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Thermo mechanical analyses and characterizations of TiNiCu shape memory alloy structures developed by laser additive manufacturing
Journal of Manufacturing Processes 48 (2019) 98–109
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Thermo mechanical analyses and characterizations of TiNiCu shape memory alloy structures developed by laser additive manufacturing
T
S. Shivaa,b,*, N. Yadaiahc, I.A. Palanib,d, C.P. Paule,f, K.S. Bindrae a
Department of Mechanical Engineering, Indian Institute of Technology Jammu, India Mechatronics and Instrumentation Laboratory, Indian Institute of Technology Indore, India c Discipline of Mechanical Engineering,, North Eastern Regional Institute of Science and Technology, Nirjuli, India d Discipline of Metallurgy Engineering and Material Science, Indian Institute of Technology Indore, India e Laser Technology Division, Raja Ramanna Centre for Advanced Technology, India f Homi Bhabha National Institute, BARC Training School Complex, Anushakti Nagar, Mumbai, India b
A R T I C LE I N FO
A B S T R A C T
Keywords: TiNiCu Shape memory alloy Laser additive manufacturing XRD DSC SEM
Three different compositions of TiNiCu (Ti50Ni (50−x) Cux (x = 10, 20 and 30)) shape memory alloys (SMA) were developed using an intelligent manufacturing technique of laser additive manufacturing (LAM). Based on a numerical analysis the nature and amount of residual stress flow was predicted. By implementing finite element method (FEM) with Gaussian distributed volumetric heat source, the deposition process was simulated. The numerical and experimental analyses were at par with each other. The developed samples were subjected to several characterizations in order to determine the best among them. Scanning electron microscopy (SEM), atomic force microscopy (AFM), were used to study the surface morphology of the samples. The mechanical properties were studied using micro-hardness test and compression test. X-Ray diffraction (XRD) was deployed to investigate the crystalline nature of the samples. The phase transformation ability of the samples were determined by differential scanning calorimetry (DSC). The SEM revealed the deposition of all three samples to be homogeneous. The AFM results showed the grain size of TiNiCu10 to be 20.12 nm, the smallest among the samples. The micro-hardness and ultimate strength of TiNiCu10 was found to be 242 VHN and MPa respectively. XRD reveals the presence of three step transformation for TiNiCu20 sample. From the results, LAM process was considered as a successful methodology in developing TiNiCu bulk SMA structures. The properties of laser additive manufactured TiNiCu10 was found to be the best among the developed samples.
1. Introduction Shape memory alloys (SMA) are smart materials that function by the internal changes like phase transformation based on external parameters like temperature, strains and other forces [1]. Using the smartness of SMA, various applications are executed efficiently, in the line of micro electro mechanical systems (MEMS) [2–4]. However the widely used applications are obtained by having SMA in the form of thin film. Bulk SMAs are used in biomedical and vibration damping applications [5,6]. Among the several SMAs available, Ni-Ti is the most preferred option in both, thin film and bulk structure applications. Though Ni-Ti is capable of performing well in several applications successfully, the problem of hysteresis that reduces the life cycle of NiTi is yet to be addressed. Also, using Ni-Ti as bio implants leads to allergies, and carcinogenic effects, due to the release of Ni into the blood stream [7]. In order to overcome the existing issues, alloying a
⁎
third element with Ni-Ti was practiced [8]. The third element was chosen with immense heed, aiming to maintain the performance efficiency of Ni-Ti even after becoming ternary alloy. Several elements were like Pt, Hf, Zr, Fe, Nb, Al, Au, Cu were tried as options by the researchers in the past [8]. Except Cu, the remaining options did not meet up with the same efficiency of binary Ni-Ti due to various reasons [8]. The replacement of Ni with Cu as compared to other elements derives many advantages: smaller transformation hysteresis; lesser martensite start temperature; sensivity in compositional dependence; more consistent martensite start temperature independent of number of actuation cycles; subtraction of Ti3Ni4 precipitate yielding more ductility in the alloys [9,10] and improved damping efficiency. The diffusion of Cu atoms into Ti is comparatively higher than Ni and all the other mentioned elements which brings Cu as the primary choice to maintain the same efficiency of binary Ni-Ti. Also the diffusion ratio of Cu and Ni into Ti is higher than the diffusion of Ti into Cu or Ni [9], and
Corresponding author. E-mail address: [email protected] (S. Shiva).
https://doi.org/10.1016/j.jmapro.2019.11.003 Received 27 February 2018; Received in revised form 13 September 2019; Accepted 1 November 2019 1526-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
Journal of Manufacturing Processes 48 (2019) 98–109
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similar to the actual process. The base plate was not fixed with any fixtures. All the displacements along X, Y and Z directions are considered at the starting point of laser source and the displacement along Z direction is zero at the end point of the first track on the base plate. Since, LAM is a thermal process, the heat conduction equation plays a crucial role in the physical modelling of the deposition process. The heat conduction equation comes from the energy balance of an appropriate volume chosen and consists of diffusive and convective heat flows with the possible sources of heat [26]. Three dimensional transient heat conduction equation in Cartesian coordinates system, when laser source moves in Y-axis, can be represented as
∂ 2T ∂ 2T ∂ 2T ⎞ ∂T ∂T ⎞ km ⎛ 2 + + = ρCp ⎛ −V 2 2 ∂ x ∂ y ∂ z ∂ t ∂y ⎠ ⎝ ⎠ ⎝
Fig. 1. Prevailing TiNiCu manufacturing techniques.
⎜
⎟
⎜
⎟
(1)
where (x, y, z) is the coordinate system attached with the heat source. km is the thermal conductivity (W m-1 K-1) and is given by the following equation to compensate the fluid flow of the molten material as
this tendency may be attributed to the nature of Cu to attract more Ti electrons which assist good TiCu bonding [11]. Hence Ti creates a base for the alloy formation where Cu and Ni sequentially get along to bond with Ti leading to the formation of TiNiCu ternary alloy [9]. By reducing the thermal hysteresis and improving the actuation response TiNiCu ternary alloy has overcome the major hindrance faced by the binary Ni-Ti alloy. The method of manufacturing TiNiCu must be made sure that no harm is brought to the shape memory property of the product. The literature reports, the successful methods of manufacturing TiNiCu alloys in three different types as shown in Fig. 1. Under thermal methods, processes like flash evaporation [12], molecular beam epitaxy [13] and vacuum plasma melting assisted by hot rolling [14] are categorized. Methods like powder metallurgy [15], mechanical alloying [16] and sputtering [17–19] are categorized under diffusion methods. Finally drawing technique like melt spin [20] represent forming method. Using the reported methods mostly TiNiCu is developed in the form of thin films, whereas development of bulk structures is not explored much. Also developing complex bulk structures in desired composition precisely remains to be a challenging part. With existing powder metallurgical techniques, complex structures can be developed with certain post processing techniques, but maintaining the composition homogeneously is difficult. LAM is a renowned technique to manufacture complex products without changing the physical and chemical properties [21,22]. The potential of LAM to fabricate SMA was proved in our earlier work as well [23]. Based on the literature survey, it is clear that the manufacturing of TiNiCu bulk structures using LAM is not explored widely. Also, it is very clear that most of the FEM analysis is carried out to study the actuation characteristics of TiNiCu thin films [24,25]. The studies on the amount of residual stress and temperature generated during the development of ternary TiNiCu alloy as bulk structure is not explored to the best of our knowledge. Hence the current study paves way to analyze the amount of residual stress and temperature, flow during the development, which plays a crucial role in the actuation properties. The current study also helps to determine the pattern of deposition to maintain the quality of the alloy. Different compositions of premixed powders were used to fabricate bulk TiNiCu structures and sub sequentially were subjected to various characterizations to analyze their nature.
