Microhardness prediction method and device for laser additive titanium alloy

Abstract

The invention relates to the technical field of metal additive manufacturing, and particularly provides a microhardness prediction method and device for a laser additive titanium alloy, and the method comprises the steps: simulating a multi-channel multi-layer forming process of a laser additive manufacturing titanium alloy sample under set process parameters in a multi-physical field numerical model, and obtaining a thermal cycle curve of internal test points of a component; the thermal cycle curve is input into a non-isothermal JMAK model, the martensite lath width, the martensite phase and the phase volume fraction of the titanium alloy in the additive manufacturing process are calculated, and the final phase composition of each test point is formed; constructing a microhardness prediction model between the microstructure and the microhardness; and substituting the final phase composition into the microhardness prediction model, and predicting the microhardness of different positions of the titanium alloy sample. According to the method, by coupling the high-precision LBM-HF multi-physical field model and the non-isothermal JMAK structure evolution model, the microstructure evolution of the laser additive manufacturing titanium alloy under the complex thermal cycle is effectively predicted.

Description

Technical Field

[0001] This application relates to the field of metal additive manufacturing technology, and in particular to a method and apparatus for predicting the microhardness of laser-added titanium alloys. Background Technology

[0002] Laser additive manufacturing is an extremely complex non-equilibrium rapid solidification process. In this process, a laser beam acts on the metal with extremely high energy density and speed, causing it to undergo rapid melting and solidification. This unique "micro-pool" metallurgical process leads to the formation of a violent and non-uniform temperature field inside the titanium alloy, resulting in a complex thermal cycling history.

[0003] The microstructure of titanium alloys is extremely sensitive to thermal history; different thermal cycles can lead to significant differences in the final microstructure, such as the formation of fine acicular martensite. ), lamellar The microstructure, even including coarse equiaxed grains, directly determines the mechanical properties of additively manufactured titanium alloy components, such as strength, hardness, plasticity, and fatigue performance. Currently, research on laser additive manufacturing of titanium alloys mainly focuses on optimizing process parameters, typically employing a trial-and-error approach. This involves conducting numerous experiments to explore the impact of different process parameters (such as laser power and scanning speed) on the final product's performance. This method is not only time-consuming, labor-intensive, and costly, but also struggles to reveal the intrinsic physical relationship between process, microstructure, and properties, making the determination of the process window highly subjective. Furthermore, for large and complex components, the thermal histories of different parts vary significantly, leading to severe inhomogeneity in microstructure and properties. Relying solely on experimental methods makes it difficult to effectively control the uniformity and consistency of the entire component's performance. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method and apparatus for predicting the microhardness of laser-additively manufactured titanium alloys. By coupling a high-precision LBM-HF multiphysics model and a JMAK microstructure evolution model, the microstructure evolution of laser-additively manufactured titanium alloys under complex thermal cycling is effectively predicted.

[0005] To achieve the objectives of this application, the following technical solution is provided: In a first aspect, this application provides a method for predicting the microhardness of laser-added titanium alloys, comprising: A multi-physics numerical model of the laser additive manufacturing process was established to simulate the multi-stage and multi-layer forming process of the laser additive manufacturing of titanium alloy samples under set process parameters. The temperature history data of multiple test points in the deposition direction inside the virtual formed titanium alloy sample were extracted to form a thermal cycling curve for each test point. The thermal cycling curves of each test point were imported into a non-isothermal JMAK model to calculate the first martensite formation of the virtual-formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The width of the slats determines the final phase composition of the virtual-formed titanium alloy sample at different test points. Determine the second [value] at each test point of the sample prepared by laser additive manufacturing under different process parameters. Phase volume fraction, second martensite Phase volume fraction and second martensite Slat width, and based on the second Phase volume fraction, second martensite Phase volume fraction and the second martensite The lath width is used to construct a microhardness prediction model between microstructure and microhardness; The final phase composition is substituted into the microhardness prediction model to predict the microhardness at different locations of the titanium alloy sample.

[0006] A further improvement of the present invention is that the step of extracting temperature history data of multiple test points in the deposition direction inside the virtual-formed titanium alloy sample to form a thermal cycling curve for each test point includes: uniformly setting N test points upward from the substrate along the center line of the deposition direction inside the titanium alloy sample; starting from the first layer, extracting the temperature data of the center point of the sample every five layers as the temperature history data received at the current position; and for each test point, recording the complete temperature history data of the temperature change over time for the test point to obtain the thermal cycling curve for each test point.

