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.
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
- NSF-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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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/powders1040014
