ICME Overview of Multiscale Structure-Property Relations for Cyclic Loading on Nitinol (NiTi) Procured from Additive Manufacturing (Laser Enhanced Net Shaping – LENS)

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Contents

Background

NiTi (i.e. Nitinol) is an almost equiatomic alloy of nickel and titanium and exhibits unique properties such as shape memory and superelasticity (also called pseudoelasticity). Shape memory is the ability of the material to recover a plastic strain by heating the material to above a certain temperature, while superelasticity is the capability of the material to recover strain levels as large as 4%-8% by just unloading. Because of these properties as well as high resistance to severe environmental conditions, NiTi has been utilized in various applications in civil engineering, automotive, aerospace and biomedical industries. Endovascular stents, endodontic files, and vena cava filters are some examples of NiTi application in the biomedical industry [1]. In addition, NiTi has been used for bio-implants [2] where an appropriate combination of Mechanical properties as well as biocompatibility is needed. In such applications, however, the main challenge is the size and geometry of the implant, which is patient-injury dependent, making the design and fabrication of the implant somewhat complicated. Laser Engineered Net Shaping (LENS) is a Direct Laser Deposition (DLD) additive manufacturing (AM) technique where the metal powder is injected into the melt pool created from the laser beam, forming the object. This method, first developed by Sandia National Laboratories in the late 1990s [3], provides the ability of producing and cladding metallic materials with complex geometries, which are difficult to fabricate by the conventional manufacturing techniques. In DLD process, components are fabricated layer-by-layer based on a sliced CAD model, and injecting metal powder into a molten pool, created by a laser beam. This process is repeated and consecutive layers are built in along the height, in order to fabricate the desired geometry. The microstructure-based multistage fatigue (MSF) model of McDowell et al. [4] is an appealing model for prognosis applications, particularly in the HCF regime, since it captures fatigue behavior in the initial stages of crack incubation and small crack growth, and explicitly addresses the role of microstructure. However, the model requires extensive experiments in the threshold crack growth regime and small crack growth rate evaluations [5] prior to application. Recently, the application of NiTi shape memory alloys has been extended to micro and nanoelectromechanical systems (MEMS/NEMS) [6]. To supplement experiments, the theoretical study of phase transitions by means of atomistic simulations such as molecular dynamics (MD) is highly desirable to provide a detailed understanding of the underlying mechanisms.

Fig. 1. The multiscale bridges that were defined for the downscaling requirements and the associated upscaling simulation results for the multistage fatigue (MSF) model.

Multiscale Modeling approach

The length scale for multiscale modeling of MultiStage Fatigue Model (MSF) of additively manufactured NiTi can be obtained by downscaling the MSF model. A dog bone fatigue NiTi specimen is used as an example throughout the paper. As shown in Fig. 1, the MSF model for a dog bone fatigue NiTi specimen can be downscaled to the macroscale, microscale, nanoscale, and electronic scale. In this paper, the constitutive models in each length scale and the bridging of information between adjacent length scales are determined.

Macroscale

The MSF model, which is used as either a material point simulator or a postprocessor for a finite element simulation, was developed from a multiscale modeling methodology in order to obtain information about macro crack growth, fatigue life, and stresses at this level. However, in order to study cracks, we need to know about crack nucleation and crack growth rate which are obtained from a lower length scale (Horstemeyer, 2012). [7].

Microscale

In MSF model, the highest scale of information is coming from macroscale which is the experiment fatigue data, then for feeding the MSF model, microscopic information such as grain size, voids, distance is required for calculating ΔCTD McDowell et al.[8].

Atomistic Scale

Using techniques like Modified Embedded Atom Method (MEAM), the values of MSC growth rate can be calculated and passed through the next level. The alloying element plays an important role in determining the MSC growth rate [9].

Electronic Scale

The electronic scale uses the Density Field Theory (DFT) to calculate elastic moduli for different crystal structures of NiTi: cubic and monoclinic. Similarly, the information on interfacial energy of NiTi and elasticity calculated at this level are passed to the upper level.

References

  1. [Plotino, Gianluca, et al. "A review of cyclic fatigue testing of nickel-titanium rotary instruments." Journal of endodontics 35.11 (2009): 1469-1476. [1]]
  2. [Andani, Mohsen Taheri, et al. "Metals for bone implants. Part 1. Powder metallurgy and implant rendering." Acta biomaterialia 10.10 (2014): 4058-4070. [2]]
  3. [Griffith, M., et al. "Laser Engineered Net Shaping (LENS) for Fabrication of Metallic Components." ASME International Mechanical Engineering Congress and Exposition. 1996. [3]]
  4. [McDowell, D. L., et al. "Microstructure-based fatigue modeling of cast A356-T6 alloy." Engineering Fracture Mechanics 70.1 (2003): 49-80. [4]]
  5. [Newman, James, et al. "Compression pre-cracking to generate near threshold fatigue-crack-growth rates in two aluminum alloys." International journal of fatigue 27.10 (2005): 1432-1440. [5]]
  6. [Greer, Julia R., and Jeff Th M. De Hosson. "Plasticity in small-sized metallic systems: Intrinsic versus extrinsic size effect." Progress in Materials Science 56.6 (2011): 654-724. [6]]
  7. [Horstemeyer, Mark F. Integrated Computational Materials Engineering (ICME) for metals: using multiscale modeling to invigorate engineering design with science. John Wiley & Sons, 2012. [[7]]>
  8. [McDowell, D. L., et al. "Microstructure-based fatigue modeling of cast A356-T6 alloy." Engineering Fracture Mechanics 70.1 (2003): 49-80. [8]]
  9. [Kuo, Albert S., and H. W. Liu. "An analysis of unzipping model for fatigue crack growth." Scripta Metallurgica 10.8 (1976): 723-728. [9]]
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