[0007] A further improvement of this invention lies in that the thermal cycling curve of each test point is imported into a non-isothermal JMAK model to calculate the first martensite formation of the virtual-formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The lath width, forming the final phase composition of the virtual formed titanium alloy sample at different test points, includes: using the thermal cycling curve as input to the non-isothermal JMAK model to establish a corresponding JMAK equation for the phase transformation behavior of the virtual formed titanium alloy sample during laser additive manufacturing, and predicting the evolution of the microstructure; wherein, the phase transformation behavior includes: The phase transforms into acicular martensite during rapid cooling. Phase transition, and in the subsequent thermal cycle Phase decomposition and Phase formation. The non-isothermal JMAK model includes: 1) Model of α-lamellae formation during cooling process:

[0008] in, The net basket α at time n+1 w Phase volume fraction; It is the grain boundary α at time n+1. gb Phase volume fraction; k α and N α These are material dynamic parameters used to constrain the transformation of the JMAK equation; It is the phase equilibrium fraction of the α phase at time n+1, which depends on the temperature. It is the volume fraction of phase β at time n; At time n, the net basket α w Phase volume fraction; It is the grain boundary α at time n. gb Phase volume fraction; Δt expresses the actual time elapsed for the phase transition reaction in each minute interval; t c To establish a fictitious equivalent time, the initial states of the two phases before each phase transition increment are determined. Fictitious equivalent time The calculation process is as follows:

[0009] 2) Model for the formation of martensite α′ phase during the cooling process:

[0010] in, It is the volume fraction of the martensite α′ phase at time n+1; T1 is the volume fraction of martensite α′ phase at time n; T2 is the temperature at time n-1; T3 is the temperature at time n+1; T4 is the temperature at time n+1; T5 is the temperature at time n+1; T6 is the temperature at time n-1; T7 is the temperature at time n+1; T8 is the temperature at time n-1; T9 is the temperature at time n+1; T1 is the temperature at time n-1; T2 is the temperature at time n+1 ms It is the martensitic initiation temperature; b KM These are the physical parameters of the materials used in the constraint equations; It is the volume fraction of phase β at time n; It is the phase equilibrium fraction of the β phase at time n+1, which depends on the temperature. 3) The martensitic α′ phase recovery model during the heating process is as follows:

[0011] in, Let be the volume fraction of the martensite α phase at time n. The volume fraction increment of the martensite α phase at time n; It is the temperature-dependent phase equilibrium fraction of the martensitic phase at time n+1; k αm and N αmThese are material dynamic parameters used to constrain the transformation of the JMAK equation; It is the volume fraction of phase β at time n; It is the phase equilibrium fraction of the β phase at time n+1, which depends on the temperature. It is the increase in the volume fraction of phase β at time n; Fictional Time The calculation process is as follows:

[0012] 4) The α-phase dissolution model during the heating process is as follows:

[0013] In the embodiments of this application f diss (T) = 2.2 × 10 -31 T 9.89 T represents the current Kelvin temperature. The calculation process for the equivalent virtual time is as follows:

[0014] The calculation process for determining whether the decomposition has ended is as follows:

[0015] 5) Lath weave structure dimension calculation model, specifically:

[0016] in, This indicates the lath structure dimensions at the next time step t+Δt; This represents the lath weave size at the current time t; This represents the α-phase volume fraction at the current time t; This represents the α-phase volume fraction at the next time step t+Δt; This represents the volume fraction of phase α′ at the current time t; This represents the volume fraction of phase α′ at the next time step t+Δt; and Both R and R depend on the material's calculation parameters, among which R represents the statistically average geometric width of the slats at various temperatures under equilibrium conditions, where R represents the activation temperature.

[0017] A further improvement of this invention lies in determining the second martensite at each test point of the sample prepared by laser additive manufacturing under different process parameters. Phase volume fraction determination includes: preparing titanium alloy samples based on the set process parameters and different scanning speeds; observing the microstructure morphology of the treated titanium alloy samples at different height positions to obtain SEM images of the titanium alloy samples; and using image analysis software to analyze the martensite in the SEM images. The width of the lath was quantitatively analyzed to obtain the second martensite at different scanning speeds. Slat width.

[0018] A further improvement of the present invention is that the statement based on the second... Phase volume fraction, second martensite Phase volume fraction and the second martensite The lath width is used to construct a microhardness prediction model between microstructure and microhardness, including: systematically characterizing the microstructure and testing the microhardness of titanium alloy samples prepared under different process parameters to obtain matching correlation data; in the matching correlation data, the microhardness of the titanium alloy sample is related to the second martensite. The increase in phase volume fraction, the second The increase in phase volume fraction and the second martensite The microhardness prediction model is related to the slat width; specifically, the construction steps include: Establish the Hall-Page relationship between microhardness and α-phase volume fraction:

[0019] Where HV represents the microhardness obtained experimentally. The volume fraction of the α phase obtained from the experiment. , The fitting coefficients are preferred; ideally, the fitting coefficients are... , Calculated using the least squares method; Establish the Hall-Page relationship between microhardness and α′ phase volume fraction:

[0020] Where HV represents the microhardness obtained experimentally. Obtained from the experiment Phase volume fraction, , The fitting coefficients are preferred; , Calculated using the least squares method; The Hall-Page relationship between microhardness and lath microstructure size is established as follows:

[0021] Where HV represents the microhardness obtained experimentally. The lath microstructure dimensions obtained in the experiment. , The fitting coefficients are preferred; , Calculated using the least squares method; A microhardness prediction model based on the functional relationship between microhardness and the volume fractions of the α-phase, α′-phase, and lath microstructure is established, specifically as follows:

[0022] Among them, HV c The fitting coefficients are HV; preferably, the fitting coefficients are HV. c It is calculated using the least squares method.

