Over the last decade, metal additive manufacturing (AM) has developed rapidly to become a revolutionary technology for the production of various components for several industries such as biomedical, aerospace, automotive, and marine, as stated by Herderick [1
]. Namatollahi et al. [2
] stated that “AM is the process of joining metallic powders layer by layer to produce complex three-dimensional parts”. AM has several advantageous over conventional manufacturing including lower density, reduction in the scrap rate, ability to create complex nearly net-shape parts, elimination of multi-step manufacturing, and many more as expressed by Camacho et al. [3
]. In contrast, there are still some limitations that hinder the applicability of AM such as a steep temperature gradient, high thermal stress, and tensile residual stress (RS) as reported by Ngo et al. [4
], anisotropy and heterogeneity in microstructure as explained in the work of Ji et al. [5
] and mechanical properties as explained in the work of Tabei et al. [6
Bartlett et al. [7
] described that steep temperature gradient induced by high laser power and low conduction may cause high thermal stress in the component. Since the yield strength of the material depends on temperature, and at elevated temperatures around the melting point, materials are usually soft, they can easily undergo plastic deformation. Consequently, due to the repeated heating and cooling, materials experience a high magnitude of RS. Residual stress is inherent in all the manufactured components, and high RS is reported by many researchers. Roehling et al. [8
] reported high tensile RS in parts built via laser powder bed fusion (LPBF) systems. Wang et al. [9
] measured the RS using neutron diffraction; they observed high tensile RS in IN625 parts. An et al. [10
] reported high RS in curved thin-walled structure manufactured via LPBF. Denlinger and Heigel [11
] simulated the RS during the additively manufactured Ti-6Al-4V samples, they stated that the predicted RS was considerably high. Zhao et al. [12
] predicted high RS in titanium alloys built via direct metal laser sintering (DMLS) using FEM. Romano et al. [13
] reported that RS has a crucial impact on fatigue life of the component, corrosion resistance, crack initiation and growth, and also microstructure and mechanical properties of the materials.
There is a considerable amount of research literature explaining different methods for the prediction of RS including experimentation, numerical modeling, and analytical modeling.
Experimental procedures to measure the RS in the components can be categorized into destructive and non-destructive methods. The non-destructive methods can be classified into X-ray diffraction, which is capable of near surface RS measurements, and neutron diffraction, which is capable of volumetric measurements. Other non-destructive methods include ultrasonics, as explained by Noronha and Wert [14
], electrical resistivity as explained by Chung [15
], magnetic behavior as explained by Krause et al. [16
] and piezo-spectroscopy in thin films as explained by Ager III and Drory [17
]; these are material- and geometry-specific. Destructive methods such as hole drilling, sectioning, crack compliance, digital image correlation, and electronic speckle pattern interferometry (ESPI) essentially create a free surface in the part and correlate resultant deformation to RS, as stated by Prime [18
Numerical modeling is another approach for the forecast of RS. Aggarangsi and Beuth [19
] used a finite element method (FEM) to simulate the RS. In their modeling, they defined a temperature gradient from the melt pool and correlated it to the maximum RS. They showed that localized preheating could reduce the maximum RS in the additively manufactured 304 stainless steel. Panda and Sahoo [20
] used FEM to predict the RS in the direct metal deposition (DMD) of AlSi10Mg. They predicted the temperature distribution using the transient temperature and they coupled the results to a structural model to predict the RS. In their modeling, the thermal and mechanical material properties were considered constant. Also, the effects of scan strategies were not considered in this modeling. Chen et al. [21
] used FEM to simulate the RS in additively manufactured parts using the inherent strain method. They utilized the temperature results obtained from thermocouples to calibrate the process parameters. Then, they used Goldak’s heat source (HS) model to obtain the thermal gradients and the inherent strains. Ganeriwala et al. [22
] also used FEM to simulate the stress state in laser powder bed fusion of Ti-6Al-4V. They used the lumping approach to speed up the computations. They indicated that the stress is higher near the boundaries for the island scan strategies. Ding and Shin [23
] proposed a 3D thermo-elastoplastic finite element model to simulate the RS. They validated the results using a neutron diffraction strain scanner.
