectional area.

Recent advancements have shown great promise in utilizing wire‐fed direct energy deposition (DED) for building aluminum alloy structures. However, utilizing the wire‐fed DED approach for fabricating metal matrix composite structures remains a significant challenge. Herein, a wire‐based additive manufacturing process is used to successfully produce a 1D boron nitride nanotube (BNNT)‐reinforced aluminum composite with high strength. Al‐BNNT electrode is developed in house. The microstructural changes that occur during layer‐by‐layer deposition are investigated. The grain morphology changes from equiaxed grains in the bottom layer to columnar grains in the top layer. BNNTs act as nuclei to promote the formation of equiaxed grains and interfacial compounds (AlN and AlB₂) during solidification. This results in improved strength, with Al‐BNNT composite exhibiting a tensile strength of 47 MPa, 2.3 times higher than its pure Al. Higher strength is attributed to the retention and uniform distribution of BNNT reinforcement in the melt pool, leading to effective load transfer. This study demonstrates the potential of additive manufacturing for producing high‐performance metal matrix composites with novel 1D reinforcements and improved multifunctional properties.

Size and weight have always been key concerns for power supplies. Today’s approaches of manufacturing integrated power electronics building block modules with the assembly of pre-packaged devices and low-volumetric density capacitors and inductors create major fundamental system integration limitations in reaching high power densities and efficiencies to meet the target SWaP-C (size, weight and performance with low cost). Cold-sprayed aluminum capacitors on leadframe metal foils are demonstrated for the first time for applications in 3D power package integration. This additive manufacturing process allows low-temperature processing of pre-patterned aluminum electrodes on metal lead-frames, insulated metal substrates or even heat-spreaders and cold-plates. Process optimization of cold-spray technologies requires careful study of particle velocity, deposition atmosphere and particle morphology design. Initial process design led to ~22X enhancement in surface area compared to planar capacitors. With further process design and optimization, this approach can extend to above 100X enhancement. Cold-sprayed capacitors are projected to eliminate several process integration and reliability issues that are associated with traditional discrete surface-assembled capacitors.