[0023] A further improvement of this invention is that it utilizes electron backscatter diffraction technology to analyze the phase composition of the titanium alloy sample.

[0024] A further improvement of the present invention is that the size of the titanium alloy sample is 2 mm × 2 mm × 4 mm.

[0025] Secondly, this application provides a microhardness prediction device for laser-added titanium alloys, used to implement the above-mentioned microhardness prediction method for laser-added titanium alloys, including: The multiphysics simulation module is used to establish a multiphysics numerical model of the laser additive manufacturing process, simulate the multi-stage and multi-layer forming process of laser additive manufacturing of titanium alloy samples under set process parameters, extract the temperature history data of multiple test points in the deposition direction inside the virtual formed titanium alloy sample, and form a thermal cycling curve for each test point. The microstructure calculation module is used to import the thermal cycling curves into a non-isothermal JMAK model to calculate the titanium alloy sample during the additive manufacturing process. Phase volume fraction, martensite The phase volume fraction and lath microstructure size determine the final phase composition at different test points of the titanium alloy sample; The microhardness prediction model construction module is used to determine the microhardness at various test points of samples prepared by laser additive manufacturing under different process parameters. Phase volume fraction, martensite Phase volume fraction and martensite Slat width, and based on the Phase volume fraction, the martensite Phase volume fraction and the martensite The lath width is used to construct a microhardness prediction model between microstructure and microhardness; The microhardness prediction module is used to substitute the final phase composition into the microhardness prediction model to obtain the microhardness prediction of the titanium alloy sample at different locations.

[0026] Compared with the prior art, the present invention has the following beneficial effects: The microhardness prediction method and apparatus for laser additive manufacturing of titanium alloys provided in this application can effectively predict the microstructure evolution of laser additive manufacturing titanium alloys under complex thermal cycling by coupling a high-precision LBM-HF multiphysics model and a JMAK microstructure evolution model. It reveals the intrinsic physical connection and mechanism of action between "process parameters-thermal cycling-microstructure-microhardness", providing a theoretical basis for fundamentally understanding and controlling the additive manufacturing process. Moreover, the prediction results are in high agreement with experimental data. Attached Figure Description

[0027] The accompanying drawings are provided to further understand this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. Figure 1 This is a schematic diagram of an optional process for predicting the microhardness of laser-additive titanium alloys provided in an embodiment of this application. Figure 2 The simulation setup provided for the embodiments of this application shows a schematic diagram of the distribution of test points for extracting 20 simulated thermal cycling curves on a titanium alloy sample; Figure 3 The thermal cycling curves of five test points extracted for detailed analysis under the condition of laser power P=180W, obtained by simulation using the LBM-HF model for embodiments of this application; Figure 4 The embodiments of this application provide the final deposition direction of each phase ( ) along the entire titanium alloy sample deposition direction. , , Statistical chart of the numerical calculation results of the content; Figure 5 Microstructure morphology images of samples at different height positions (top, upper middle, middle, lower middle, and bottom) at different laser scanning speeds, provided for embodiments of this application; Figure 6 The microhardness (HV) provided for the embodiments of this application and A graph showing the relationship between phase volume fractions; Figure 7 Microhardness and martensite provided for embodiments of this application A graph showing the relationship between contents; Figure 8The relationship between microhardness and lath microstructure size W is obtained by summarizing all experimental data points provided in the embodiments of this application. Figure 9 A comparative diagram showing the comparison between the calculation results of the finally established microhardness prediction model and the experimental data at V=900mm / s, provided for an embodiment of this application. Figure 10 A comparative diagram showing the comparison between the calculation results of the finally established microhardness prediction model and the experimental data at V=1080mm / s, provided for the embodiments of this application. Figure 11 This is a structural diagram of a microhardness prediction device for laser additive titanium alloys provided in the embodiments of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0030] Laser additive manufacturing, due to its ability to fabricate complex structures and high-performance metal parts, has been widely used in aerospace, medical devices, and other fields. However, the laser additive manufacturing process is an extremely complex non-equilibrium rapid solidification process. In this process, a laser beam acts on the metal with extremely high energy density and speed, causing it to undergo rapid melting and solidification. This unique "micro-melting pool" metallurgical process leads to the formation of a violent and non-uniform temperature field within the titanium alloy, resulting in a complex thermal cycling history. The microstructure of titanium alloys is extremely sensitive to thermal history; different thermal cycles can lead to significant differences in the final microstructure, such as the formation of fine acicular martensite (…). ), lamellar The microstructure, even including coarse equiaxed grains, directly determines the mechanical properties of additively manufactured titanium alloy components, such as strength, hardness, plasticity, and fatigue performance.