Physics-based closed-form analytical solutions are the other approach for the prediction of RS. Prediction of RS should be started by modeling of the temperature field induced by laser in AM. There are various analytical methods to model the temperature field in metal AM. Mirkoohi et al. [24
] introduced five distinct HS models to analytically simulate the three-dimensional temperature profile. They investigated the viability of all the models under various process conditions. They considered the material properties temperature-dependent; phase change was also considered through modification of specific heat. Furthermore, the effect of scan strategy was also considered in their modeling. Mirkoohi et al. [26
] proposed a model to analytically simulate the temperature field considering the effect of layer thickness. Fergani et al. [27
] presented a model for the prediction of stress. In their modeling, properties were considered constant. Also, the effect of scan strategy was not considered in their modeling. Moreover, the stress in the build direction was considered to be elastic.
Experimentation is a different approach which facilitates the perception of RS in metal AM. However, measuring the stress state of the entire part is challenging, time-consuming, and expensive. Physics-based numerical models are an assuring approach for the calculation of RS when the results are compared to experiments. However, the simulation of the entire process considering the multi-physics aspects of metal AM cannot be achieved in a reasonable amount of time. On the other hand, physics-based closed-form analytical solutions validated by physical experiments enable prediction of the stress state within the additively manufactured part much faster than FEM and experimentation. Furthermore, the multi-physics aspect of metal AM such as the multi-layer and multi-scan aspect of AM—which is challenging to consider by any other methods—can be considered in the process modeling of AM using closed-form analytical solutions. This accurate and reliable model can be adopted to optimize the AM parameters and to guide the build process in achieving the desired part quality. Consequently, having one model to accurately and rapidly predict the stress state within the part is extremely valuable.
The physics governing AM is considerably complicated, thus, capturing the entire physical phenomena involved in this process currently is not possible. In this work, a physics-based closed-form analytical model is proposed to predict the RS within the additively manufactured part. Herein, the proposed thermomechanical model is capable of the prediction of RS accurately in a few seconds. Prediction of RS cannot be achieved within this amount of time by any numerical methods or experimentation; this shows the computational efficiency of the proposed model. In this work, first, a transient moving point HS is employed to analytically simulate the in-process temperature field within the components. Second, temperature gradient along the scan direction (x-axis) and build direction (z-axis) is calculated from the temperature field. Third, thermal stress is obtained by combining three different sources of stress known as stresses due to body forces, normal tension, and hydrostatic stress. Lastly, the RS distributions along the build direction and also along the scan direction are found from incremental plasticity and kinematic hardening behavior of the metal based upon the premises of a plane strain condition in the build of isotropic and homogeneous properties. Moreover, the RS is calculated according to the conservation of volume in plastic deformation, in coupling with the equilibrium and compatibility conditions. In this modeling, the thermal and mechanical material properties are temperature-sensitive, since the steep temperature gradient causes the material properties to vary substantially. Moreover, due to the cyclic melting and solidification, the build part experiences phase change. The heat capacity is modified to consider the effect of latent heat of melting during this process. Furthermore, the impacts of multi-layer and multi-scan aspects of metal AM are analyzed by including the effect of the temperature history on the former layers and scans.
X-ray diffraction experimental measurements are conducted to validate the proposed analytical model. The Ti-6Al-4V samples are built using the direct metal deposition (DMD) process under different process condition. Good agreement is obtained between the measured and predicted RS.
5. Results and Discussion
illustrates the melt pool area and the heat-affected zone induced by highly localized heat input and low thermal conductivity. Non-uniform heating induces non-uniform thermal expansion. During the heating cycle, the melt pool area and the heat-affected zone are trying to expand; however, they are surrounded by solidified metal. This would generate the compressive stress state within the melt pool and heat-affected zones as shown in Figure 6
a. During the cooling cycle, the shrinkage of the material would develop a tensile stress state as illustrated in Figure 6
The proposed analytical model is extremely valuable since it provides a fast (less than 45 s with a 4-processor laptop) and accurate prediction of the stress state within the build. In this modeling, first, a transient moving heat source approach is used to predict the temperature field in AM. Second, the thermal stress induced is calculated by combining three stresses known as stresses due to body forces, normal tension, and hydrostatic stress. Lastly, the stresses may exceed the yield point and the material would experience plastic deformation. As a consequence of repeated loading and unloading (heating and cooling), the material experiences high RS. Therefore, both the in plane and out of plane RS distributions are calculated from the incremental plasticity and kinematic hardening behavior of the metal, based upon the premises of the plane strain condition in the build of isotropic and homogeneous properties, in coupling with the equilibrium and compatibility conditions.