ko, T < Tm km = ⎧ ko ⎨ ⎩ + k ′, T ≥ Tm
(2)
where ko is the thermal conductivity of the material used, k’ is the additional value by which the convection heat transfer capability is considered in the thermal model, Tm is the melting point of TiNiCu. T is the temperature variable (K), ρ is the density (kg m-3), Cp is the specific heat capacity (J kg-1 K-1) of TiNiCu. t is time variable (s) and v is the deposition velocity (m s-1). The natural boundary condition can be represented mathematically as
kn
∂T – q+ h (T − To) + σ ε (T4 − To4) = 0 ∂n
(3)
where σ, ε, kn, q, h, and To illustrates Stefan-Boltzmann constant, emissivity, thermal conductivity (W m-1 K-1) normal to the surface, imposed heat flux onto the surface (W), convection heat transfer coefficient (W m-2 K-1). The value of h = 153 W m-2 K-1, is calculated using the standard method as mentioned by the researchers in the past [27], the absorption coefficient is considered as 0.3 which is estimated using the methodology in published literature [28]. and ambient temperature respectively (K). The term q stands for the heat input fed by laser source that is considered to follow a Gaussian distribution that can be mathematically represented as
qs =
Pηd d. y 2 ⎞ d. x 2 exp ⎛− 2 − π r2 r r2 ⎠ ⎝ ⎜
⎟
(4)
where P refers to the power of heat source (W), η is laser source efficiency, r is the laser spot radius (mm) of the heat source, and d is the power density distribution factor of heat source. The values of η, d, and r are considered as 90 %, 3, and 2 mm, respectively. The properties of TiNiCu, taken for simulation are as quoted in Table 1. The initial condition for the transient heat transfer analysis of the LAM process is stated at time t = 0 as T (x,y,z,0) = To
2. Numerical model
(5)
where To is the initial temperature (K), (room temperature) in the present work. The governing equation along with the boundary conditions is discretized to a set of algebraic equations in matrix form to solve unknown temperature field in the domain considered for the solution. The calculated time step with the chosen laser spot diameter, deposition rate and track length is 40 s per track. The complete deposition process is exhibited as a multistep transient heat transfer analysis where each time step is further divided into a number of smaller time increments. This requires an integration of the heat conduction equation with respect to time. In the finite element formulation, this equation can be written for each element as [30]
The Fig. 2 represents a schematic view of TiNiCu bulk structures being deposited using LAM, and the typical boundary conditions, considered in the present heat transfer analysis. The heat transfer analysis was initiated, followed by mechanical analysis. In thermal analysis, the heat input is considered as Gaussian distributed and the convection & radiation heat losses from the surfaces of work piece were taken into account. Moreover, in mechanical analysis, the temperature generated is considered as thermal load along with mechanical boundary conditions. In the current work, the mechanical boundary conditions are 99
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Fig. 2. Three dimensional solution geometry for the applied boundary conditions. Table 1 Material Properties of TiNiCu. Properties Density Specific Heat Melting Point Poisson Ratio Emissivity Thermal Conductivity Young’s Modulus Thermal Expansion Coefficient
[C (T )]{T˙ } + [K (T )]{T } + {V } = {Q (T )}
γxy = Unit
TiNiCu 3
kg/m J/kgoC C – – W/moC GPa o -1 C o
6450 460 1300 0.3 0.4 0.13 78 15.4 × 10-6
{dε } = {dε t } + {dε p} + {dε e}
∂u ∂v ∂w ;εy = ; εz = ∂x ∂y ∂z
(10)
The Eq. (9) refers to the reduced equation of Eq. (6) for a specific time step. Similarly, Eq. (9) will vary for other time step, since, the thermal conductivity and specific heat varies with temperature. Following Prandtl-Reuss flow rule and Von-Mises yield criteria, the incremental stress can be represented as
(6)
{dσ } = [Dep ]{dε } − [D e]{⍺}(ΔT )
(11)
Where
∂f ∂f T 1 ⎞⎟ [Dep] = ⎜⎛ [D e] − [D e] ⎧ ⎫ ⎧ ⎫ [D e] ⎨ ⎬ ⎨ ∂ σ ∂ 3G + ET ⎠ ⎩ ⎭⎩ σ ⎬ ⎭ ⎝
(12)
where [De] depicts the elasticity matrix which consists of mechanical properties like Young’s modulus E (GPa) and Poisson’s ratio μ. G is the shear modulus (GPa) and ET is the local slope between stress and plastic strain of specified material. ⍺ represents the thermal expansion. Eq.s (10–12) relate to mechanical analysis part such as the strain-displacement relations, the increment of the total strain and Prandtl-Reuss flow rule.The last term of the equation, represents the thermal strain which varies depending the temperature distribution. [Dep] is some sort of elastic-plastic matrix where the first term in Eq. (12) is due to elastic response of material or recovery of elastic response when the material is in plastic zone. The second term of the Eq. (12) is due to plastic flow of material which is zero when the material is elastic zone only. The evolution of the yield surface is governed by the bilinear isotropic hardening rule. The sample model of single tracks deposited parallely and mounted on each other to be developed using AutoCAD and fed in Ansys 14.0 with TiNiCu material properties as quoted in Table 1 [32]. After fixing the initial and thermal boundary conditions for the model, the corresponding elements for temperature analyses (SOLID 70) and thermo mechanical analyses (SOLID 45) are to be chosen. The self-developed APDL codes were used to input the heat source in the simulation. The ADPL codes were developed in the following steps. Initially the CAD model for solution domain was designed
(7)
where [K¯ ] is the modified form of conductivity and specific heat capacity matrix at current time step and {Q¯ } is the modified form of load vector at current time step. In the current work, thermo-mechanical analysis was carried out using ANSYS parametric design language (APDL). The element SOLID 45 was chosen for mechanical analysis. In the present work, the material response was assumed as thermo-elasto-plastic along with mechanical properties. The elasto-plastic analysis was generally performed by incremental mode of stress and strain. Rate independent plasticity is considered followed by Von-Mises criterion, the associated flow rule and bilinear isotropic hardening behavior. In Cartesian coordinates system, the strain-displacement relation can be written as [30,31].
εx =
(9)
where u, v and w represents displacements in x, y, z directions respectively; εx, εy and εz refer to the normal strains in x, y and z directions respectively; and γxy, γyz and γzx represents shear strains in xy, yz and zx planes respectively. Assuming the isotropic material, the thermal strain remains same in three directions and the increment of the total strain is sum of the incremental plastic strain, incremental thermal strain and incremental elastic strain, represented as
where [K] is conductivity matrix, [C] is specific heat matrix, {T} is vector of nodal temperatures, {T˙ } is vector of time derivative of {T}, {V} is velocity vector and {Q} is nodal heat flow vector. The Eq. (6) is just the vector and matrix equivalent of Eq. (1) [29]. To solve these system equations, a standard variation technique, Crank-Nicholson/Euler theta integration method in which the equations are solved at discrete time points within the transient, is applied. The first iteration in the solution procedure solves the system equations at an assumed starting temperature (ambient temperature) and subsequent iterations use temperatures from previous iterations to calculate the specific heat and thermal conductivity matrices. This can be represented mathematically as [30]
[K¯ ]{T } = {Q¯ }
∂u ∂v ∂v ∂w ∂w ∂u + ; γyz = + ;γ = + ∂y ∂x ∂z ∂y xy ∂x ∂z
(8)
100
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Fig. 4. Mesh independency test of the proposed model.
coaxial nozzle and a twin powder feeder are interconnected using a computer control that assists precise development of samples automatically [34]. A sand blasted 100 mm × 100 mm x 10 mm titanium plate was used as substrate. Sandblasting was done to improve the bonding at the interface between the deposited sample and the substrate surface. A controlled atmosphere with argon gas was maintained throughout the deposition process. Published reports intimate the percentage of Cu in TiNiCu to be crucial in determining the number of steps to be involved in phase transformation [16]. The usual number of steps involved in the phase transformation are two {austenite (B2) = > martensite (B19′)}, when the percentage of Cu remains less than 7 in the alloy. Eventually as the percentage of Cu exceeds 7, the number of steps involved in phase transformation raises to three {(austenite (B2) = > orthorhombic (B19) = > martensite (B19′)} [15]. In a motive to investigate the presence of various steps in phase transformation in TiNiCu alloy developed using LAM, three different compositions of TiNiCu (Ti-50 %Ni-40%Cu-10 %, Ti-50 %Ni-30%Cu-20%, Ti-50 %Ni20%Cu-30%) were opted. The pre mixing of powders was ensured to be homogeneous by using a table top powder mixer that functions under the inverse kinematics based principle. In subsequent sections, the powder ratios of TiNiCuTi-50 %Ni-40%Cu-10 %, Ti-50 %Ni-30%Cu20%, Ti-50 %Ni-20%Cu-30% are designated as TiNiCu10, TiNiCu20, TiNiCu30, respectively. The deposition process was based on the results incurred from the numerical studies performed earlier and explained in detail in the session ahead. The parameters used for deposition are as quoted in Table 2. The Ti, Ni and Cu powders were procured from MEC, India. The shape of the powders was made sure to be in spherical shape expecting a good deposition. The laser beam used for the LAM was multimode with a flat top beam profile and the powders (Make: MEC, India) (Ni, Ti and Cu) selected for LAM had the particle size in the range of 45−106 μm for Ni and Cu, whereas for Ti it was in the range of 75–150 μm. The range of powder particle size was selected based upon the observations of the preliminary experiments that showed the powder particles less than 45 μm tend to fly away in an uncontrolled manner due to the higher surface area to weight ratio. Similarly, the powder particles greater than 110 μm results in semi-fused deposition due to smaller absorbed laser energy per unit volume of each particle. The Ti powder was angular or blocky in shape, whereas Ni and Cu powders were spherical in shape with a smooth surface as shown in Fig. 6. A number of brick shaped structures of TiNiCu were fabricated using laser additive manufacturing as shown in Fig. 7 Just after the fabrication, the samples in as fabricated condition were brought under several investigations. Subsequently, samples were
Fig. 3. Entire work process of the thermal and thermo mechanical analyses.
