Method for predicting residual stresses and deformations of laser directed energy deposited ta15 alloy components

Abstract

The present application relates to the technical field of laser manufacturing and intelligent manufacturing, and particularly relates to a residual stress and deformation prediction method for laser directed energy deposition (LDED) of TA15 alloy components, and a thermal-metallurgical-mechanical multi-physical field coupling model is constructed; firstly, a transient temperature field model during the laser directed energy deposition process is established based on the Fourier heat conduction theory, which comprehensively considers the effects of heat conduction, convection and radiation; then a metallurgical model is established based on the phase transition principle; finally, a multi-stage phase transition dynamics model is established, which considers both diffusion-controlled and non-diffusion-controlled transition mechanisms; by introducing the phase transition dynamics model into the sequentially coupled thermal-mechanical framework, the multi-scale integrated prediction from microstructure evolution to macroscopic stress distribution is realized; and the present application provides a theoretical basis for microstructure regulation and residual stress optimization of the TA15 alloy LDED process.

Description

Technical Field

[0001] This invention relates to the fields of laser manufacturing and intelligent manufacturing technology, and in particular to a method for predicting residual stress and deformation of TA15 alloy components by laser-directed energy deposition. Background Technology

[0002] TA15 titanium alloy (Ti-6.5Al-2Zr-Mo-V), a typical near-alpha titanium alloy, has been widely used in key hot-end components such as compressor blades and casings of aero-engines due to its excellent specific strength, high-temperature creep resistance, and fatigue performance. However, traditional manufacturing processes suffer from low material utilization and long production cycles, hindering the efficient fabrication and application of TA15 structural components. Laser-directed energy deposition (LDED), an advanced metal additive manufacturing method, achieves near-net-shape forming of components by layer-by-layer cladding of metal powder with a high-energy laser beam. With its high deposition rate, large-size manufacturing capability, and in-situ repair potential, it has become an important technological choice for manufacturing complex components.

[0003] During laser-directed energy deposition (LDED) of TA15 titanium alloy, the rapid non-equilibrium solidification process generates a severe temperature gradient and extremely high cooling rate, leading to complex non-equilibrium thermal cycling of the material. This unique thermal history promotes the formation of spatially heterogeneous gradient structures at the microscale and induces uneven thermal expansion and contraction at the macroscale, resulting in residual stress. The presence of residual stress can lead to defects such as component deformation and interlaminar cracking, severely affecting dimensional accuracy and performance. Studying the formation mechanism of residual stress during LDED is of significant theoretical guidance for process control and performance optimization. Currently, research on phase transformation prediction and residual stress formation mechanisms during TA15 alloy LDED is relatively scarce, making it difficult to accurately predict and control the formation and evolution of residual stress. Summary of the Invention

[0004] The purpose of this invention is to provide a method for predicting residual stress and deformation of TA15 alloy components by laser-directed energy deposition (LDED). This method can predict the temperature field evolution, microstructure transformation and residual stress distribution during the LDED process, thereby providing a theoretical basis for the microstructure control and residual stress optimization of TA15 alloy LDED process.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for predicting residual stress and deformation of TA15 alloy components by laser-directed energy deposition includes the following steps:

[0007] Step 1: Establish a transient temperature field model for laser directional energy deposition that comprehensively considers heat conduction, convection, and radiation based on Fourier heat conduction theory;

[0008] Input ambient temperature The thermal properties and laser process parameters of TA15 alloy are used to predict the thermal history data of different times and spaces during the LDED process based on the transient temperature field model.

[0009] The transient temperature field model ;

[0010] in, The density of TA15 alloy, For constant pressure specific heat, Thermal conductivity, For historical thermal data, As a laser volumetric heat source, For heat loss, The convective heat transfer coefficient, For emission rate, For ambient temperature, Boltzmann's constant;

[0011] Step 2: Establish a metallurgical model based on the phase transition principle, input the thermal history data predicted by the transient temperature field model, and predict the volume fraction of solid-state phase transition at different times and in different spaces during the LDED process based on the metallurgical model.

