Optimising the laser powder bed fusion processing parameters of Cu-Al-Ni shape memory alloys: microstructure and functional properties relationship

Shape memory alloys (SMA) are intelligent materials that could benefit from additive manufacturing, creating actuators with near-net-morphologies for each particular application. Herein, the complete processing route of a Cu–Al–Ni SMA through Laser Powder Bed Fusion (LPBF) is presented. Beginning from the design of the alloy and the atomization process, the strategy of LPBF is studied focusing on the optimization of the experimental parameters to obtain the highest compaction and optimal functional properties. The produced samples were characterized along the different steps of the processing route, including the thermal transformation, the microstructure and the functional shape memory and superelastic properties. Electron back-scatter diffraction analysis shows a columnar grains microstructure with a preferential [001] texture on the LPBF building direction, which has a noticeable effect on the functional properties. Fully recoverable superelastic effect of 2.7 % tensile strain, and shape memory recovery of ± 4 % bending strain were obtained in LPBF processed samples. These functional performances are compared with those of a powder metallurgy reference sample, evidencing the enhancement of the superelasticity and shape memory obtained by the LPBF processing method. These results pave the road for producing Cu-Al-based SMA by additive manufacturing.

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Publication Details

Journal
Virtual and Physical Prototyping
Published
2025-02-22
DOI
https://doi.org/10.1080/17452759.2025.2458679
Citations
2
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
1.43

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article

Optimising the laser powder bed fusion processing parameters of Cu-Al-Ni shape memory alloys: microstructure and functional properties relationship

Ernesto Urionabarrenetxea, M.L. Nó, I. Ruiz‐Larrea, Nerea Burgos et al.
2 citations
Virtual and Physical Prototyping
Additive Manufacturing Materials and Processes
1.43
article

Optimising the laser powder bed fusion processing parameters of Cu-Al-Ni shape memory alloys: microstructure and functional properties relationship

Ernesto Urionabarrenetxea, M.L. Nó, I. Ruiz‐Larrea, Nerea Burgos, Mikel Pérez-Cerrato, Ízaro Ayesta, J. San Juán, Jose F. Gómez‐Cortés
article en
2 citations

Abstract

Shape memory alloys (SMA) are intelligent materials that could benefit from additive manufacturing, creating actuators with near-net-morphologies for each particular application. Herein, the complete processing route of a Cu–Al–Ni SMA through Laser Powder Bed Fusion (LPBF) is presented. Beginning from the design of the alloy and the atomization process, the strategy of LPBF is studied focusing on the optimization of the experimental parameters to obtain the highest compaction and optimal functional properties. The produced samples were characterized along the different steps of the processing route, including the thermal transformation, the microstructure and the functional shape memory and superelastic properties. Electron back-scatter diffraction analysis shows a columnar grains microstructure with a preferential [001] texture on the LPBF building direction, which has a noticeable effect on the functional properties. Fully recoverable superelastic effect of 2.7 % tensile strain, and shape memory recovery of ± 4 % bending strain were obtained in LPBF processed samples. These functional performances are compared with those of a powder metallurgy reference sample, evidencing the enhancement of the superelasticity and shape memory obtained by the LPBF processing method. These results pave the road for producing Cu-Al-based SMA by additive manufacturing.

Virtual and Physical PrototypingVol. 20(1)
University of the Basque Country (ES), Universidad de Navarra (ES)
Eusko Jaurlaritza, Euskal Herriko Unibertsitatea, Ministerio de Ciencia e Innovación
Affordable and clean energy
Openalex Percentile: Top 22%
Additive Manufacturing Materials and Processes
1.43
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