Additive manufacturing technologies, viz. laser powder bed fusion (LPBF), represent a promising alternative for the direct manufacturing of Cu-based shape memory alloys (Cu-SMAs) and fully functional parts [1]. The complex interplay of the processing conditions with the microstructure (e.g., grain size distribution) and shape-memory behavior (e.g., shape-recovery) has been studied intensively at IFW Dresden for high-temperature Cu-SMAs (e.g., Cu-Al-Ni-Mn). The possibility to shift the transformation temperatures (TTs) to higher values via increasing the energy input during processing, without compromising the mechanical behavior (pseudoelastic strains around 4%), has shown that Cu-SMAs are ideal candidates for 4D printing approaches. Thus, the continuing development of the LPBF process for the flexible manufacturing of Cu-SMAs with a locally adjusted transformation behavior is of high interest for potential shape-memory applications.

In our study, a Cu-11.85Al-3.2Ni-3Mn high-temperature shape memory alloy has been processed for the first time via LPBF using a 1 kW top-hat (or flat-top) laser source (diameter around 700 µm). A process window, in which samples with a high relative density above 99% can be obtained for various energy inputs, has been established by fine-tuning the process parameters. This allowed a systematic investigation of selected specimens in terms of the phase formation, microstructure, and mechanical behavior under compression with respect to the applied parameters. The transformation behavior can be correlated with the processing conditions and is compared with LPBF samples that have been produced using a gaussian laser beam (diameter around 90 µm, laser power = 330 W).

The use of a high-power top-hat laser during LPBF of Cu-11.85Al-3.2Ni-3Mn proves to be a promising approach to prepare shape-memory parts with enhanced built-up rates due to applicable layer thicknesses above 200 µm. The new freedom in process parameter design allows an in-situ adjustment of the TTs in a range of 20 °C without any additional rescanning (e.g., remelting) or post-processing and paths the way for novel process strategies regarding programmable functional materials.

[1] Gustmann, T.: PhD Thesis, TU Dresden (2018).