[0012] Based on the solid-state phase transition effect during the LDED process, the metallurgical model includes the following four models:

[0013] α-phase formation model:

[0014] ,

[0015] in, Let be the volume fraction at time n+1 of phase α. Let be the volume fraction of phase α at time n. Let be the volume fraction of phase β at time n. For time step, and The coefficients for the β→α phase transition at time n are given. Let n be the equilibrium time of the β→α phase. The equilibrium volume fraction of phase α at time n+1;

[0016] Martensite phase formation model:

[0017] ;

[0018] in, Let be the volume fraction of martensite at time n+1. denoted as , where is the volume fraction of martensite at time n; b is the martensitic transformation kinetic coefficient. T represents the temperature at which martensite begins to transform; T represents thermal history data. Let be the volume fraction of phase β at time n. Let be the equilibrium volume fraction of phase β at time n+1. Cooling rate;

[0019] Martensitic phase dissolution model:

[0020] ;

[0021] in, Let be the volume fraction of martensite at time n+1. Let be the volume fraction of martensite at time n; T represents the thermal history data. Let be the volume fraction of phase β at time n. For time step, and Let be the martensitic phase transformation kinetic coefficient at time n. Let n be the martensitic phase equilibrium time at time n; The equilibrium volume fraction of the martensitic phase at time n+1;

[0022] α-phase dissolution model:

[0023] ;

[0024] in, Let be the volume fraction of phase β at time n+1. and For αβ phase transition kinetic coefficients, For time step, The equilibrium time of phase β at time n+1;

[0025] Step 3: Describe the strain evolution in an incremental manner and construct a mechanical model; input the thermal history data predicted by the transient temperature field model and the volume fraction of solid-state phase transition predicted by the metallurgical model, and predict the strain distribution at different times and in different spaces during the LDED process based on the mechanical model;

[0026] The mechanical model ;in, This represents the total strain increment. For elastic strain increment, For the increment of plastic strain, For thermal strain increment, This represents the volumetric strain increment during phase transition. This represents the increment of plastic strain induced by phase transformation;

[0027] Thermal strain increment , The coefficient of thermal expansion is This is an increment of historical thermal data;

[0028] Phase transformation volumetric strain increment , For the volume fraction increment of the phase, The rate of volume change caused by the phase transition; The Kronecker function;

[0029] Phase transformation induced plastic strain increment , These are phase transformation plasticity parameters; and These represent the volume fraction of martensite phase and its increment, respectively. It is the deviatoric stress tensor.

[0030] Furthermore, the laser volumetric heat source The Goldak double ellipsoidal heat source model was adopted;

[0031] ,

[0032] in, , , and The shape parameters of the double ellipsoidal heat source; and Divided into energy distribution parameters; It is the laser power; It is the energy absorption rate.

[0033] Furthermore, the constant-pressure specific heat of TA15 alloy thermal conductivity The temperature is obtained in real time through a linear interpolation algorithm between adjacent data points under the temperature function relationship.

[0034] Furthermore, the phase transition kinetic coefficient and Obtained through isothermal transition TTT curves;

[0035] Equilibrium volume fraction of α phase A and c are the fitting coefficients, and T is the thermal history data. This is the β transition temperature.

[0036] Furthermore, the martensitic transformation kinetic coefficient b and the martensitic transformation initiation temperature... It was obtained through thermal expansion experiments.

[0037] Furthermore, the martensitic phase transformation kinetic coefficient at time n... , and the equilibrium volume fraction of martensite at time n+1. The degree of martensite dissolution at different temperatures is determined by hardness measurement methods.

[0038] , and represents the fitting coefficient.

[0039] Furthermore, the αβ phase transition kinetic coefficient It is a constant that does not change with temperature;

[0040] , As the pre-exponential factor, For activation energy, This is the universal gas constant; This represents the hot historical data at time n+1.

[0041] Furthermore, based on the ABAQUS platform, the transient temperature field model, metallurgical model, and mechanical model are constructed into a thermo-metallurgical-mechanical multiphysics coupled numerical model; the thermo-metallurgical-mechanical multiphysics coupled numerical model can perform integrated and coordinated prediction of temperature field evolution, microstructure transformation, and residual stress distribution.

[0042] The beneficial effects of this invention are as follows:

[0043] (1) Deformation prediction: The maximum overall deformation was 1.581 mm, which was only -1.0% relative to the experimentally measured 1.597 mm. This was significantly better than the thermo-mechanical coupling model that did not consider solid-state phase change (relative error +21.7%).