and was discretized into, finite number of elements using element SOLID 70. Sub sequentially, model parameters and the process parameter were defined. In transient analysis, the total process time is divided into small time steps, and then the heat transfer analysis is implemented for each time step explicitly. Similarly, in mechanical analysis, the FE model used in thermal analysis is considered with element SOLID 45 and the mechanical material properties are defined. The reading of output temperatures is considered for overall thermal analysis at different time steps with the, imposed boundary conditions the mechanical analysis is performed. In the current work, the mechanical boundary conditions are much similar to the actual process. The base plate was not fixed with any fixtures. All the displacements along X, Y and Z directions are considered at the starting point of laser source and the displacement along Z direction is zero at the end point of the first track on the base plate. Finally the maximum amount of temperature and residual stress generated in the samples are to be determined to finalize the pattern of deposition. The entire work process is as shown in Fig. 3. Prior to the proposed simulation model, the mesh size independency was established using the ratio of the least geometric dimension (DL) of the model and element size (e). It was observed that the temperature difference in successive runs of reduced element size by a factor of two becomes less than 10-3 for DL/e > 30. In the present simulation study, DL/e = 40 is used and it corresponds to an element size of 50 μm [33]. Hence fine mesh of the mentioned size is used in the simulation process. In addition, all required element quality checks of the FE model were performed as shown in Fig. 4. The developed numerical methodology was deployed to study the effect of deposition pattern, predicting the temperature and residual stress distribution. 3. Experimental procedure The LAM system used for developing TiNiCu samples, is equipped with a 2 kW fiber laser as the heat source to melt the pre mixed powders of Ti, Ni, and Cu. The experimental setup diagram of the LAM system deployed in the current work is as shown in Fig. 5. The assembled intelligent manufacturing system was developed indigenously with a 5 axis workstation in a glove box, a computerized numerical controller, a 101
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Fig. 5. LAM experimental set up used to develop the TiNiCu samples. Table 2 Parameters used for experiments. Parameter
Unit
Values
Laser Power Laser Diameter Scanning Speed Powder Feed rate Argon Gas gushing rate
kW mm m/min g/min l/min
1-1.5 2 0.2 - 0.5 5-8 10
cut transverse to the track laying direction and prepared using standard metallographic techniques for further examination. The micro structure analyses by optical microscope (Make: Dewinter, Model: DMI Prime). The scanning electron microscopy (Make: Zeiss, Model: Supra55) attached with energy dispersive spectrograph (Make: Oxford Instruments, Model: X-mas), was used for the surface morphological analyses. The nanometric level surface analyses were carried out using atomic force microscope (AFM) (Make: Nanoscope-E, Model: NSE). The microhardness (Make: UHL, Model: VMH 002), compression test (Make: Biss, Model: Makron UTS-02-0010) was used to analyze the mechanical properties of the samples. Optical profilometer (Make: Veeco, Model: NT9080) was used to find the surface roughness measurements. The Xray diffractor (Make: Rigaku, Model: Smart lab Automated Multipurpose) were used for crystal structure studies. Differential scanning calorimetry (Make: Netzsch, Model: DSC 214) was used for studying the phase transformation property.
Fig. 7. TiNiCu brick structures of various compositions developed by LAM.
4. Results and discussion 4.1. Simulation and validation In order to finalize the pattern of depositing the samples a numerical study was carried out based on the material properties in order to reduce the trial of experiments. A thermal model was generated to study the amount of temperature and residual stress distribution during the development of the process. Hence the simulation was carried out to build the samples vertical and horizontal directions to compare the temperature and stress distribution during the sample development. Initially the entire model was finely meshed and converted into small elements. The heat input was based on the laser input energy. The convective boundary conditions were chosen as discussed earlier. Fig. 8 shows the complete set of simulations developed for vertically and horizontally developed samples. The simulated models were fed
Fig. 6. The surface morphology of the powder particles used for deposition. 102
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Fig. 8. The simulated results of (a) temperature distribution and (b) residual stress distribution of vertically developed samples and (c) temperature distribution and (d) residual stress distribution of horizontally developed samples.
with the material properties of TiNiCu. From the figure it is vividly seen that the temperature distribution in the samples gradually decreases as the number of layers mounting increase. This may be attributed to the temperature distributed in the layers at the bottom. Similarly Fig. 8(b) and (d) shows the overall residual stress generated on the three layers using the above mentioned properties and equations in the previous sections. The temperature and residual stress distribution seem to be evenly distributed throughout the developed samples. This nature of distribution has high chances of influencing the SME properties of the developed samples. Simulated results were validated using in-situ temperature measurement by thermal imaging camera (Make: Luma Sense Technologies, Model: Luma Spec TM RT). The real time temperature distributed on the surface of the sample and the deposit was acquired and analysed using the Luma Sense software, and XRD method was deployed to measure the residual stress distribution. A preliminary experiment was attempted by developing the sample vertically for the single wall layer as shown in Fig. 9 and the development of several problems were detected. Also the prime factor of wetting of the surface of the samples due to high temperature brings change in the dimension of the sample. Also generation of micro cracks become inevitable due to greater accumulation of temperature and residual stress. In case of parallel
deposition the mentioned problems don’t occur as the temperature and stress gets uniformly distributed and the chances of accumulating in the samples are comparatively lesser. Hence the risk of secondary phase formation is also very less. The measured and simulated values are as shown in the Fig. 10(a) and (b). From the figure it is vivid that the numerical values are a bit higher than the experimental values. The decrease in the real time experimentation values is attributed to the radiation losses. Similar impact is observed in the residual stress distribution as well. The simulation results and experimental results show the parallely developed samples generate high temperature on the surface and that simultaneously reflects in reducing the residual stress level in the samples. In vertically built samples high residual stress is generated due to the nonuniform temperature flow within the samples. While comparing the images in Fig. 10 the numerical and experimental results of parallely developed sample are closer to each other. Hence, considering the factors mentioned above, the samples developed for the current research were in a parallel pattern.
4.2. Surface morphology In Fig. 11 the SEM images of the formed samples are displayed. The surface morphology of all three samples shows homogeneous crack free deposition of all three samples. The presence of porosity holes are not detected and the particles are close and tightly packed. The Fig. 11(a) shows the surface of TiNiCu10 in which presence of a coarse surface is detected with presence of mild precipitates. The Fig. 11(b) shows the presence of huge amount of precipitates making the surface look with more bumps and dumps. Whereas Fig. 11(b) shows the surface of TiNiCu20 with small dents and craters on the surface. From the surface nature the sample is expected to be in brittle nature. From the images, surface roughness of TiNiCu30 is expected to be among the highest of the three samples To investigate the compositional variation in the
Fig. 9. Vertically developed TiNiCu structure using LAM. 103
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Fig. 10. The comparison of experimental and numerical values for (a) temperature distribution (b) residual stress distribution in the developed TiNiCu sample.
samples after deposition, energy dispersive spectroscopy (EDS) was opted. The results of EDS are as quoted in Table 3. For all samples a mild increase in the percentage of Ti is observed and this may be attributed to the mixture of Ti particles from the substrate into the samples during LAM. Variations in other elements like Ni and Cu may be attributed to the mild precipitates formed during the melting process. LAM process is a powder metallurgical technique in which, the formation of Ti2 (NiCu) precipitate is inevitable, when heat is distributed above 700 °C during the deposition process. Ti2 (NiCu) precipitate increases the percentage of Ni and Cu in the sample, fluctuating the compositions of the alloys after deposition. Similar effects in TiNiCu alloys are reported when developed by other powder metallurgical
Table 3 EDS results of TiNiCu samples developed by LAM. Integrands
TiNiCu10
TiNiCu20
TiNiCu30
Ti Ni Cu
58.27 ± 2 % 38.65 ± 3 % 3.08 ± 2 %
57.56 ± 4 % 28.24 ± 3 % 14.2 ± 1 %
55.28 ± 2 % 23.98 ± 2 % 20.74 ± 3 %
methods as well [35]. To have a preview about the nature of the samples more deeply, microstructure of the samples were examined. In Fig. 12, the micro structures of all three samples are shown. The standard metallographic
Fig. 11. SEM images showing the surface morphology of the formed TiNiCu samples. 104
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Fig. 12. Microstructure images of (a) TiNiCu10, (b) TiNiCu20 and (c) TiNiCu30 samples.
precipitates, like Ti(NiCu3) which prevents the formation of the interface region between two or three grain boundaries nearby. Hence the grains are flocking together in large size without much interface region between them. The stored energy due to asperity deformation during deposition is expected to be the reason behind the absence of interface region. Due to the above mentioned reasons it is very obvious that the surface roughness of TiNiCu20 sample is to be higher than TiNiCu10. In case of TiNiCu30 the needle like structures are observed in the microstructure which clearly indicates the surface roughness to be higher than the remaining two samples. Hence the surface roughness values of TiNiCu30 sample is just above the value of TiNiCu20. The obtained results can also be compared with the surface roughness values in nanometric level obtained from atomic force microscopy (AFM) presented in the forthcoming session. The Fig. 13 shows the results of atomic force microscopy (AFM). The Fig. 13(a), 13(b), 13(c) represents TiNiCu10, TiNiCu20, and TiNiCu30 respectively. The magnifications of TiNiCu10 were 1 μm2 and TiNiCu20, TiNiCu30 were 500 nm2. Similar to the SEM results the dents in TiNiCu30 is clearly visible in Fig. 13(c). The grain size detected from AFM of TiNiCu10, TiNiCu20 and TiNiCu30 were found to be 20.12 nm, 33.57 nm and 25.51 nm respectively. TiNiCu10 has lots of wax like structures on the surface and the projection of hillock structures are very less compared to the other two samples. TiNiCu20 has lots of hillock and valley structures on the surface. The measured grain size shows TiNiCu20 to be having the maximum grain size among the three samples. TiNiCu30 has semi waxed structures with bigger islands are in the form of clustered hillock structures with pointed round tip. The SEM, micro structure and AFM results are in close alignment with the surface roughness value obtained using the surface profilometer. The surface roughness of TiNiCu10 is 3.97 nm, TiNiCu20 is 4.25 nm and TiNiCu30 is 5.83 nm. From the surface morphology analyses TiNiCu10 is expected to have good shape memory properties. The analyses of
procedures were followed to obtain the microstructures. The etchant used was HF, HNO3 and H2O in the ratio 1:12:4 [36]. The most common observation in all the three images are the closely packed structures without any porosity holes or cracks. TiNiCu10 as shown in Fig. 12 (a) has microstructures formed with good grain boundaries. A small precipitate like structure is visible in the microstructure. This may be the small gap generated due to the removal of certain particles during micro polishing. The grain boundaries are large in size indicating possibilities of high ductility in the sample. In Fig. 12 (b) the grains of TiNiCu20 are shown and it is very clear that the formation of grain boundary is hindered by the presence of random dentrical structures. The reason may be attributed to the non-availability of enough time to form the grain structure due to quick cooling effect or precipitate formation. Also precipitates, like TiCu generally have the tendency to generate grain boundary fracture that induces brittleness in the sample [37]. Presence of needle like structures indicate the martensite phase domination in TiNiCu30 as shown in Fig. 12 (c). As the martensite phase is high a good phase transformation ability can be expected in the sample. The surface roughness was measured in the as deposited state using a surface profilometer. The surface roughness of TiNiCu10, TiNiCu20 and TiNiCu30 are 5.64 μm, 6.32 μm and 6.46 μm respectively. The surface roughness results obtained, closely align with the SEM results. The TiNiCu30 had the maximum surface roughness value as expected from the SEM results. The TiNiCu10 has the minimum surface roughness value among the three samples. From the microstructure diagram it is clear that the grain boundaries of TiNiCu10 sample are very much closely packed. This may be attributed to the grain boundary migration homogeneously taking place during the diffusion bonding of the alloys at the time of deposition. This leads to the formation of smooth surface on the sample. But the grain boundaries are not clearly visible for TiNiCu20 sample and this may be attributed to the formation of 105
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Fig. 13. AFM images of (a) TiNiCu10 (b) TiNiCu20 (c) TiNiCu30.