[0031] Currently, research on laser additive manufacturing of titanium alloys mainly focuses on optimizing process parameters, typically employing a trial-and-error approach. This involves conducting numerous experiments to explore the impact of different process parameters (such as laser power and scanning speed) on the final product's performance. This method is not only time-consuming, labor-intensive, and costly, but also struggles to reveal the intrinsic physical relationships between process, microstructure, and properties, making the determination of the process window highly subjective. Furthermore, for large and complex components, the thermal histories of different parts vary significantly, leading to severe inhomogeneities in microstructure and properties. Relying solely on experimental methods makes it difficult to effectively control the uniformity and consistency of the entire component's performance.

[0032] To address these challenges, researchers have begun exploring numerical simulations to study laser additive manufacturing processes. Existing research has included attempts to couple models such as the phase-field method with macroscopic thermal models to simulate the formation of microstructures. However, the phase-field method is computationally intensive and typically only simulates micrometer-scale, millisecond-level processes, making it difficult to apply to the simulation of the entire additive manufacturing process for macroscopic components. Other studies employ simplified models, such as empirical models based on continuous cooling transformation (CCT) diagrams to predict phase transformations. However, these models are usually based on traditional casting or heat treatment processes and cannot fully account for the extreme non-equilibrium conditions in additive manufacturing. Furthermore, the traditional Hall-Page relation primarily focuses on the influence of grain size on strength; its applicability to the complex martensitic structures in additive manufacturing requires modification and verification.

[0033] To address the aforementioned technical problems, the present invention proposes the following technical solutions and corresponding embodiments.

[0034] The following is combined Figures 1 to 11 The illustrated embodiments describe the technical solution of the present invention: Example 1 This application provides an embodiment of a method for predicting the microhardness of laser-added titanium alloys, referring to... Figure 1 As shown, the microhardness prediction method of this embodiment includes the following steps S101 to S104: Step S101: Establish a multi-physics numerical model of the laser additive manufacturing process, simulate the multi-stage and multi-layer forming process of the laser additive manufacturing of titanium alloy samples under set process parameters, extract the temperature history data of multiple test points in the deposition direction inside the virtual formed titanium alloy sample, and form a thermal cycling curve for each test point.

[0035] In this embodiment, a multiphysics model based on the Lattice Boltzmann Method-Height Function (LBM-HF) is first established. This multiphysics model can comprehensively consider complex physical processes such as fluid dynamics and heat transfer in laser additive manufacturing, and accurately simulate multiple physical phenomena in the laser additive manufacturing process, including but not limited to: laser energy absorption, powder melting, molten pool flow, heat conduction, heat radiation, heat convection, and solidification processes.

[0036] In this embodiment, the high-precision discrete velocity framework of the D3Q19 model is used to solve the flow field to describe the complex flow behavior of molten metal under the influence of the Marangoni effect, surface tension, and gravity. Specifically, the Lattice Boltzmann Method (LBM) is used to solve the incompressible Navier-Stokes equations by discretizing the fluid into particles and calculating the density and velocity of the fluid by calculating the discrete particle distribution function. This method has a natural advantage in handling complex boundary and multiphase flow problems. The energy equations are solved within the LBM framework, considering heat conduction, convection, and radiation.

[0037] In this embodiment, the laser heat source adopts a Gaussian surface heat source model, whose energy distribution conforms to a Gaussian distribution, which can better simulate the energy input of the laser beam. In addition, the Gaussian surface heat source model also considers the changes of thermophysical parameters such as thermal conductivity and specific heat capacity of the material in the solid and liquid states with temperature.

[0038] In this embodiment, the curvature and unit normal vector of the molten pool surface are calculated using the height function (HF) method. This is crucial for accurately calculating surface tension and the Marangoni effect, which in turn affect the shape and size of the molten pool, and consequently, the distribution of the temperature field.

[0039] In this embodiment, the thermal cycling curve refers to the complete temperature-time history of any specified point inside the component. These curves (any point inside the component) contain rich thermal history information, such as the rapid temperature rise and subsequent cooling caused by each laser scan, as well as the thermal cycling and heat accumulation effects caused by the heat input of subsequent deposited layers. As a feasible implementation method, during the simulation, multiple feature points are selected along the height direction (deposition direction) of the virtual formed titanium alloy sample, and the complete temperature-time history (i.e., thermal cycling curve) of these points is extracted. The key features of the thermal cycling curve, such as peak temperature, cooling rate, and heat accumulation effect, are analyzed.

