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Copper (Cu) and tungsten (W) possess exceptional electrical and thermal conductivity properties, making them suitable candidates for applications such as interconnects and thermal conductivity enhancements. Solution-based additive manufacturing (SBAM) offers unique advantages, including patterning capabilities, cost-effectiveness, and scalability among the various methods for manufacturing Cu and W-based films and structures. In particular, SBAM material jetting techniques, such as inkjet printing (IJP), direct ink writing (DIW), and aerosol jet printing (AJP), present a promising approach for design freedom, low material wastes, and versatility as either stand-alone printers or integrated with powder bed-based metal additive manufacturing (MAM). Thus, this review summarizes recent advancements in solution-processed Cu and W, focusing on IJP, DIW, and AJP techniques. The discussion encompasses general aspects, current status, challenges, and recent research highlights. Furthermore, this paper addresses integrating material jetting techniques with powder bed-based MAM to fabricate functional alloys and multi-material structures. Finally, the factors influencing large-scale fabrication and potential prospects in this area are explored.
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Fabrication of Cu-CNT Composite and Cu Using Laser Powder Bed Fusion Additive Manufacturing
Additive manufacturing (AM) as a disruptive technique has offered great potential to design and fabricate many metallic components for aerospace, medical, nuclear, and energy applications where parts have complex geometry. However, a limited number of materials suitable for the AM process is one of the shortcomings of this technique, in particular laser AM of copper (Cu) is challenging due to its high thermal conductivity and optical reflectivity, which requires higher heat input to melt powders. Fabrication of composites using AM is also very challenging and not easily achievable using the current powder bed technologies. Here, the feasibility to fabricate pure copper and copper-carbon nanotube (Cu-CNT) composites was investigated using laser powder bed fusion additive manufacturing (LPBF-AM), and 10 × 10 × 10 mm3 cubes of Cu and Cu-CNTs were made by applying a Design of Experiment (DoE) varying three parameters: laser power, laser speed, and hatch spacing at three levels. For both Cu and Cu-CNT samples, relative density above 90% and 80% were achieved, respectively. Density measurement was carried out three times for each sample, and the error was found to be less than 0.1%. Roughness measurement was performed on a 5 mm length of the sample to obtain statistically significant results. As-built Cu showed average surface roughness (Ra) below 20 µm; however, the surface of AM Cu-CNT samples showed roughness values as large as 1 mm. Due to its porous structure, the as-built Cu showed thermal conductivity of ~108 W/m·K and electrical conductivity of ~20% IACS (International Annealed Copper Standard) at room temperature, ~70% and ~80% lower than those of conventionally fabricated bulk Cu. Thermal conductivity and electrical conductivity were ~85 W/m·K and ~10% IACS for as-built Cu-CNT composites at room temperature. As-built Cu-CNTs showed higher thermal conductivity as compared to as-built Cu at a temperature range from 373 K to 873 K. Because of their large surface area, light weight, and large energy absorbing behavior, porous Cu and Cu-CNT materials can be used in electrodes, catalysts and their carriers, capacitors, heat exchangers, and heat and impact absorption.
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- Award ID(s):
- 2152254
- PAR ID:
- 10412920
- Date Published:
- Journal Name:
- Powders
- Volume:
- 1
- Issue:
- 4
- ISSN:
- 2674-0516
- Page Range / eLocation ID:
- 207 to 220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Powder bed fusion of copper has been extensively investigated using both laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) additive manufacturing technologies. Each technique offers unique benefits as well as specific limitations. Near-infrared (NIR) laser-based LPBF is widely accessible; however, the high reflectivity of copper limits energy absorption, thereby resulting in a narrow processing window. Although optimized parameters can yield relative densities above 97%, issues such as keyhole porosity, incomplete melting, and anisotropy remain concerns. Green lasers, with higher absorptivity in copper, offer broader process windows and enable more consistent fabrication of high-density parts with superior electrical conductivity, often reaching or exceeding 99% relative density and 100% International Annealed Copper Standard (IACS). Mechanical properties, including tensile and yield strength, are also improved, though challenges remain in surface finish and geometrical resolution. In contrast, Electron Beam Powder Bed Fusion (EB-PBF) uses high-energy electron beams in a vacuum, eliminating oxidation and leveraging copper’s high conductivity to achieve high energy absorption at lower volumetric energy densities (~80 J/mm3). This results in consistently high relative densities (>99.5%) and excellent electrical and thermal conductivity, with additional benefits including faster scanning speeds and in situ monitoring capabilities. However, EB-PBF processes in general face their own limitations, such as surface roughness and powder smoking. This paper provides a comprehensive review of the current state of laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) powder bed fusion processes for the additive manufacturing of copper, summarizing key trends, material properties, and process innovations. Both approaches continue to evolve, with ongoing research aimed at refining these technologies to enable the reliable and efficient additive manufacturing of high-performance copper components.more » « less
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Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/powders1040014
