The First Layer: Single-Track Insights into Direct Energy Deposition Processed Cu-Ni Thermoelectric Alloys
The shift to sustainable energy has accelerated the development of thermoelectric (TE) material for direct heat-to-electricity conversion without batteries or grid reliance. Cu-Ni alloys show promise for high-power, thermally stable TE applications like waste heat recovery and electronics cooling bu...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Journal of Manufacturing and Materials Processing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4494/9/6/170 |
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| Summary: | The shift to sustainable energy has accelerated the development of thermoelectric (TE) material for direct heat-to-electricity conversion without batteries or grid reliance. Cu-Ni alloys show promise for high-power, thermally stable TE applications like waste heat recovery and electronics cooling but require thermal conductivity and microstructure optimization. This study investigates additive manufacturing (AM) of Cu-Ni alloys via laser powder-directed energy deposition (L-DED), enabling precise control over deposition parameters. Track geometries were analyzed using linear mass density (M<sub>L</sub>) and linear heat input (H<sub>L</sub>), which influence deposition quality and microstructural characteristics. A weighted qualitative process parameter decision matrix was developed to evaluate process conditions systematically. Optimal deposition was achieved with H<sub>L</sub> < 70 J/mm for M<sub>L</sub> ~0.016–0.021 g/mm and 98 J/mm < H<sub>L</sub> < 137 J/mm for M<sub>L</sub> = 0.026 g/mm, corresponding to an energy-to-mass ratio of ~4000 ± 500 kJ/g. While this study does not directly assess thermoelectric properties, it provides essential first-layer insights into how processing conditions affect track geometry, defect formation, and microstructure—information that is foundational for optimizing multi-layer builds and, ultimately, improving thermoelectric performance. These findings mark a critical step toward predictive process optimization and the accelerated design of Cu-Ni-based thermoelectric materials using AM techniques. |
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| ISSN: | 2504-4494 |