[0040] For example, the simulated titanium alloy sample size was set to 2mm × 2mm × 4mm (length × width × height), and the laser power was set to 180W, consistent with the experimental process parameters. To obtain the internal thermal history of the sample, such as... Figure 2 As shown, 20 test points were uniformly arranged from the substrate upwards along the deposition direction (Z-axis) on the center line of the sample. Starting from the first layer, temperature data of the center point of the sample was extracted every five layers to represent the temperature history experienced at that location. The complete temperature change over time at these points was recorded to form a thermal cycling curve. For example, Figure 3 The thermal cycling curves of the titanium alloy sample from bottom to top for different layers (layer 1, layer 25, layer 50, layer 75, layer 95) are obtained under the process conditions of laser power P = 180W and speed V = 900mm / s.

[0041] Step S102: Import the thermal cycling curve of each test point into the non-isothermal JMAK model to calculate the first martensite formation of the virtual formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The width of the slats determines the final phase composition of the virtual-formed titanium alloy sample at different test points.

[0042] In this embodiment, the non-isothermal JMAK (Johnson-Mehl-Avrami-Kolmogorov) model is a mature model describing solid-state phase transformation processes under isothermal or non-isothermal conditions. It can effectively calculate the change in phase transformation volume fraction over time and has been widely used in various alloy processing fields. It has been proven that the more accurate the input time-temperature curve during simulation, the closer the calculation results are to the actual phase transformation reaction. However, for laser-powder-wood bed fusion technology, the temperature change during L-PBF forming is very rapid. Due to the extreme supercooling during the phase transformation, atoms have virtually no time to diffuse. Therefore, the phase transformation reaction inside the sample under L-PBF forming generally involves a large number of non-diffusion transformation types. Therefore, to adapt to the special L-PBF processing mechanism, this embodiment makes further adaptive modifications to the non-isothermal JMAK model.

[0043] Specifically, the non-isothermal JMAK model in this embodiment includes: 1) The α-lamellae formation model during the cooling process is shown in equation (1):

[0044] in, The net basket α at time n+1 w Phase volume fraction; It is the grain boundary α at time n+1. gb Phase volume fraction; k α and N α These are material dynamic parameters used to constrain the transformation of the JMAK equation; It is the phase equilibrium fraction of the α phase at time n+1, which depends on the temperature. It is the volume fraction of phase β at time n; At time n, the net basket α w Phase volume fraction; It is the grain boundary α at time n. gb Phase volume fraction; Δt expresses the actual time elapsed for the phase transition reaction in each minute interval; t c To establish a fictitious equivalent time, the initial states of the two phases before each phase transition increment are determined. Fictitious equivalent time The calculation is shown in equation (2):

[0045] 2) Model for the formation of martensite α′ phase during the cooling process:

[0046] in, It is the volume fraction of the martensite α′ phase at time n+1; T1 is the volume fraction of martensite α′ phase at time n; T2 is the temperature at time n-1; T3 is the temperature at time n+1; T4 is the temperature at time n+1; T5 is the temperature at time n+1; T6 is the temperature at time n-1; T7 is the temperature at time n+1; T8 is the temperature at time n-1; T9 is the temperature at time n+1; T1 is the temperature at time n-1; T2 is the temperature at time n+1 ms It is the martensitic initiation temperature; b KM These are the physical parameters of the materials used in the constraint equations; It is the volume fraction of phase β at time n; It is the phase equilibrium fraction of the β phase at time n+1, which depends on the temperature. Regarding b KM The value of b varies depending on the specific application, but generally it will not exceed 0.01. Because laser additive manufacturing experiences significant temperature fluctuations and rapid phase transitions, b is chosen in this embodiment. KM =0.005.

[0047] 3) The martensite α′ phase recovery model during the heating process is shown in equation (4):

[0048] in, Let be the volume fraction of the martensite α phase at time n. The volume fraction increment of the martensite α phase at time n; It is the temperature-dependent phase equilibrium fraction of the martensitic phase at time n+1; k αm and N αm These are material dynamic parameters used to constrain the transformation of the JMAK equation; It is the volume fraction of phase β at time n; It is the phase equilibrium fraction of the β phase at time n+1, which depends on the temperature. It is the increase in the volume fraction of phase β at time n; Fictional Time The calculation is shown in equation (5):

[0049] 4) The α-phase dissolution model during the heating process is shown in equation (6):

[0050] In the embodiments of this application f diss (T) = 2.2 × 10 -31 T 9.89 T represents the current Kelvin temperature. The equivalent virtual time is calculated as shown in equation (7):