[0044] (2) Residual stress prediction: On the top surface of the thin-walled part, the predicted residual stress distribution is basically consistent with the XRD measurement results, showing an approximately symmetrical distribution in which the compressive stress at both ends is higher than that in the central region. The stress distribution trend along the typical tensile and compressive regions also shows good consistency with the results of the profile method measurement.

[0045] (3) Microstructure prediction: The microstructure zoning characteristics caused by the spatiotemporal heterogeneity of thermal history were predicted—martensite was formed in the high-cooling-rate zone in the lower part, while the middle and upper parts were dominated by diffusion-type α phase due to the influence of thermal accumulation.

[0046] (4) The accuracy and effectiveness of the prediction of thermal history, phase transformation behavior and residual stress distribution confirm that considering the solid phase transformation effect can simultaneously reduce residual stress and deformation and improve the prediction accuracy of the model; thus providing a theoretical basis for the microstructure control and residual stress optimization of TA15 alloy LDED process. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the thermo-metallurgical-mechanical coupling model of the present invention.

[0048] Figure 2 This is a schematic diagram of the fitting of thermophysical parameters of TA15 titanium alloy.

[0049] Figure 3 (a) shows the TTT curve of TA15 titanium alloy. Figure 3 (b) is the equilibrium volume fraction curve of the α phase.

[0050] Figure 4 This is a schematic diagram comparing the fitted curve of the martensite phase volume fraction with the experimental results.

[0051] Figure 5 (a) shows the curves of hardness versus annealing time at different temperatures; Figure 5 (b) represents the degree of martensite dissolution at different annealing temperatures.

[0052] Figure 6 This is a schematic diagram showing the relationship between the equilibrium volume fraction of martensite and temperature.

[0053] Figure 7 This is a schematic diagram comparing the simulation results of α-phase dissolution with experimental results.

[0054] Figure 8 This is a schematic diagram of the finite element mesh for a thin-walled component.

[0055] Figure 9 This is a schematic diagram comparing the results of in-situ deformation experiments and simulations at the monitoring points.

[0056] Figure 10 (a) shows the results of residual stress testing at three representative locations on the top surface of a thin-walled component using X-ray diffraction. Figure 10 Figures (b) and (c) show the stress comparison curves for the two paths, respectively. Detailed Implementation

[0057] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0058] like Figure 1 As shown in this embodiment, a method for predicting residual stress and deformation of a laser-directed energy deposition (LADED) TA15 alloy component includes the following steps:

[0059] Step 1: Establish a transient temperature field model for laser-directed energy deposition that comprehensively considers heat conduction, convection, and radiation based on Fourier heat conduction theory; input ambient temperature. The thermal properties and laser process parameters of TA15 alloy are used to predict the thermal history data T at different times and in different spaces during the LDED process based on the transient temperature field model.

[0060] The transient temperature field model ;in, The density of TA15 alloy is 4.5 g / cm³. For constant pressure specific heat, Thermal conductivity, For historical thermal data, As a laser volumetric heat source, For heat loss, This refers to heat loss due to convection on the surface. This refers to the heat loss caused by radiation on the surface. The convective heat transfer coefficient, For emission rate, For ambient temperature, This is the Boltzmann constant, specifically 5.67 × 10⁻⁶. -8 W / m 2 / K 4 .

[0061] The laser volumetric heat source The Goldak double ellipsoidal heat source model is adopted, specifically:

[0062] ,in, , , and The shape parameters of the double ellipsoidal heat source; and Divided into energy distribution parameters; It is the laser power; It represents the energy absorption rate, and xyz represents the coordinates in a local coordinate system with the center of the moving laser spot as the origin, used to calculate the distance of this point from the center of the heat source.

[0063] Thermophysical properties of TA15 alloy (specific heat at constant pressure) thermal conductivity In the transient temperature field model, it is defined as a function of temperature; its functional relationship is as follows: Figure 2 As shown, the values ​​are determined by experimental measurement data. To ensure computational efficiency and accuracy, during the simulation, the instantaneous physical property parameters at any temperature are obtained in real time through a linear interpolation algorithm between adjacent data points.

