Multi-scale pseudoelasticity of NiTi alloys fabricated by laser additive manufacturing

Zhang, Dongzhe; Li, Yunze; Cong, Weilong

doi:10.1016/j.msea.2021.141600

Cited by 19 publications

(8 citation statements)

References 57 publications

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“…Various factors may contribute to the reduction in shape memory and superelastic strain in additively manufactured NiTi. The microstructure might contain phases which do not contribute to the shape memory effect, like Ti 2 NiO x , TiC, Ni 4 Ti 3 [29], Ni 3 Ti [53], or phases which exhibit only a small shape memory effect like the R-phase [78]. The relatively small shape memory effect reported in this work might be due to severe uptake of oxygen and carbon during sintering at the high temperature of 1290 °C.…”

Section: Thermo-mechanical Properties Of 3d-printed Nitimentioning

confidence: 96%

“…Besides NiTi alloys [8][9][10][11][12][13][17][18][19][20], CoNiGa high temperature SMAs [21], CuAlNi [22,23], ferrous SMAs [24,25], and magnetic NiMnGa SMAs [26][27][28] have been fabricated by PBF. Directed energy deposition was used for fabrication of NiTi [29,30], CuAlNi [31], and CoNiGa [32]. In addition, NiTi has been fabricated via Wire arc AM [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Filament extrusion-based additive manufacturing of NiTi shape memory alloys

et al. 2023

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Section: Thermo-mechanical Properties Of 3d-printed Nitimentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Filament extrusion-based additive manufacturing of NiTi shape memory alloys

et al. 2023

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“…The hardness of NiTi alloy is complicated and highly depends on the existing phase and the temperature. The hardness of austenite phase is significantly larger than that of martensite phase [147]. Thus stress-induced martensitic transformation or martensite variant reorientation and dislocation have a great influence on the Vickers hardness [65,131].…”

Section: Damping Properties and Hardnessmentioning

confidence: 99%

Laser powder bed fusion additive manufacturing of NiTi shape memory alloys: a review

Wei

Zhang

et al. 2023

Int. J. Extrem. Manuf.

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NiTi alloys have drawn significant attention in biomedical and aerospace fields due to their unique shape memory effect (SME), superelasticity (SE), damping characteristics, high corrosion resistance, and good biocompatibility. Because of the unsatisfying processabilities and manufacturing requirements of complex NiTi components, additive manufacturing technology, especially laser powder bed fusion (LPBF), is appropriate for fabricating NiTi products. This paper comprehensively summarizes recent research on the NiTi alloys fabricated by LPBF, including printability, microstructural characteristics, phase transformation behaviors, lattice structures, and applications. Process parameters and microstructural features mainly influence the printability of LPBF-processed NiTi alloys. The phase transformation behaviors between austenite and martensite phases, phase transformation temperatures, and an overview of the influencing factors are summarized in this paper. This paper provides a comprehensive review of the mechanical properties with unique strain-stress responses are also explained, which comprise tensile mechanical properties, thermomechanical properties (e.g., critical stress to induce martensite transformation, thermo-recoverable strain, and superelasticity strain), damping properties and hardness. Moreover, several common structures (e.g., a negative Poisson’s ratio structure and a diamond-like structure) are considered, and the corresponding studies are summarized. It illustrates the various fields of application, including biological scaffolds, shock absorbers, and driving devices. In the end, the paper concludes with the main achievements from the recent studies and puts forward the limitations and development tendencies in the future.

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“…Considering the above literature comprehensively, this paper presents a multi-objective optimization method to obtain minimizing surface roughness and maximizing superelasticity. The superelasticity can be measured by the remnant depth ratio (η p ) [25], so the regression equations of surface roughness and remnant depth ratio are established by the turning experiment of NiTi SMA based on RSM. The optimized parameters are cutting speed (126 m/min), feed rate (0.11 mm/rev), and depth of cut (0.14 mm).…”

Section: Introductionmentioning

confidence: 99%

Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

Zhao,

Cui,

Sivalingam

et al. 2023

Materials

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This study aimed to optimize machining parameters to obtain better surface roughness and remnant depth ratio values under dry turning of NiTi-shape memory alloy (SMA). During the turning experiments, various machining parameters were used, including three different cutting speeds vc (105, 144, and 200 m/min), three different feed rates f (0.05, 0.1, and 0.15 mm/rev), and three different depths of cut ap (0.1, 0.15, and 0.2 mm). The effects of machining parameters in turning experiments were investigated on the response surface methodology (RSM) with Box–Behnken design (BBD) using the Design Expert 11; how the cutting parameters affect the surface quality is discussed in detail. In this context, the cutting parameters were successfully optimized using a genetic algorithm (GA). The optimized processing parameters are vc = 126 m/min, f = 0.11 mm/rev, ap = 0.14 mm, resulting in surface roughness and remnant depth ratio values of 0.489 μm and 64.13%, respectively.

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Multi-scale pseudoelasticity of NiTi alloys fabricated by laser additive manufacturing

Cited by 19 publications

References 57 publications

Filament extrusion-based additive manufacturing of NiTi shape memory alloys

Filament extrusion-based additive manufacturing of NiTi shape memory alloys

Laser powder bed fusion additive manufacturing of NiTi shape memory alloys: a review

Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

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