[0051] in, The criterion for determining whether the decomposition has ended is calculated as shown in equation (8):

[0052] 5) Lath microstructure dimension calculation model:

[0053] in, This indicates the lath structure dimensions at the next time step t+Δt; This represents the lath weave size at the current time t; This represents the α-phase volume fraction at the current time t; This represents the α-phase volume fraction at the next time step t+Δt; This represents the volume fraction of phase α′ at the current time t; This represents the volume fraction of phase α′ at the next time step t+Δt; and Both R and R depend on the material's calculation parameters, among which This represents the statistically average geometric width of the slats at various temperatures under equilibrium conditions, where R represents the activation temperature. (This is from an embodiment of the application.) =1.42 μm, R=294K.

[0054] In this embodiment, the thermal cycling curve obtained in step S101 is used as the input to the non-isothermal JMAK model to target the main phase transformation behavior of titanium alloys in the laser additive manufacturing process, namely high temperature. The phase transforms into acicular martensite during rapid cooling. Phase transition, and in subsequent thermal cycling Phase decomposition and The formation of phases is investigated, and corresponding JMAK equations are established to predict the evolution of microstructures.

[0055] In this embodiment, the content of microstructure evolution prediction includes: → Martensitic phase transformation, → Decomposition; among which, when the cooling rate exceeds a critical value, high temperature The phases transform into acicular martensite through a diffusionless shear mechanism. This phase transformation process is extremely rapid, and its start and end are typically determined by the martensitic transformation initiation temperature (Ms) and the end temperature (Mf). It should be noted that the initiation temperature for the titanium alloy in this model is set at 575℃, and the end temperature at 450℃. In subsequent thermal cycling, if the temperature is reheated to the martensitic decomposition temperature range (typically 600-900℃) and held for a certain time, metastable... The phase will decompose into a more stable equilibrium phase. and .

[0056] As a feasible implementation method, this embodiment segments the thermal cycling curve. Within each time step, based on the current temperature and the rate of temperature change, it uses a non-isothermal JMAK model to calculate... Harmony The change in phase volume fraction allows for the prediction of the final phase composition at different locations of the component after the entire additive manufacturing process, for example... Harmony Phase content. Specifically, each thermal cycle curve extracted from the LBM-HF simulation is used as input to the non-isothermal JMAK model. Temperature values ​​are read at each time step, and the phase transformation kinetics model is used to calculate... Harmony The increase in phase volume fraction ultimately yields the final phase composition (first martensite) at each test point after the entire additive manufacturing process is completed. Phase volume fraction, first Phase volume fraction and first martensite (Slat width). Figure 4 This application's embodiment demonstrates the final deposition of each phase along the entire titanium alloy sample direction. , , Statistical chart of the numerical calculation results of the content.

[0057] Step S103: Determine the second [test point] at each test point of the sample prepared by laser additive manufacturing under different process parameters. Phase volume fraction, second martensite Phase volume fraction and second martensite Slat width, and based on the second Phase volume fraction, second martensite Phase volume fraction and the second martensite The lath width is used to construct a microhardness prediction model that relates microstructure and microhardness.

[0058] In this embodiment, under a laser power of P=180W, four different scanning speeds V=360 mm / s, 720 mm / s, 1080 mm / s, and 1440 mm / s were set to prepare four sets of titanium alloy samples. Then, the four sets of titanium alloy samples were cut, inlaid, ground, polished, and etched. Subsequently, as... Figure 5 As shown, multiple test points were selected, and the α-phase volume fraction (second phase) at the test points was obtained using the EBSD method. Phase volume fraction), martensite Phase volume fraction (second martensite) (Phase volume fraction); The microstructure morphology at the test points was obtained using scanning electron microscopy to obtain SEM images of the titanium alloy samples. Image analysis software was used to analyze the martensite. Quantitative statistical analysis of lath width was performed to obtain martensite at different scanning speeds. Lath width (second martensite) (Slat width).

[0059] In this embodiment, the martensite microstructure along the height direction inside the titanium alloy sample is... The width and microhardness show opposite trends (i.e., as the martensite...) (Increasing the width of the martensite significantly reduces its microhardness). The relationship between the width and microhardness was fitted to determine the martensite. width A Hall-Petch-like relationship exists between microhardness and [the material's] internal properties. Tissue content and martensite width They exhibit the same trend of first increasing and then decreasing, while martensite... The microstructure content and microhardness show the same trend of first decreasing and then increasing. Therefore, when establishing the relationship between microhardness and the internal microstructure of the sample, it is necessary to consider the influence of the microstructure content of each phase on microhardness, and further use the change of microstructure content to correct the relationship, so as to obtain the correction function expression (Hall-Page relation).