[0064] Step 2: Establish a metallurgical model based on the phase transition principle, input the thermal history data predicted by the transient temperature field model, and predict the volume fraction of solid-state phase transition at different times and in different spaces during the LDED process based on the metallurgical model.

[0065] The solid-state phase transformation effect generated during LDED (heating and cooling) produces α phase, β phase, and martensitic phase (αm). Therefore, the metallurgical model includes four models:

[0066] (1) α-phase formation model (β→α); α-phase formation belongs to diffusion-type phase transition, and its volume fraction of α-phase is: , Let be the volume fraction at time n+1 of phase α. Let be the volume fraction of phase α at time n. Let be the volume fraction of phase β at time n. For time step, and The coefficients for the β→α phase transition at time n are given. Let n be the equilibrium time of the β→α phase. The equilibrium volume fraction of phase α at time n+1;

[0067] Phase transition kinetic coefficients and Obtained through isothermal transformation TTT curves, such as Figure 3 The TTT curve shown in (a) is used at high temperature. Data, and low temperature conditions are used data.

[0068] Equilibrium volume fraction of phase α at time n+1 A and c are the fitting coefficients, A = 0.96485, c = 0.00925, and T is the current temperature. The β-transformation temperature represents the equilibrium volume fraction of the α-phase at different temperatures, as shown below. Figure 3 As shown in (b).

[0069] (2) Martensite phase formation model (β→αm), the volume fraction of martensite phase is:

[0070] ;

[0071] in, Let be the volume fraction of martensite at time n+1. denoted as , where is the volume fraction of martensite at time n; b is the martensitic transformation kinetic coefficient. T represents the temperature at which martensite begins to transform; T represents thermal history data. Let be the volume fraction of phase β at time n, and be the equilibrium volume fraction of phase β at time n+1. , The value represents the cooling rate (°C / s).

[0072] b and The results were obtained through thermal expansion experiments conducted on a Gleeble-3500 thermal simulation testing machine. TA15 alloy samples were heated from room temperature to 1100°C at a rate of 10°C / s and held for 5 minutes; then, the samples were cooled to room temperature at cooling rates of 20°C / s, 30°C / s, and 100°C / s, respectively; finally, the experimental data on the degree of martensitic transformation were obtained using the lever method. Figure 4 As shown, the curve fitted by the KM equation agrees well with the experimental data; Table 1 shows the values ​​of b and φ at different cooling rates. The value of .

[0073] Table 1. b and T at different cooling rates MS value

[0074] Cooling rate <![CDATA[T MS (℃)]]> b 20 800 0.035 30 780 0.045 100 770 0.045

[0075] (3) Martensitic phase dissolution model (αm→α+β), the volume fraction of martensitic phase is:

[0076] ,

[0077] in, Let be the volume fraction of martensite at time n+1. Let T be the volume fraction of martensite at time n; T is the current temperature. Let be the volume fraction of phase β at time n. For time step, and Let be the martensitic phase transformation kinetic coefficient at time n. Let n be the martensitic phase equilibrium time at time n; The equilibrium volume fraction of martensite at time n+1;

[0078] To obtain the kinetic coefficient of martensite dissolution, the degree of martensite dissolution at different temperatures was determined by hardness measurement. This method was applied and validated in TC4 titanium alloy. Thirty samples were heat-treated in a tube furnace: first, they were heated to 1100°C at a rate of 10°C / s and held for 10 minutes, followed by water quenching to obtain a fully martensitic microstructure. Subsequently, annealing was performed at six different temperatures (600, 700, 750, 800, 850, and 900°C), with five different holding times at each temperature (10, 20, 30, 60, and 120 minutes). Finally, microhardness measurements were performed on all heat-treated samples under a load of 1000 gf and a dwell time of 15 seconds. To ensure the reliability of the data, five tests were performed on each sample and the average value was taken. The results are shown below. Figure 5 As shown in (a). Figure 5Figure (b) shows the relationship between the degree of martensite dissolution and annealing time at different temperatures. The degree of dissolution was modeled using the JMA equation, from which the kinetic parameters under each temperature condition were derived. and The equilibrium volume fraction is shown in Table 2.