strength and the young’s modulus values of the sample are as quoted in Table 4. The curve of TiNiCu20 reaches the martensite plateau level with some initial elasticity in the sample. But the elastic deformation is not visible ahead of the detwinning point. The Young’s modulus value is also lesser than the TiNiCu10 sample. Though the graph of TiNiCu30 is quite similar to TiNiCu20, the brittle nature is confirmed from the very low elastic modulus. From Table 4 it is very clear TiNiCu30 has the least ultimate strength and young’s modulus values. As discussed earlier the role of precipitates or excessive slip happening in the sample is expected to be the reason. The growth of the curve itself reveals the brittle nature of the sample. The compression test results closely align with micro-hardness and surface morphological results.
mechanical properties is much essential in the applications point of view. Hence, in the session ahead the results of the mechanical properties are presented.
4.3. Mechanical properties The cold mounted samples were finely polished for micro-hardness test as per the metallographic standards. A load of 5 N was intended and a distance of 25 μm was maintained between two indentations. Ten readings were taken for each sample, expecting precision in the results. As shown in Fig. 14 the micro-hardness values of TiNiCu10, TiNiCu20 and TiNiCu30 are 242 +15 VHN, 278 + 123 VHN and 297 + 40 VHN respectively. The micro-hardness generally increases with the percentage of Cu in the TiNiCu alloys. The results closely align with the observation reported by researchers in the past [38]. The standard deviation of TiNiCu20 sample is higher than the remaining two samples, due to the formation of precipitates that reduce the strength of the sample. In case of TiNiCu30 the maximum percentage of Cu aggravates the formation of precipitates, like TiCu leading to the increase of dislocation density that sequentially increases the micro-hardness of the sample. As the micro-hardness is maximum for TiNiCu30, the sample is expected to be brittle in nature. The TiNiCu10 sample has the minimum micro-hardness value among the three and is expected to be ductile in nature, as the observation closely aligns with the surface morphological results as well. The Fig. 15 shows the compression test results of the TiNiCu samples for comparison. The stress strain curve of TiNiCu10 resembles the typical pattern of binary NiTi alloys. The elastic behavior of the sample is vividly seen initially, as the curve elongates till the martensite plateau. The yield point after the plateau indicates the detwinning process in the sample. The elastic deformation of the sample prolongs the curve for a while, reaching the ultimate strength of the sample. The ultimate
4.4. Crystalline nature The Fig. 16, presents the XRD results of all the three TiNiCu samples undertaken for investigation. The presence of two phases austenite and martensite are vivid in the results of all samples,. The results of TiNiCu10 reveal small martensite peaks popping up very close to each other between 2θ values 41°–43°. The B2 austenite peak is exactly present at 2θ = 45°7′ and 2θ = 53°4′. For TiNiCu20 samples the presence of intermediate martensite phase in orthorhombic structure (B19) is observed at 2θ = 40°26′ just before the austenite B2 peak. The internal strain caused during the deposition process reduces the stability of B19 martensite phase [39]. The strain generates the driving force to transform B19 to B19′ in the reverse cycle. The B2 phase peak is at 2θ = 43°08′ In the current work the generated strain forces decreased the d spacing in the lattice structure that leads to peak shift towards lesser 2θ value. Also the presence of two step martensite evolution in TiNiCu20 may lead to lots of martensite phases in the sample, at room temperature. The martensite B19′ peaks are positioned in 2θ = 39°57′ and 2θ = 62°64′. The TiNiCu30 has only two visible peaks. One B19 austenite peak and B19′ martensite at 44°8′ and 64°27′ respectively. Crystallite size can be calculated using the Scherrer formula:
d=
0.9λ BCosθ
(14)
Where’d’ stands for crystallite size, λ stands for the wavelength of the X-Radiation used, B is the peak width at half the intensity, and θ is the Bragg angle [40]. Using the Scherrer formula, the calculated grain size of TiNiCu10, TiNiCu20 and TiNiCu30 are 19.56 nm, 32.73 nm and 24.62 nm, respectively. These values are almost very close to the grain sizes obtained through AFM as reported earlier. 4.5. Phase transformation properties The Fig. 17 shows the DSC curves of TiNiCu10, TiNiCu20 and TiNiCu30 respectively. For all three samples visible peaks are obtained in both heating and cooling curves. The presence of TiCu is expected to be the cause of very small peak formation in TiNiCu30. The phase transformation temperatures of all the three samples martensite start (Ms),
Fig. 14. Micro-hardness results of TiNiCu10, TiNiCu20 and TiNiCu30. 106
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Fig. 15. Compression test results of TiNiCu10, TiNiCu20 and TiNiCu30.
properties in the lattice structure leading to an alteration in the transformation sequence. Also the formation of phases like Ti2(NiCu) prevents the evolution of B19 peak by reducing the amount of heat in the sample. The absence of TiCu phases in the alloy induces very low Ms temperature in TiNiCu10 and TiNiCu20 samples as mentioned in the XRD results previously. From Fig. 17(c) the presence of TiCu precipitate in TiNiCu30 sample is very much evident, as the Ms temperature is very high. The peak width of TiNiCu10 and TiNiCu20 is narrow in both heating and cooling cycles, whereas very wide peaks are obtained for TiNiCu30 due to dislocations caused by rapid solidification and inbuilt stress during fabrication [41].
Table 4 Compression test results of TiNiCu samples developed by LAM. Sample
Ultimate Strength MPa
Elastic Modulus GPa
TiNiCu10 TiNiCu20 TiNiCu30
430 ± 7 402 ± 12 386 ± 9
24 ± 2 21 ± 4 20 ± 3
5. Conclusion In present study, an indigenously developed LAM system was deployed successfully for fabricating ternary shape memory alloy structures of different compositions. The study investigated the pattern of depositing TiNiCu structures ensuring reduced accumulation of residual stress and homogeneous temperature distribution during the fabrication process through numerical simulation and experimental work. Both simulation and experimental studies were at par and confirmed that the deposition pattern in unilateral parallel direction yields the best results. Various examination of different composition of TiNiCu alloy under investigation (Ti50Ni (50−x) Cux (x = 10, 20 and 30)) revealed that TiNiCu30 had the highest surface roughness and highest hardness, while the large grain boundaries are observed for TiNiCu10 sample indicating good ductility. The elastic modulus was also highest for TiNiCu10 samples. Moreover, two step phase transformation similar to conventionally produced samples was observed in TiNiCu20 samples. But the formation of TiCu precipitates affected the strength of the sample and inducing more brittleness in TiNiCu20. During DSC examination, all the samples exhibited visible phase transformation, however, only TiNiCu10 are the best choice for real time on account of high brittleness, which can further be improved by post processing techniques, like - annealing or laser surface processing. Further, the obtained simulation and experimental results can be used a precursor for microstructure engineering investigation to improve TiNiCu20 and TiNiCu30 samples. Declaration of Competing Interest Fig. 16. XRD results of TiNiCu10, TiNiCu20 and TiNiCu30 samples.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
martensite finish (Mf), austenite start (As) and austenite finish (Af) temperatures are as quoted in Table 5. From the results in Table 5 it is very obvious that the phase transformation temperatures are completely different for all three samples. This may be attributed to the compositional effects and the sub sequential secondary phase formation in the samples. The DSC result of TiNiCu20 sample does not reveal the martensite phase transformation in two steps as only one peak is visible. The absence of second peak in TiNiCu20 may be attributed to the internal strain detracting the elastic
Acknowledgement The authors would like to thank the Sophisticated Instrument Centre (SIC) in Indian Institute of Technology Indore for providing the necessary characterization facilities. Thanks are also due to the members of LAM laboratory, RRCAT, Indore for the technical help provided to develop the samples. 107
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Fig. 17. DSC results of TiNiCu10, TiNiCu20 and TiNiCu30 samples.