Material properties could vary significantly from point to point due to the steep temperature gradient. Consequently, it is not a fair assumption to consider the material properties constant. Herein, the thermal and mechanical material properties are temperature sensitive. Moreover, in this process, the material experiences cyclic melting and solidification. The energy needed for the phase change is taken into account by incorporating the latent heat of fusion into the specific heat. Furthermore, the multi-layer and multi-scan aspect of metal AM are considered by including the temperature histories from the previous layers and scans.
Experimental measurements are conducted via X-ray diffraction to measure the RS in Ti-6Al-4V specimens built via the DMD process at the middle of the specimens (X = 10 mm, Y = 1.5 mm) along the build direction (z-axis), every 0.5 mm. The scan strategy in both experimentation and analytical modeling is bi-directional. Moreover, the hatching space and layer thickness are 105 and 250 respectively. Comparison of the results from the proposed analytical model and the experimentation of the Ti-6Al-4V specimens built via the DMD process showed good qualitative and quantitative agreement.
illustrates the predicted temperature field for the Ti-6Al-4V specimens. Figure 7
a demonstrates the predicted melt pool area and the heat-affected zone (HAZ) for the first specimen in Table 3
, which has a laser power of 206 W and a scan speed of 25 mm/s, with a layer height and hatch spacing of 250 and 105
, respectively. It should be noted that the absorption ratio is 30% for Ti-6Al-4V samples as explained in the work of Selvan et al. [48
]. Since the evaporation of the metallic powders is not considered in the modeling, the maximum temperature does not go beyond the evaporation temperature, which is around 3000 °C for Ti-6Al-4V, as reported by Selvan et al. [48
]. In this figure, the melt pool depth is around 0.1 mm, based on the melting point of 1600 °C, and the depth of the heat-affected zone is around 0.15 mm. Below this depth, the material is not affected by the laser. The rapid temperature change at the border of HAZ and the completely solid material—which is not affected by the laser—is the region where the stress state within the build part changes from tensile to compressive. This phenomenon will be explained in detail in the following sections of this manuscript. Figure 7
b illustrates the predicted temperature field for the second specimen, with the laser power of 385 W, scan speed of 40 mm/s, layer height of 250
and hatch spacing of 105
. Based on the melting point of Ti-6Al-4V (1600 °C), the melt pool depth is 0.14 mm and the HAZ depth is around 0.21 mm. The proposed temperature model is validated in the previous work of Mirkoohi et al. [24
As explained before. material properties vary significantly since the temperature gradient is quite high in AM. The variation of thermal and mechanical material properties for the second specimen with the laser power of 385 W and scan speed of 40 mm/s is illustrated in Figure 8
. The density of the solid material is around 4400
. As the temperature increases the density decreases to 4050
in the liquid zone; in the melt pool zone, thermal conductivity reaches its maximum value due to the high magnitude of temperatures, and decreases to around 5
in the solid zone; the specific heat has the highest magnitude of 530
at the melt pool area and drops to 480
when the material is solidified; thermal expansion is more expanded in the liquid zone compared to other properties, which shows that the thermal expansion is more sensitive to temperature, and it has the highest magnitude in the liquid zone; the elastic modulus in the liquid zone is almost zero, in the HAZ reaches 60 GPa, and in the solid zone it reaches the maximum value of 120 GPa; Poisson’s ratio in the liquid zone has the magnitude of 0.38, and in the solid zone has the value of 0.29; yield strength reaches almost zero in the liquid zone and has the maximum value of 1200 MPa in the solid zone.