[0079] Table 2 Fitting parameters for the degree of martensite dissolution at different annealing temperatures

[0080] Temperature (°C) Balanced volume fraction kinetic parameter K Dynamic parameter N 700 0.728 0.00287 1.66985 750 0.414 0.07328 0.74108 800 0.355 0.13168 0.79228 850 0 0.31939 0.47125 900 0 1.08760 0.25304

[0081] Equilibrium volume fraction of martensitic phase at time n+1 , and These are the fitting coefficients. , The comparison between the experimental values ​​and the fitted curve is as follows: Figure 6 As shown.

[0082] (4) α-phase dissolution model (α→β): When the volume fraction of α-phase is greater than the equilibrium volume fraction, the dissolution of α-phase generates β-phase, and the volume fraction of β-phase is:

[0083] , Let be the volume fraction of phase β at time n+1. and For αβ phase transition kinetic coefficients, For time step, Let be the equilibrium time of phase β at time n+1.

[0084] αβ phase transition kinetic coefficient It is a constant that does not change with temperature, and its value is 0.75; , This is the pre-exponential factor, with a value of 41.5. The activation energy is set to 333. This is the universal gas constant, with a value of 8.314. This represents the hot historical data at time n+1.

[0085] like Figure 7 As shown, the β phase generated by the dissolution of the α phase is in significant agreement with the experimental data across the entire temperature range, verifying the proposed dissolution mechanism.

[0086] After obtaining the thermal history data predicted by the transient temperature field model, this embodiment further calculates the volume fraction of solid-state phase transformation (martensitic phase transformation and diffusion-type phase transformation) based on the temperature thermal history data. Then, it introduces the TTT curve, CCT curve and phase transformation law of martensite dissolution experiment of TA15 titanium alloy, and realizes a complete characterization of four major phase transformation processes, including the formation and dissolution of α phase and the formation and dissolution of martensite.

[0087] Step 3: Describe the strain evolution in an incremental manner and construct a mechanical model. Input the thermal history data predicted by the transient temperature field model and the volume fraction of solid-state phase transformation predicted by the metallurgical model. Based on the mechanical model, predict the strain distribution at different times and in different spaces during the LDED process.

[0088] The mechanical model ,in, This represents the total strain increment. For elastic strain increment, For the increment of plastic strain, For thermal strain increment, This represents the volumetric strain increment during phase transition. This represents the increment of plastic strain induced by phase transformation.

[0089] Elastic strain increment , It is the stress increment tensor; It is the elastic modulus; I is Poisson's ratio; I is the unit tensor. It is the trace of the stress increment tensor for principal stress space.

[0090] Plastic strain increment , It is the plasticity multiplier. For stress tensor, Let be the yield function. For the deviatoric stress tensor, For Cauchy stress tensor; Let i be the Kronecker function; if i = j, ;otherwise ; This is the yield stress.

[0091] Thermal strain increment , The coefficient of thermal expansion is This is an increment of historical thermal data.

[0092] Phase transformation volumetric strain increment , For the volume fraction increment of the phase, The volume change rate caused by the phase transition; the volume change rate of the β→α and β→αm phase transitions. All equal to 0.2%; Let Kronecker function be used.

[0093] Phase transformation induced plastic strain increment , This is the phase transformation plasticity parameter, with a value of 3.57 × 10⁻⁶. -5 ; and These represent the volume fraction of martensite phase and its increment, respectively. It is the deviatoric stress tensor.

[0094] Based on the ABAQUS 2022 platform, a thermo-metallurgical-mechanical multiphysics coupled numerical model of a single-pass multilayer thin-walled TA15 titanium alloy component is constructed using the above method; it can realize integrated and coordinated prediction of temperature field evolution, microstructure transformation and residual stress distribution.

[0095] Taking a single-layer multi-layer thin-walled component as an example, its finite element mesh model is as follows: Figure 8 As shown, the dimensions of the deposition area for the thin-walled part are 80mm × 6mm × 32.8mm, with a single layer height of 0.8mm and a total of 41 layers deposited. The printing path employs an alternating scanning strategy in the X-direction. The thin-walled structure is discretized using fine mesh elements of 1mm × 1mm × 0.8mm, while the substrate uses a gradient mesh scheme to improve computational efficiency. The computational model is discretized using 8-node hexahedral elements, totaling 36,800 elements and 43,659 nodes.