Table 5 Phase transformation temperatures of TiNiCu samples obtained from DSC graphs. Sample
Martensite Start Temperature (Ms)
Martensite Finish Temperature (Mf)
Austenite Start Temperature (As)
Austenite Finish Temperature (Af)
TiNiCu10 TiNiCu20 TiNiCu30
35.88 °C 59.91 °C 63.12 °C
35.14 °C 59.05 °C 62.21 °C
38.24 °C 64.26 °C 64.57 °C
39.03 °C 65.95 °C 65.94 °C
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Journal of Manufacturing Processes journal homepage: www.elsevier.com/locate/manpro
Thermo mechanical analyses and characterizations of TiNiCu shape memory alloy structures developed by laser additive manufacturing
T
S. Shivaa,b,*, N. Yadaiahc, I.A. Palanib,d, C.P. Paule,f, K.S. Bindrae a
Department of Mechanical Engineering, Indian Institute of Technology Jammu, India Mechatronics and Instrumentation Laboratory, Indian Institute of Technology Indore, India c Discipline of Mechanical Engineering,, North Eastern Regional Institute of Science and Technology, Nirjuli, India d Discipline of Metallurgy Engineering and Material Science, Indian Institute of Technology Indore, India e Laser Technology Division, Raja Ramanna Centre for Advanced Technology, India f Homi Bhabha National Institute, BARC Training School Complex, Anushakti Nagar, Mumbai, India b
A R T I C LE I N FO
A B S T R A C T
Keywords: TiNiCu Shape memory alloy Laser additive manufacturing XRD DSC SEM
Three different compositions of TiNiCu (Ti50Ni (50−x) Cux (x = 10, 20 and 30)) shape memory alloys (SMA) were developed using an intelligent manufacturing technique of laser additive manufacturing (LAM). Based on a numerical analysis the nature and amount of residual stress flow was predicted. By implementing finite element method (FEM) with Gaussian distributed volumetric heat source, the deposition process was simulated. The numerical and experimental analyses were at par with each other. The developed samples were subjected to several characterizations in order to determine the best among them. Scanning electron microscopy (SEM), atomic force microscopy (AFM), were used to study the surface morphology of the samples. The mechanical properties were studied using micro-hardness test and compression test. X-Ray diffraction (XRD) was deployed to investigate the crystalline nature of the samples. The phase transformation ability of the samples were determined by differential scanning calorimetry (DSC). The SEM revealed the deposition of all three samples to be homogeneous. The AFM results showed the grain size of TiNiCu10 to be 20.12 nm, the smallest among the samples. The micro-hardness and ultimate strength of TiNiCu10 was found to be 242 VHN and MPa respectively. XRD reveals the presence of three step transformation for TiNiCu20 sample. From the results, LAM process was considered as a successful methodology in developing TiNiCu bulk SMA structures. The properties of laser additive manufactured TiNiCu10 was found to be the best among the developed samples.
1. Introduction Shape memory alloys (SMA) are smart materials that function by the internal changes like phase transformation based on external parameters like temperature, strains and other forces [1]. Using the smartness of SMA, various applications are executed efficiently, in the line of micro electro mechanical systems (MEMS) [2–4]. However the widely used applications are obtained by having SMA in the form of thin film. Bulk SMAs are used in biomedical and vibration damping applications [5,6]. Among the several SMAs available, Ni-Ti is the most preferred option in both, thin film and bulk structure applications. Though Ni-Ti is capable of performing well in several applications successfully, the problem of hysteresis that reduces the life cycle of NiTi is yet to be addressed. Also, using Ni-Ti as bio implants leads to allergies, and carcinogenic effects, due to the release of Ni into the blood stream [7]. In order to overcome the existing issues, alloying a
⁎
third element with Ni-Ti was practiced [8]. The third element was chosen with immense heed, aiming to maintain the performance efficiency of Ni-Ti even after becoming ternary alloy. Several elements were like Pt, Hf, Zr, Fe, Nb, Al, Au, Cu were tried as options by the researchers in the past [8]. Except Cu, the remaining options did not meet up with the same efficiency of binary Ni-Ti due to various reasons [8]. The replacement of Ni with Cu as compared to other elements derives many advantages: smaller transformation hysteresis; lesser martensite start temperature; sensivity in compositional dependence; more consistent martensite start temperature independent of number of actuation cycles; subtraction of Ti3Ni4 precipitate yielding more ductility in the alloys [9,10] and improved damping efficiency. The diffusion of Cu atoms into Ti is comparatively higher than Ni and all the other mentioned elements which brings Cu as the primary choice to maintain the same efficiency of binary Ni-Ti. Also the diffusion ratio of Cu and Ni into Ti is higher than the diffusion of Ti into Cu or Ni [9], and
Corresponding author. E-mail address: [email protected] (S. Shiva).
https://doi.org/10.1016/j.jmapro.2019.11.003 Received 27 February 2018; Received in revised form 13 September 2019; Accepted 1 November 2019 1526-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
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similar to the actual process. The base plate was not fixed with any fixtures. All the displacements along X, Y and Z directions are considered at the starting point of laser source and the displacement along Z direction is zero at the end point of the first track on the base plate. Since, LAM is a thermal process, the heat conduction equation plays a crucial role in the physical modelling of the deposition process. The heat conduction equation comes from the energy balance of an appropriate volume chosen and consists of diffusive and convective heat flows with the possible sources of heat [26]. Three dimensional transient heat conduction equation in Cartesian coordinates system, when laser source moves in Y-axis, can be represented as
∂ 2T ∂ 2T ∂ 2T ⎞ ∂T ∂T ⎞ km ⎛ 2 + + = ρCp ⎛ −V 2 2 ∂ x ∂ y ∂ z ∂ t ∂y ⎠ ⎝ ⎠ ⎝
Fig. 1. Prevailing TiNiCu manufacturing techniques.
⎜
⎟
⎜
⎟
(1)
where (x, y, z) is the coordinate system attached with the heat source. km is the thermal conductivity (W m-1 K-1) and is given by the following equation to compensate the fluid flow of the molten material as
this tendency may be attributed to the nature of Cu to attract more Ti electrons which assist good TiCu bonding [11]. Hence Ti creates a base for the alloy formation where Cu and Ni sequentially get along to bond with Ti leading to the formation of TiNiCu ternary alloy [9]. By reducing the thermal hysteresis and improving the actuation response TiNiCu ternary alloy has overcome the major hindrance faced by the binary Ni-Ti alloy. The method of manufacturing TiNiCu must be made sure that no harm is brought to the shape memory property of the product. The literature reports, the successful methods of manufacturing TiNiCu alloys in three different types as shown in Fig. 1. Under thermal methods, processes like flash evaporation [12], molecular beam epitaxy [13] and vacuum plasma melting assisted by hot rolling [14] are categorized. Methods like powder metallurgy [15], mechanical alloying [16] and sputtering [17–19] are categorized under diffusion methods. Finally drawing technique like melt spin [20] represent forming method. Using the reported methods mostly TiNiCu is developed in the form of thin films, whereas development of bulk structures is not explored much. Also developing complex bulk structures in desired composition precisely remains to be a challenging part. With existing powder metallurgical techniques, complex structures can be developed with certain post processing techniques, but maintaining the composition homogeneously is difficult. LAM is a renowned technique to manufacture complex products without changing the physical and chemical properties [21,22]. The potential of LAM to fabricate SMA was proved in our earlier work as well [23]. Based on the literature survey, it is clear that the manufacturing of TiNiCu bulk structures using LAM is not explored widely. Also, it is very clear that most of the FEM analysis is carried out to study the actuation characteristics of TiNiCu thin films [24,25]. The studies on the amount of residual stress and temperature generated during the development of ternary TiNiCu alloy as bulk structure is not explored to the best of our knowledge. Hence the current study paves way to analyze the amount of residual stress and temperature, flow during the development, which plays a crucial role in the actuation properties. The current study also helps to determine the pattern of deposition to maintain the quality of the alloy. Different compositions of premixed powders were used to fabricate bulk TiNiCu structures and sub sequentially were subjected to various characterizations to analyze their nature.
