Abstract
The Cu-based shape-memory alloy 81Cu-11Al-3Ni-3Mn (wt%) was processed by selective laser melting (SLM) and the parameters were optimized to obtain samples with a high relative density of up to 99 %. The study used ball milling of elemental powders to obtain the feedstock material and the process was developed for building dense SMA structures. The process optimization is performed to obtain dense structures by varying laser power and scan speed. Further, the parameters yielding higher density is selected to study the influence of the different laser power on the overall density, defect distribution, mechanical properties, and grain sizes. Cu-based SMA fabricated at different laser powers were characterized using Scanning electron microscopy (SEM), Differential scanning calorimetry, X-ray diffraction (XRD), tensile test, and microhardness tests. It was observed that the processing technique has a strong influence on the degree of porosity and the distribution of the pores, the grain size as well as the grain morphology. Dense and uniform samples are formed with elemental powder which shows a maximum hardness of 380 HV, and tensile strength of 1550 MPa. The transformation peak became sharp with respect to laser power increment and the transformation temperature increased with an increase in laser power. The study provides a path toward the deployment of SLM for building dense Cu-based SMA from elemental powders.
Original language | English |
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Article number | 171029 |
Journal | Journal of Alloys and Compounds |
Volume | 961 |
Early online date | 17 Jun 2023 |
DOIs | |
Publication status | Published - 25 Oct 2023 |
Externally published | Yes |
Bibliographical note
Funding Information:The authors thank the Sophisticated Instrumentation Centre (SIC), IIT Indore for their support. The lead authors also thank the financial support from SERB OVDF [Award no.: SB/S9/Z-16/2016-VIII (2020-21)], INDIA.
Funding Information:
The authors thank the Sophisticated Instrumentation Centre (SIC), IIT Indore for their support. The lead authors also thank the financial support from SERB OVDF [Award no.: SB/S9/Z-16/2016-VIII (2020-21) ], INDIA.
Publisher Copyright:
© 2023 Elsevier B.V.