[0096] To achieve accurate numerical simulation of the additive manufacturing process, this requires dynamically activated elements and high-precision spatiotemporal thermal input analysis. This is achieved by combining event sequences and progressive element activation techniques. The event sequence controls the loading sequence of the finite element mesh based on the laser scanning path and heat source parameters, while the progressive element activation technique tracks the material deposition evolution process in real time and analyzes the accumulated information of the scanning path. Compared to the traditional static element activation technique—the birth-death method—this strictly adheres to the actual manufacturing scanning sequence, avoiding artificial thermal conduction artifacts.

[0097] Figure 9Experimental and numerical simulation results of in-situ deformation of the substrate's free end in the vertical direction during LDED were compared. Analysis shows that during the laser heating stage, due to limited thermal expansion, the substrate's free end undergoes rapid downward bending deformation, while during the interlayer cooling stage, stress release caused by material contraction leads to rapid upward rebound. This deformation mode of "heating-induced downward bending - cooling-induced upward rebound" repeatedly occurs in subsequent thermal cycles. As the number of deposited layers increases, the amplitude of single-layer deformation fluctuations gradually decreases, while the residual plastic deformation accumulated after each cooling cycle results in a maximum overall deformation of 1.597 mm when cooled to room temperature after deposition. Numerical simulations show that the thermo-mechanical coupling model predicts a value of 1.944 mm (relative error +21.7%), while the thermo-metallurgical-mechanical coupling model predicts a value of 1.581 mm (relative error -1.0%). This comparison ultimately verifies that the numerical model incorporating solid-state phase transition effects has higher deformation prediction accuracy.

[0098] Figure 10 (a) shows the residual stress measured at three representative locations on the top surface of the thin-walled component using X-ray diffraction: -32±26 MPa at the left end, -7±15 MPa in the middle, and -55±33.5 MPa at the right end. The results indicate that the compressive stress at both ends is significantly higher than that in the central region, exhibiting an approximately symmetrical distribution. The simulation results are in good agreement with the X-ray diffraction data. The calculation results from the thermo-metallurgical-mechanical coupling model are closer to the experimental values ​​than those from the thermo-mechanical coupling model, although the difference between the two models is not significant. This is mainly due to the relatively small order of magnitude of the residual compressive stress on the surface of the thin-walled component.

[0099] This embodiment also further compares the residual stress data with that obtained by the contour method. Figure 10 Figures (b) and (c) show the stress comparison curves for the two paths, located in the typical tensile and compressive regions of the printed part. The numerical simulation results show good agreement with the contour method measurements in terms of stress distribution trends, and the thermo-metallurgical-mechanical coupling model is closer to the experimental data with higher prediction accuracy. It should be noted that slight asymmetric deviations are observed in the experimental data at specific locations due to measurement errors and local distortions.

[0100] In summary, the solid-state phase transformation effect can simultaneously reduce residual stress and deformation, improving the prediction accuracy of the thermo-metallurgical-mechanical coupling model. Through the thermo-metallurgical-mechanical coupling model, the temperature field evolution, microstructure transformation, and residual stress distribution during the LDED process can be accurately predicted, thus providing a theoretical basis for the microstructure control and residual stress optimization of TA15 alloy LDED process.