ko, T < Tm km = ⎧ ko ⎨ ⎩ + k ′, T ≥ Tm
(2)
where ko is the thermal conductivity of the material used, k’ is the additional value by which the convection heat transfer capability is considered in the thermal model, Tm is the melting point of TiNiCu. T is the temperature variable (K), ρ is the density (kg m-3), Cp is the specific heat capacity (J kg-1 K-1) of TiNiCu. t is time variable (s) and v is the deposition velocity (m s-1). The natural boundary condition can be represented mathematically as
kn
∂T – q+ h (T − To) + σ ε (T4 − To4) = 0 ∂n
(3)
where σ, ε, kn, q, h, and To illustrates Stefan-Boltzmann constant, emissivity, thermal conductivity (W m-1 K-1) normal to the surface, imposed heat flux onto the surface (W), convection heat transfer coefficient (W m-2 K-1). The value of h = 153 W m-2 K-1, is calculated using the standard method as mentioned by the researchers in the past [27], the absorption coefficient is considered as 0.3 which is estimated using the methodology in published literature [28]. and ambient temperature respectively (K). The term q stands for the heat input fed by laser source that is considered to follow a Gaussian distribution that can be mathematically represented as
qs =
Pηd d. y 2 ⎞ d. x 2 exp ⎛− 2 − π r2 r r2 ⎠ ⎝ ⎜
⎟
(4)
where P refers to the power of heat source (W), η is laser source efficiency, r is the laser spot radius (mm) of the heat source, and d is the power density distribution factor of heat source. The values of η, d, and r are considered as 90 %, 3, and 2 mm, respectively. The properties of TiNiCu, taken for simulation are as quoted in Table 1. The initial condition for the transient heat transfer analysis of the LAM process is stated at time t = 0 as T (x,y,z,0) = To
2. Numerical model
(5)
where To is the initial temperature (K), (room temperature) in the present work. The governing equation along with the boundary conditions is discretized to a set of algebraic equations in matrix form to solve unknown temperature field in the domain considered for the solution. The calculated time step with the chosen laser spot diameter, deposition rate and track length is 40 s per track. The complete deposition process is exhibited as a multistep transient heat transfer analysis where each time step is further divided into a number of smaller time increments. This requires an integration of the heat conduction equation with respect to time. In the finite element formulation, this equation can be written for each element as [30]
The Fig. 2 represents a schematic view of TiNiCu bulk structures being deposited using LAM, and the typical boundary conditions, considered in the present heat transfer analysis. The heat transfer analysis was initiated, followed by mechanical analysis. In thermal analysis, the heat input is considered as Gaussian distributed and the convection & radiation heat losses from the surfaces of work piece were taken into account. Moreover, in mechanical analysis, the temperature generated is considered as thermal load along with mechanical boundary conditions. In the current work, the mechanical boundary conditions are 99
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Fig. 2. Three dimensional solution geometry for the applied boundary conditions. Table 1 Material Properties of TiNiCu. Properties Density Specific Heat Melting Point Poisson Ratio Emissivity Thermal Conductivity Young’s Modulus Thermal Expansion Coefficient
[C (T )]{T˙ } + [K (T )]{T } + {V } = {Q (T )}
γxy = Unit
TiNiCu 3
kg/m J/kgoC C – – W/moC GPa o -1 C o
6450 460 1300 0.3 0.4 0.13 78 15.4 × 10-6
{dε } = {dε t } + {dε p} + {dε e}
∂u ∂v ∂w ;εy = ; εz = ∂x ∂y ∂z
(10)
The Eq. (9) refers to the reduced equation of Eq. (6) for a specific time step. Similarly, Eq. (9) will vary for other time step, since, the thermal conductivity and specific heat varies with temperature. Following Prandtl-Reuss flow rule and Von-Mises yield criteria, the incremental stress can be represented as
(6)
{dσ } = [Dep ]{dε } − [D e]{⍺}(ΔT )
(11)
Where
∂f ∂f T 1 ⎞⎟ [Dep] = ⎜⎛ [D e] − [D e] ⎧ ⎫ ⎧ ⎫ [D e] ⎨ ⎬ ⎨ ∂ σ ∂ 3G + ET ⎠ ⎩ ⎭⎩ σ ⎬ ⎭ ⎝
(12)
where [De] depicts the elasticity matrix which consists of mechanical properties like Young’s modulus E (GPa) and Poisson’s ratio μ. G is the shear modulus (GPa) and ET is the local slope between stress and plastic strain of specified material. ⍺ represents the thermal expansion. Eq.s (10–12) relate to mechanical analysis part such as the strain-displacement relations, the increment of the total strain and Prandtl-Reuss flow rule.The last term of the equation, represents the thermal strain which varies depending the temperature distribution. [Dep] is some sort of elastic-plastic matrix where the first term in Eq. (12) is due to elastic response of material or recovery of elastic response when the material is in plastic zone. The second term of the Eq. (12) is due to plastic flow of material which is zero when the material is elastic zone only. The evolution of the yield surface is governed by the bilinear isotropic hardening rule. The sample model of single tracks deposited parallely and mounted on each other to be developed using AutoCAD and fed in Ansys 14.0 with TiNiCu material properties as quoted in Table 1 [32]. After fixing the initial and thermal boundary conditions for the model, the corresponding elements for temperature analyses (SOLID 70) and thermo mechanical analyses (SOLID 45) are to be chosen. The self-developed APDL codes were used to input the heat source in the simulation. The ADPL codes were developed in the following steps. Initially the CAD model for solution domain was designed
(7)
where [K¯ ] is the modified form of conductivity and specific heat capacity matrix at current time step and {Q¯ } is the modified form of load vector at current time step. In the current work, thermo-mechanical analysis was carried out using ANSYS parametric design language (APDL). The element SOLID 45 was chosen for mechanical analysis. In the present work, the material response was assumed as thermo-elasto-plastic along with mechanical properties. The elasto-plastic analysis was generally performed by incremental mode of stress and strain. Rate independent plasticity is considered followed by Von-Mises criterion, the associated flow rule and bilinear isotropic hardening behavior. In Cartesian coordinates system, the strain-displacement relation can be written as [30,31].
εx =
(9)
where u, v and w represents displacements in x, y, z directions respectively; εx, εy and εz refer to the normal strains in x, y and z directions respectively; and γxy, γyz and γzx represents shear strains in xy, yz and zx planes respectively. Assuming the isotropic material, the thermal strain remains same in three directions and the increment of the total strain is sum of the incremental plastic strain, incremental thermal strain and incremental elastic strain, represented as
where [K] is conductivity matrix, [C] is specific heat matrix, {T} is vector of nodal temperatures, {T˙ } is vector of time derivative of {T}, {V} is velocity vector and {Q} is nodal heat flow vector. The Eq. (6) is just the vector and matrix equivalent of Eq. (1) [29]. To solve these system equations, a standard variation technique, Crank-Nicholson/Euler theta integration method in which the equations are solved at discrete time points within the transient, is applied. The first iteration in the solution procedure solves the system equations at an assumed starting temperature (ambient temperature) and subsequent iterations use temperatures from previous iterations to calculate the specific heat and thermal conductivity matrices. This can be represented mathematically as [30]
[K¯ ]{T } = {Q¯ }
∂u ∂v ∂v ∂w ∂w ∂u + ; γyz = + ;γ = + ∂y ∂x ∂z ∂y xy ∂x ∂z
(8)
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Fig. 4. Mesh independency test of the proposed model.
coaxial nozzle and a twin powder feeder are interconnected using a computer control that assists precise development of samples automatically [34]. A sand blasted 100 mm × 100 mm x 10 mm titanium plate was used as substrate. Sandblasting was done to improve the bonding at the interface between the deposited sample and the substrate surface. A controlled atmosphere with argon gas was maintained throughout the deposition process. Published reports intimate the percentage of Cu in TiNiCu to be crucial in determining the number of steps to be involved in phase transformation [16]. The usual number of steps involved in the phase transformation are two {austenite (B2) = > martensite (B19′)}, when the percentage of Cu remains less than 7 in the alloy. Eventually as the percentage of Cu exceeds 7, the number of steps involved in phase transformation raises to three {(austenite (B2) = > orthorhombic (B19) = > martensite (B19′)} [15]. In a motive to investigate the presence of various steps in phase transformation in TiNiCu alloy developed using LAM, three different compositions of TiNiCu (Ti-50 %Ni-40%Cu-10 %, Ti-50 %Ni-30%Cu-20%, Ti-50 %Ni20%Cu-30%) were opted. The pre mixing of powders was ensured to be homogeneous by using a table top powder mixer that functions under the inverse kinematics based principle. In subsequent sections, the powder ratios of TiNiCuTi-50 %Ni-40%Cu-10 %, Ti-50 %Ni-30%Cu20%, Ti-50 %Ni-20%Cu-30% are designated as TiNiCu10, TiNiCu20, TiNiCu30, respectively. The deposition process was based on the results incurred from the numerical studies performed earlier and explained in detail in the session ahead. The parameters used for deposition are as quoted in Table 2. The Ti, Ni and Cu powders were procured from MEC, India. The shape of the powders was made sure to be in spherical shape expecting a good deposition. The laser beam used for the LAM was multimode with a flat top beam profile and the powders (Make: MEC, India) (Ni, Ti and Cu) selected for LAM had the particle size in the range of 45−106 μm for Ni and Cu, whereas for Ti it was in the range of 75–150 μm. The range of powder particle size was selected based upon the observations of the preliminary experiments that showed the powder particles less than 45 μm tend to fly away in an uncontrolled manner due to the higher surface area to weight ratio. Similarly, the powder particles greater than 110 μm results in semi-fused deposition due to smaller absorbed laser energy per unit volume of each particle. The Ti powder was angular or blocky in shape, whereas Ni and Cu powders were spherical in shape with a smooth surface as shown in Fig. 6. A number of brick shaped structures of TiNiCu were fabricated using laser additive manufacturing as shown in Fig. 7 Just after the fabrication, the samples in as fabricated condition were brought under several investigations. Subsequently, samples were
Fig. 3. Entire work process of the thermal and thermo mechanical analyses.