Residual stress along the scan direction and transverse direction is predicted using the proposed model and validated experimentally. Figure 9
illustrates the predicted RS along the scan direction and transverse direction as a function of depth into the build part for the first sample with the laser power of 206 W, scan speed of 25 mm/s, layer height of 250
, and hatch spacing of 105
. Since the samples have a rough surface, the top surface of the samples is polished up to 100
with an electromechanical polishing to be able to accurately measure the RS on the surface.
Both the in plane and out of plane residual stresses are highly tensile in the melt pool zone and the heat-affected zone in coherence with most of the reported results in the literature as explained in the introduction section. As shown in Figure 9
and Figure 10
, in both the scan direction and the transverse direction, there is a gradual change in the stress state from tensile to compressive. This change occurs at around the depth where the medium is solidified and is not affected by the laser. The results confirm that upon cooling the melt pool and the heat-affected zones are under tension due to the shrinkage of the material, and when the material is completely solidified and is not affected by the laser anymore, the material experiences a compression stress state. The oscillations in the melt pool and the heat-affected zone are due to the fact that the thermal and mechanical material properties vary significantly in these regions. Therefore, an abrupt change in one of the material properties would result in oscillations. This could be better improved by having more material property data points at different temperatures.
shows the predicted RS in the scan and transverse directions for the second sample in Table 2
with the laser power of 385 W, scan speed of 25 mm/s, and layer height and hatch spacing of 250 and 105
, respectively. The stress state is tensile in both the scan and transverse directions and changes to compressive at the depth of around 0.9 mm. Around this region the material is completely solidified and is not affected by the laser. Comparison of the predicted and measured RS shows that they follow each other closely.
A physics-based analytical model is proposed to rapidly and accurately calculate the stress state within the additively manufactured part. As the explicit computation is executed without meshing or iteration, it can be completed at a speed several orders of magnitude higher than that of conventional FEMs. The high computational efficiency of the proposed model has the potential to affect a wide range of applications, as it could become a powerful tool for design and also fatigue assessment of components undergoing cyclic loading. It also enables efficient control and optimization of the process parameters, to achieve a high-quality part.
In this modeling, a transient moving point heat source is employed to predict the in-process temperature field within the built part. Thermal stress induced by steep temperature gradient is then obtained using the Green’s functions of stresses due to the point body load in a homogeneous semi-infinite medium. Lastly, the stress state is obtained by importing the thermal history as an input for residual stress prediction using the modified McDowell algorithm, in which both the in plane and out of plane residual stress distributions are obtained from the incremental plasticity and kinematic hardening behavior of the metal, coupled with the equilibrium and compatibility conditions. In this modeling, the relationship between three normal stresses are considered according to the property of volume invariance in plastic deformation.
Due to the fast irradiation of the laser and quite low thermal conductivity of the material, the material experiences a high temperature gradient. The high temperature gradient induced variation of the material properties in the medium. Consequently, the material properties are considered temperature sensitive. Moreover, the energy needed for the solid-state phase change is considered by modifying the heat capacity using the latent heat of fusion. Furthermore, the effect of the layer thickness, the hatching space and the scan strategy are considered in this modeling by incorporating the temperature history from the previous scans.
In this work, first the temperature field is predicted for the Ti-6Al-4V specimens. Distribution of thermal and mechanical material properties including density, specific heat, thermal conductivity, elastic modulus, coefficient of thermal expansion, yield strength, and Poisson’s ratio for the laser power of 385 W and scan speed of 40 mm/s showed that the high temperature gradient in the AM process notably changes the material properties within the medium.
The predicted temperature fields for both samples showed that in the vicinity of the melt pool area the temperature and the magnitude of the temperature gradient is high enough to cause the thermal stress to exceed the yield strength of the material. Upon cyclic loading and unloading of the material during the heating and cooling cycles, the material experience high residual stress. The predicted residual stress showed that the stress state is highly tensile in the melt pool and the heat-affected zones, and compressive in the solidified regions. Good qualitative and quantitative agreement is achieved between the predicted and measured residual stresses for the Ti-6Al-4V samples.