[0101] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method of predicting residual stresses and distortions in a laser directed energy deposited TA15 alloy component, characterized by, Comprising the following steps: Step 1: based on Fourier heat conduction theory, a transient temperature field model of laser directed energy deposition process considering heat conduction, convection and radiation is established; Input ambient temperature thermal history data at different time and space during the LDED process based on the transient temperature field model. The transient temperature field model ; wherein, is the density of TA15 alloy, is the constant pressure specific heat, is the thermal conductivity, is the thermal history data, is the laser volumetric heat source, is the heat loss, is the convective heat transfer coefficient, is the emissivity, is the ambient temperature, is the Boltzmann constant; Step 2: based on phase change principle, a metallurgical model is established, heat history data predicted by the transient temperature field model is input, and the volume fraction of solid phase change at different times and different spaces in the LDED process is predicted based on the metallurgical model; According to the solid phase change effect in the LDED process, the metallurgical model comprises the following four models: Alpha phase formation model: ， wherein, is the volume fraction of the a phase at the (n+1)th time step, is the volume fraction of the a phase at the nth time step, is the volume fraction of the β phase at the nth time step, is the time step, and is the β→a phase transformation kinetics coefficient at the nth time step, is the β→a phase equilibrium time at the nth time step, is the equilibrium volume fraction of the a phase at the (n+1)th time step; Martensite phase formation model: ； wherein, Vn+1is the volume fraction of martensite at time n+1, Vnis the volume fraction of martensite at time n; b is the martensite transformation kinetics coefficient, T0is the martensite start temperature; T is the thermal history data, Vn is the volume fraction of beta phase at time n, Vn+1is the equilibrium volume fraction of beta phase at time n+1, is the cooling rate; Martensite phase dissolution model: ； wherein, Vn+1is the volume fraction of martensite at time n+1, Vnis the volume fraction of martensite at time n; T is thermal history data, Vn is the volume fraction of beta phase at time n, is the time step, and is the martensite phase transformation kinetics coefficient at time n, is the martensite phase equilibrium time at time n; is the equilibrium volume fraction of martensite at time n+1. Alpha phase dissolution model: ； wherein, is the volume fraction of the β phase at the (n+1)th instant, and is the αβ phase transformation kinetics coefficient, is the time step, is the β phase equilibrium time at the (n+1)th instant; Step 3: the strain evolution is described in incremental form, and a mechanics model is constructed; heat history data predicted by the transient temperature field model and the volume fraction of solid phase change predicted by the metallurgical model are input, and the strain distribution at different times and different spaces in the LDED process is predicted based on the mechanics model; the mechanical model ; wherein, is the total strain increment, is the elastic strain increment, is the plastic strain increment, is the thermal strain increment, is the phase transformation volume strain increment, is the phase transformation induced plastic strain increment; thermal strain increment , is the thermal expansion coefficient, is the increment of thermal history data; Phase volume strain increment , is the phase volume fraction increment, is the volume change rate due to phase change; is the Kronecker function; Phase transformation induced plasticity strain increment , is the phase transformation plasticity parameter; and denote the volume fraction of martensite and its increment, respectively, is the deviatoric stress tensor.

2. The method of claim 1, wherein the TA15 alloy component is a laser directed energy deposition TA15 alloy component. The laser volume heat source A Goldak double ellipsoid heat source model is used; ， wherein, , , and are shape parameters of the double-ellipsoid heat source; and are energy distribution parameters; is the laser power; is the energy absorption rate; and xyz is the coordinate value in the local coordinate system with the center of the moving laser spot as the origin, used to calculate the position of the point from the center of the heat source.

3. The method of claim 1, wherein the TA15 alloy component is a laser directed energy deposition TA15 alloy component. Constant pressure specific heat of TA15 alloy Thermal conductivity Real-time acquisition by linear interpolation algorithm between adjacent data points under temperature function relationship.

4. The method of claim 1, wherein, Phase transformation kinetics coefficients and By isothermal transformation TTT curves; Equilibrium volume fraction of alpha phase , A and c are fitting coefficients, T is thermal history data, β-transus temperature.

5. The method of claim 1, wherein, martensitic transformation kinetic coefficient b and martensitic start transformation temperature are obtained by thermal expansion experiments.

6. The method of claim 1, wherein, n-th time martensite phase transformation kinetics coefficient , and the n+1-th time martensite equilibrium volume fraction The dissolution degree of martensite at different temperatures is determined by hardness measurement method; , and are fitting coefficients.

7. The method of claim 1, wherein the TA15 alloy component is a laser directed energy deposition TA15 alloy component. Dynamical coefficient of the αβ phase transition is a constant independent of temperature; , is an exponential pre-factor, is an activation energy, is a universal gas constant; is the thermal history data at time n+1.

8. The method for predicting residual stress and deformation of a laser directed energy deposition TA15 alloy component according to claim 1, based on the ABAQUS platform, the transient temperature field model, the metallurgical model and the mechanics model are constructed into a thermal-metallurgical-mechanics multi-physical field coupling numerical model; the thermal-metallurgical-mechanics multi-physical field coupling numerical model can integrally and cooperatively predict the evolution of temperature field, microstructure transformation and residual stress distribution.

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