and was discretized into, finite number of elements using element SOLID 70. Sub sequentially, model parameters and the process parameter were defined. In transient analysis, the total process time is divided into small time steps, and then the heat transfer analysis is implemented for each time step explicitly. Similarly, in mechanical analysis, the FE model used in thermal analysis is considered with element SOLID 45 and the mechanical material properties are defined. The reading of output temperatures is considered for overall thermal analysis at different time steps with the, imposed boundary conditions the mechanical analysis is performed. In the current work, the mechanical boundary conditions are much similar to the actual process. The base plate was not fixed with any fixtures. All the displacements along X, Y and Z directions are considered at the starting point of laser source and the displacement along Z direction is zero at the end point of the first track on the base plate. Finally the maximum amount of temperature and residual stress generated in the samples are to be determined to finalize the pattern of deposition. The entire work process is as shown in Fig. 3. Prior to the proposed simulation model, the mesh size independency was established using the ratio of the least geometric dimension (DL) of the model and element size (e). It was observed that the temperature difference in successive runs of reduced element size by a factor of two becomes less than 10-3 for DL/e > 30. In the present simulation study, DL/e = 40 is used and it corresponds to an element size of 50 μm [33]. Hence fine mesh of the mentioned size is used in the simulation process. In addition, all required element quality checks of the FE model were performed as shown in Fig. 4. The developed numerical methodology was deployed to study the effect of deposition pattern, predicting the temperature and residual stress distribution. 3. Experimental procedure The LAM system used for developing TiNiCu samples, is equipped with a 2 kW fiber laser as the heat source to melt the pre mixed powders of Ti, Ni, and Cu. The experimental setup diagram of the LAM system deployed in the current work is as shown in Fig. 5. The assembled intelligent manufacturing system was developed indigenously with a 5 axis workstation in a glove box, a computerized numerical controller, a 101
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Fig. 5. LAM experimental set up used to develop the TiNiCu samples. Table 2 Parameters used for experiments. Parameter
Unit
Values
Laser Power Laser Diameter Scanning Speed Powder Feed rate Argon Gas gushing rate
kW mm m/min g/min l/min
1-1.5 2 0.2 - 0.5 5-8 10
cut transverse to the track laying direction and prepared using standard metallographic techniques for further examination. The micro structure analyses by optical microscope (Make: Dewinter, Model: DMI Prime). The scanning electron microscopy (Make: Zeiss, Model: Supra55) attached with energy dispersive spectrograph (Make: Oxford Instruments, Model: X-mas), was used for the surface morphological analyses. The nanometric level surface analyses were carried out using atomic force microscope (AFM) (Make: Nanoscope-E, Model: NSE). The microhardness (Make: UHL, Model: VMH 002), compression test (Make: Biss, Model: Makron UTS-02-0010) was used to analyze the mechanical properties of the samples. Optical profilometer (Make: Veeco, Model: NT9080) was used to find the surface roughness measurements. The Xray diffractor (Make: Rigaku, Model: Smart lab Automated Multipurpose) were used for crystal structure studies. Differential scanning calorimetry (Make: Netzsch, Model: DSC 214) was used for studying the phase transformation property.
Fig. 7. TiNiCu brick structures of various compositions developed by LAM.
4. Results and discussion 4.1. Simulation and validation In order to finalize the pattern of depositing the samples a numerical study was carried out based on the material properties in order to reduce the trial of experiments. A thermal model was generated to study the amount of temperature and residual stress distribution during the development of the process. Hence the simulation was carried out to build the samples vertical and horizontal directions to compare the temperature and stress distribution during the sample development. Initially the entire model was finely meshed and converted into small elements. The heat input was based on the laser input energy. The convective boundary conditions were chosen as discussed earlier. Fig. 8 shows the complete set of simulations developed for vertically and horizontally developed samples. The simulated models were fed
Fig. 6. The surface morphology of the powder particles used for deposition. 102
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Fig. 8. The simulated results of (a) temperature distribution and (b) residual stress distribution of vertically developed samples and (c) temperature distribution and (d) residual stress distribution of horizontally developed samples.
with the material properties of TiNiCu. From the figure it is vividly seen that the temperature distribution in the samples gradually decreases as the number of layers mounting increase. This may be attributed to the temperature distributed in the layers at the bottom. Similarly Fig. 8(b) and (d) shows the overall residual stress generated on the three layers using the above mentioned properties and equations in the previous sections. The temperature and residual stress distribution seem to be evenly distributed throughout the developed samples. This nature of distribution has high chances of influencing the SME properties of the developed samples. Simulated results were validated using in-situ temperature measurement by thermal imaging camera (Make: Luma Sense Technologies, Model: Luma Spec TM RT). The real time temperature distributed on the surface of the sample and the deposit was acquired and analysed using the Luma Sense software, and XRD method was deployed to measure the residual stress distribution. A preliminary experiment was attempted by developing the sample vertically for the single wall layer as shown in Fig. 9 and the development of several problems were detected. Also the prime factor of wetting of the surface of the samples due to high temperature brings change in the dimension of the sample. Also generation of micro cracks become inevitable due to greater accumulation of temperature and residual stress. In case of parallel
deposition the mentioned problems don’t occur as the temperature and stress gets uniformly distributed and the chances of accumulating in the samples are comparatively lesser. Hence the risk of secondary phase formation is also very less. The measured and simulated values are as shown in the Fig. 10(a) and (b). From the figure it is vivid that the numerical values are a bit higher than the experimental values. The decrease in the real time experimentation values is attributed to the radiation losses. Similar impact is observed in the residual stress distribution as well. The simulation results and experimental results show the parallely developed samples generate high temperature on the surface and that simultaneously reflects in reducing the residual stress level in the samples. In vertically built samples high residual stress is generated due to the nonuniform temperature flow within the samples. While comparing the images in Fig. 10 the numerical and experimental results of parallely developed sample are closer to each other. Hence, considering the factors mentioned above, the samples developed for the current research were in a parallel pattern.
4.2. Surface morphology In Fig. 11 the SEM images of the formed samples are displayed. The surface morphology of all three samples shows homogeneous crack free deposition of all three samples. The presence of porosity holes are not detected and the particles are close and tightly packed. The Fig. 11(a) shows the surface of TiNiCu10 in which presence of a coarse surface is detected with presence of mild precipitates. The Fig. 11(b) shows the presence of huge amount of precipitates making the surface look with more bumps and dumps. Whereas Fig. 11(b) shows the surface of TiNiCu20 with small dents and craters on the surface. From the surface nature the sample is expected to be in brittle nature. From the images, surface roughness of TiNiCu30 is expected to be among the highest of the three samples To investigate the compositional variation in the
Fig. 9. Vertically developed TiNiCu structure using LAM. 103
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Fig. 10. The comparison of experimental and numerical values for (a) temperature distribution (b) residual stress distribution in the developed TiNiCu sample.
samples after deposition, energy dispersive spectroscopy (EDS) was opted. The results of EDS are as quoted in Table 3. For all samples a mild increase in the percentage of Ti is observed and this may be attributed to the mixture of Ti particles from the substrate into the samples during LAM. Variations in other elements like Ni and Cu may be attributed to the mild precipitates formed during the melting process. LAM process is a powder metallurgical technique in which, the formation of Ti2 (NiCu) precipitate is inevitable, when heat is distributed above 700 °C during the deposition process. Ti2 (NiCu) precipitate increases the percentage of Ni and Cu in the sample, fluctuating the compositions of the alloys after deposition. Similar effects in TiNiCu alloys are reported when developed by other powder metallurgical
Table 3 EDS results of TiNiCu samples developed by LAM. Integrands
TiNiCu10
TiNiCu20
TiNiCu30
Ti Ni Cu
58.27 ± 2 % 38.65 ± 3 % 3.08 ± 2 %
57.56 ± 4 % 28.24 ± 3 % 14.2 ± 1 %
55.28 ± 2 % 23.98 ± 2 % 20.74 ± 3 %
methods as well [35]. To have a preview about the nature of the samples more deeply, microstructure of the samples were examined. In Fig. 12, the micro structures of all three samples are shown. The standard metallographic
Fig. 11. SEM images showing the surface morphology of the formed TiNiCu samples. 104
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Fig. 12. Microstructure images of (a) TiNiCu10, (b) TiNiCu20 and (c) TiNiCu30 samples.
precipitates, like Ti(NiCu3) which prevents the formation of the interface region between two or three grain boundaries nearby. Hence the grains are flocking together in large size without much interface region between them. The stored energy due to asperity deformation during deposition is expected to be the reason behind the absence of interface region. Due to the above mentioned reasons it is very obvious that the surface roughness of TiNiCu20 sample is to be higher than TiNiCu10. In case of TiNiCu30 the needle like structures are observed in the microstructure which clearly indicates the surface roughness to be higher than the remaining two samples. Hence the surface roughness values of TiNiCu30 sample is just above the value of TiNiCu20. The obtained results can also be compared with the surface roughness values in nanometric level obtained from atomic force microscopy (AFM) presented in the forthcoming session. The Fig. 13 shows the results of atomic force microscopy (AFM). The Fig. 13(a), 13(b), 13(c) represents TiNiCu10, TiNiCu20, and TiNiCu30 respectively. The magnifications of TiNiCu10 were 1 μm2 and TiNiCu20, TiNiCu30 were 500 nm2. Similar to the SEM results the dents in TiNiCu30 is clearly visible in Fig. 13(c). The grain size detected from AFM of TiNiCu10, TiNiCu20 and TiNiCu30 were found to be 20.12 nm, 33.57 nm and 25.51 nm respectively. TiNiCu10 has lots of wax like structures on the surface and the projection of hillock structures are very less compared to the other two samples. TiNiCu20 has lots of hillock and valley structures on the surface. The measured grain size shows TiNiCu20 to be having the maximum grain size among the three samples. TiNiCu30 has semi waxed structures with bigger islands are in the form of clustered hillock structures with pointed round tip. The SEM, micro structure and AFM results are in close alignment with the surface roughness value obtained using the surface profilometer. The surface roughness of TiNiCu10 is 3.97 nm, TiNiCu20 is 4.25 nm and TiNiCu30 is 5.83 nm. From the surface morphology analyses TiNiCu10 is expected to have good shape memory properties. The analyses of
procedures were followed to obtain the microstructures. The etchant used was HF, HNO3 and H2O in the ratio 1:12:4 [36]. The most common observation in all the three images are the closely packed structures without any porosity holes or cracks. TiNiCu10 as shown in Fig. 12 (a) has microstructures formed with good grain boundaries. A small precipitate like structure is visible in the microstructure. This may be the small gap generated due to the removal of certain particles during micro polishing. The grain boundaries are large in size indicating possibilities of high ductility in the sample. In Fig. 12 (b) the grains of TiNiCu20 are shown and it is very clear that the formation of grain boundary is hindered by the presence of random dentrical structures. The reason may be attributed to the non-availability of enough time to form the grain structure due to quick cooling effect or precipitate formation. Also precipitates, like TiCu generally have the tendency to generate grain boundary fracture that induces brittleness in the sample [37]. Presence of needle like structures indicate the martensite phase domination in TiNiCu30 as shown in Fig. 12 (c). As the martensite phase is high a good phase transformation ability can be expected in the sample. The surface roughness was measured in the as deposited state using a surface profilometer. The surface roughness of TiNiCu10, TiNiCu20 and TiNiCu30 are 5.64 μm, 6.32 μm and 6.46 μm respectively. The surface roughness results obtained, closely align with the SEM results. The TiNiCu30 had the maximum surface roughness value as expected from the SEM results. The TiNiCu10 has the minimum surface roughness value among the three samples. From the microstructure diagram it is clear that the grain boundaries of TiNiCu10 sample are very much closely packed. This may be attributed to the grain boundary migration homogeneously taking place during the diffusion bonding of the alloys at the time of deposition. This leads to the formation of smooth surface on the sample. But the grain boundaries are not clearly visible for TiNiCu20 sample and this may be attributed to the formation of 105
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Fig. 13. AFM images of (a) TiNiCu10 (b) TiNiCu20 (c) TiNiCu30.