[0060] In this embodiment, a Hall-Page relationship between microhardness and various microstructure data is established based on experimentally obtained α-phase volume fraction, α'-phase volume fraction, and lath microstructure size. Finally, a microhardness prediction model based on the functional relationship between microhardness and α-phase volume fraction, α'-phase volume fraction, and lath microstructure size is established, mainly including: 1) Establish the Hall-Page relationship between microhardness and α phase volume fraction, as shown in equation (10):

[0061] Where HV represents the microhardness obtained experimentally. The volume fraction of the α phase obtained from the experiment. , Here, the fitting coefficients are: , The calculation is obtained by the least squares method, and the calculation process is shown in equations (11), (12), (13), and (14):

[0062]

[0063]

[0064]

[0065] Where p is the total number of test points; It is the i-th test point obtained from the experiment. Phase volume fraction; It is the microhardness at the i-th test point obtained in the experiment.

[0066] In the embodiments of this application, the relationship between the α-phase volume fraction and microhardness is as follows: Figure 6 As shown, k1=-59.46, b1=337.40.

[0067] 2) Establish the Hall-Page relationship between microhardness and α′ phase volume fraction, as shown in equation (15):

[0068] Where HV represents the microhardness obtained experimentally. The volume fraction of the α′ phase obtained from the experiment. , These are the fitting coefficients; , The calculation is obtained by the least squares method, and the calculation process is shown in equations (16), (17), (18), and (19):

[0069]

[0070]

[0071]

[0072] in, It is the i-th test point obtained from the experiment. Phase volume fraction.

[0073] In the embodiments of this application, The relationship between phase volume fraction and microhardness is as follows: Figure 7 As shown, k2=62.43, b2=275.95.

[0074] 3) Establish microhardness and lath microstructure size (second martensite) The Hall-Page relation for the slat width is as follows (20):

[0075] Where HV represents the microhardness obtained experimentally. The lath microstructure dimensions obtained in the experiment. , These are the fitting coefficients; , The results are obtained by the least squares method, and the calculation process is shown in equations (21), (22), (23), (24), and (25):

[0076]

[0077]

[0078]

[0079]

[0080] in, It is the lath structure size at the i-th test point obtained from the experiment; In the embodiments of this application, the relationship between lath microstructure size and microhardness is as follows: Figure 8 As shown, k3=27.3, b3=307.85.

[0081] 4) Establish a microhardness prediction model based on the functional relationship between microhardness and the volume fraction of α phase, the volume fraction of α′ phase, and the size of lath microstructure, as shown in equation (26):

[0082] Among them, HV c The fitting coefficients are HV. c It is calculated by the least squares method, as shown in equation (27);

[0083] In this embodiment of the application, HV c =280.4.

[0084] Therefore, the microhardness prediction model based on the functional relationship between microhardness and the volume fraction of α phase, the volume fraction of α′ phase, and the size of lath structure is shown in equation (28):

[0085] Among them, based on the martensite The linear relationship between strip width and microhardness was used to determine the microhardness through fitting. With martensite -1 / 2 of the width of the slat The relationship is as follows: (29) Sure Phase volume fraction With microhardness The relationship is as follows: (30) Determine the martensite Phase volume fraction With microhardness The relationship is as follows: (31) Based on the functional relationship model between microhardness and microstructure, and by weighted fitting of equations (29), (30), and (31), the following microhardness prediction model is obtained: (32) In the formula, The predicted microhardness value. for Phase volume fraction Martensite Phase volume fraction Martensite Slat width Step S104: The martensite obtained based on the non-isothermal JMAK model Phase volume fraction, The phase volume fraction and lath microstructure size are substituted into the microhardness prediction model to predict the microhardness at different locations of the titanium alloy sample.

[0086] In this embodiment, the first martensite calculated by the non-isothermal JMAK model in step S102, using the simulated thermal cycling curve as input data, is... Phase volume fraction, first Phase volume fraction and lath microstructure size (first martensite) Substituting the strip width into the microhardness prediction model established in step S103, the microhardness of the titanium alloy sample at different locations can be predicted.

[0087] Therefore, the microhardness distribution of laser-added titanium alloys can be predicted using the above formula (28).

[0088] The microhardness prediction method for laser additive manufacturing of titanium alloys provided in this embodiment, by coupling a high-precision LBM-HF multiphysics model and a JMAK microstructure evolution model, can effectively predict the microstructure evolution of laser additive manufacturing titanium alloys under complex thermal cycling. It reveals the intrinsic physical connection and mechanism of action between "process parameters-thermal cycling-microstructure-microhardness", providing a theoretical basis for fundamentally understanding and controlling the additive manufacturing process. Moreover, the prediction results have a high degree of agreement with experimental data.

[0089] like Figure 9 and Figure 10 As shown, the microhardness curve predicted by the method of this invention (the simulation result line in the figure) and the experimentally measured microhardness data (the experimental data points in the figure) exhibit a high degree of consistency in both trend and value. This fully demonstrates the accuracy and reliability of the prediction model established by this invention.