strength and the young’s modulus values of the sample are as quoted in Table 4. The curve of TiNiCu20 reaches the martensite plateau level with some initial elasticity in the sample. But the elastic deformation is not visible ahead of the detwinning point. The Young’s modulus value is also lesser than the TiNiCu10 sample. Though the graph of TiNiCu30 is quite similar to TiNiCu20, the brittle nature is confirmed from the very low elastic modulus. From Table 4 it is very clear TiNiCu30 has the least ultimate strength and young’s modulus values. As discussed earlier the role of precipitates or excessive slip happening in the sample is expected to be the reason. The growth of the curve itself reveals the brittle nature of the sample. The compression test results closely align with micro-hardness and surface morphological results.
mechanical properties is much essential in the applications point of view. Hence, in the session ahead the results of the mechanical properties are presented.
4.3. Mechanical properties The cold mounted samples were finely polished for micro-hardness test as per the metallographic standards. A load of 5 N was intended and a distance of 25 μm was maintained between two indentations. Ten readings were taken for each sample, expecting precision in the results. As shown in Fig. 14 the micro-hardness values of TiNiCu10, TiNiCu20 and TiNiCu30 are 242 +15 VHN, 278 + 123 VHN and 297 + 40 VHN respectively. The micro-hardness generally increases with the percentage of Cu in the TiNiCu alloys. The results closely align with the observation reported by researchers in the past [38]. The standard deviation of TiNiCu20 sample is higher than the remaining two samples, due to the formation of precipitates that reduce the strength of the sample. In case of TiNiCu30 the maximum percentage of Cu aggravates the formation of precipitates, like TiCu leading to the increase of dislocation density that sequentially increases the micro-hardness of the sample. As the micro-hardness is maximum for TiNiCu30, the sample is expected to be brittle in nature. The TiNiCu10 sample has the minimum micro-hardness value among the three and is expected to be ductile in nature, as the observation closely aligns with the surface morphological results as well. The Fig. 15 shows the compression test results of the TiNiCu samples for comparison. The stress strain curve of TiNiCu10 resembles the typical pattern of binary NiTi alloys. The elastic behavior of the sample is vividly seen initially, as the curve elongates till the martensite plateau. The yield point after the plateau indicates the detwinning process in the sample. The elastic deformation of the sample prolongs the curve for a while, reaching the ultimate strength of the sample. The ultimate
4.4. Crystalline nature The Fig. 16, presents the XRD results of all the three TiNiCu samples undertaken for investigation. The presence of two phases austenite and martensite are vivid in the results of all samples,. The results of TiNiCu10 reveal small martensite peaks popping up very close to each other between 2θ values 41°–43°. The B2 austenite peak is exactly present at 2θ = 45°7′ and 2θ = 53°4′. For TiNiCu20 samples the presence of intermediate martensite phase in orthorhombic structure (B19) is observed at 2θ = 40°26′ just before the austenite B2 peak. The internal strain caused during the deposition process reduces the stability of B19 martensite phase [39]. The strain generates the driving force to transform B19 to B19′ in the reverse cycle. The B2 phase peak is at 2θ = 43°08′ In the current work the generated strain forces decreased the d spacing in the lattice structure that leads to peak shift towards lesser 2θ value. Also the presence of two step martensite evolution in TiNiCu20 may lead to lots of martensite phases in the sample, at room temperature. The martensite B19′ peaks are positioned in 2θ = 39°57′ and 2θ = 62°64′. The TiNiCu30 has only two visible peaks. One B19 austenite peak and B19′ martensite at 44°8′ and 64°27′ respectively. Crystallite size can be calculated using the Scherrer formula:
d=
0.9λ BCosθ
(14)
Where’d’ stands for crystallite size, λ stands for the wavelength of the X-Radiation used, B is the peak width at half the intensity, and θ is the Bragg angle [40]. Using the Scherrer formula, the calculated grain size of TiNiCu10, TiNiCu20 and TiNiCu30 are 19.56 nm, 32.73 nm and 24.62 nm, respectively. These values are almost very close to the grain sizes obtained through AFM as reported earlier. 4.5. Phase transformation properties The Fig. 17 shows the DSC curves of TiNiCu10, TiNiCu20 and TiNiCu30 respectively. For all three samples visible peaks are obtained in both heating and cooling curves. The presence of TiCu is expected to be the cause of very small peak formation in TiNiCu30. The phase transformation temperatures of all the three samples martensite start (Ms),
Fig. 14. Micro-hardness results of TiNiCu10, TiNiCu20 and TiNiCu30. 106
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Fig. 15. Compression test results of TiNiCu10, TiNiCu20 and TiNiCu30.
properties in the lattice structure leading to an alteration in the transformation sequence. Also the formation of phases like Ti2(NiCu) prevents the evolution of B19 peak by reducing the amount of heat in the sample. The absence of TiCu phases in the alloy induces very low Ms temperature in TiNiCu10 and TiNiCu20 samples as mentioned in the XRD results previously. From Fig. 17(c) the presence of TiCu precipitate in TiNiCu30 sample is very much evident, as the Ms temperature is very high. The peak width of TiNiCu10 and TiNiCu20 is narrow in both heating and cooling cycles, whereas very wide peaks are obtained for TiNiCu30 due to dislocations caused by rapid solidification and inbuilt stress during fabrication [41].
Table 4 Compression test results of TiNiCu samples developed by LAM. Sample
Ultimate Strength MPa
Elastic Modulus GPa
TiNiCu10 TiNiCu20 TiNiCu30
430 ± 7 402 ± 12 386 ± 9
24 ± 2 21 ± 4 20 ± 3
5. Conclusion In present study, an indigenously developed LAM system was deployed successfully for fabricating ternary shape memory alloy structures of different compositions. The study investigated the pattern of depositing TiNiCu structures ensuring reduced accumulation of residual stress and homogeneous temperature distribution during the fabrication process through numerical simulation and experimental work. Both simulation and experimental studies were at par and confirmed that the deposition pattern in unilateral parallel direction yields the best results. Various examination of different composition of TiNiCu alloy under investigation (Ti50Ni (50−x) Cux (x = 10, 20 and 30)) revealed that TiNiCu30 had the highest surface roughness and highest hardness, while the large grain boundaries are observed for TiNiCu10 sample indicating good ductility. The elastic modulus was also highest for TiNiCu10 samples. Moreover, two step phase transformation similar to conventionally produced samples was observed in TiNiCu20 samples. But the formation of TiCu precipitates affected the strength of the sample and inducing more brittleness in TiNiCu20. During DSC examination, all the samples exhibited visible phase transformation, however, only TiNiCu10 are the best choice for real time on account of high brittleness, which can further be improved by post processing techniques, like - annealing or laser surface processing. Further, the obtained simulation and experimental results can be used a precursor for microstructure engineering investigation to improve TiNiCu20 and TiNiCu30 samples. Declaration of Competing Interest Fig. 16. XRD results of TiNiCu10, TiNiCu20 and TiNiCu30 samples.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
martensite finish (Mf), austenite start (As) and austenite finish (Af) temperatures are as quoted in Table 5. From the results in Table 5 it is very obvious that the phase transformation temperatures are completely different for all three samples. This may be attributed to the compositional effects and the sub sequential secondary phase formation in the samples. The DSC result of TiNiCu20 sample does not reveal the martensite phase transformation in two steps as only one peak is visible. The absence of second peak in TiNiCu20 may be attributed to the internal strain detracting the elastic
Acknowledgement The authors would like to thank the Sophisticated Instrument Centre (SIC) in Indian Institute of Technology Indore for providing the necessary characterization facilities. Thanks are also due to the members of LAM laboratory, RRCAT, Indore for the technical help provided to develop the samples. 107
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Fig. 17. DSC results of TiNiCu10, TiNiCu20 and TiNiCu30 samples.
Table 5 Phase transformation temperatures of TiNiCu samples obtained from DSC graphs. Sample
Martensite Start Temperature (Ms)
Martensite Finish Temperature (Mf)
Austenite Start Temperature (As)
Austenite Finish Temperature (Af)
TiNiCu10 TiNiCu20 TiNiCu30
35.88 °C 59.91 °C 63.12 °C
35.14 °C 59.05 °C 62.21 °C
38.24 °C 64.26 °C 64.57 °C
39.03 °C 65.95 °C 65.94 °C
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