[0090] Thus, this embodiment successfully established a method for accurately predicting the microstructure and microhardness of laser additive manufacturing titanium alloys by combining multiphysics simulation, phase transformation kinetics model, and experimental data fitting. This method not only reveals the complex relationship between process, microstructure, and properties, but also provides strong theoretical guidance for process optimization and performance control in actual production.

[0091] The microhardness prediction method for laser additive titanium alloys provided in this embodiment can realize the virtual optimization design of process parameters and conduct a large number of "virtual experiments" on a computer, thereby replacing expensive and time-consuming physical experiments, significantly shortening the R&D cycle and reducing development costs. At the same time, it makes it possible to "design on demand" the microstructure and mechanical properties of additively manufactured titanium alloy components, and to manufacture functional components with specific gradient microstructures or specific property distributions according to different application requirements.

[0092] This invention also provides a microhardness prediction device 10 for laser-added titanium alloys, used to implement a microhardness prediction method for laser-added titanium alloys. The structure of the device is as follows: Figure 11 As shown, it includes: a multiphysics simulation module 11, a microstructure calculation module 12, a microhardness prediction model construction module 13, and a microhardness prediction module 14.

[0093] The multiphysics simulation module 11 is used to establish a multiphysics numerical model of the laser additive manufacturing process, simulate the multi-stage and multi-layer forming process of the laser additive manufacturing of titanium alloy samples under set process parameters, extract the temperature history data of multiple test points in the deposition direction inside the virtual formed titanium alloy sample, and form a thermal cycling curve for each test point. The microstructure calculation module 12 is used to import the thermal cycling curve of each test point into the non-isothermal JMAK model to calculate the first martensite of the virtual formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The width of the slats determines the final phase composition of the virtual-formed titanium alloy sample at different test points. Microhardness prediction model construction module 13 is used to determine the second microhardness at each test point of the sample prepared by laser additive manufacturing under different process parameters. Phase volume fraction, second martensite Phase volume fraction and second martensite Slat width, and based on the second Phase volume fraction, second martensite Phase volume fraction and the second martensite The lath width is used to construct a microhardness prediction model between microstructure and microhardness; The microhardness prediction module 14 is used to substitute the final phase composition into the microhardness prediction model to predict the microhardness at different locations of the titanium alloy sample.

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0095] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0096] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0097] In the several embodiments provided in this application, it should be understood that the disclosed systems, modules, and methods can be implemented in other ways. For example, the module embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules or units, and may be electrical, mechanical, or other forms.

[0098] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. This application is not limited to the exact structures described above and illustrated in the accompanying drawings, and it should not be considered that the specific implementation of this application is limited to these descriptions. For those skilled in the art, various changes and modifications made without departing from the concept of this application should be considered to fall within the protection scope of this application.

Claims

1. A method for predicting the microhardness of laser-added titanium alloys, characterized in that, include: A multi-physics numerical model of the laser additive manufacturing process was established to simulate the multi-stage and multi-layer forming process of the laser additive manufacturing of titanium alloy samples under set process parameters. The temperature history data of multiple test points in the deposition direction inside the virtual formed titanium alloy sample were extracted to form a thermal cycling curve for each test point. The thermal cycling curves of each test point were imported into a non-isothermal JMAK model to calculate the first martensite formation of the virtual-formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The width of the slats determines the final phase composition of the virtual-formed titanium alloy sample at different test points. Determine the second [value] at each test point of the sample prepared by laser additive manufacturing under different process parameters. Phase volume fraction, second martensite Phase volume fraction and second martensite Slat width, and based on the second Phase volume fraction, second martensite Phase volume fraction and the second martensite Lath width is used to construct a microhardness prediction model between microstructure and microhardness; The final phase composition is substituted into the microhardness prediction model to predict the microhardness at different locations of the titanium alloy sample.

2. The method for predicting the microhardness of laser-added titanium alloys according to claim 1, characterized in that, The extraction of historical temperature data from multiple test points along the deposition direction inside the virtual-formed titanium alloy sample, forming a thermal cycling curve for each test point, includes: N test points are uniformly set from the substrate upwards along the center line of the deposition direction inside the titanium alloy sample. Starting from the first layer, the temperature data of the center point of the sample is extracted every five layers as the historical temperature data of the current position. For each test point, record the complete temperature history data of the temperature change over time for that test point to obtain the thermal cycling curve for each test point.

3. The method for predicting the microhardness of laser-added titanium alloys according to claim 1, characterized in that, The extraction of historical temperature data from multiple test points along the deposition direction inside the virtual-formed titanium alloy sample includes: Five test points are uniformly arranged from the substrate upwards along the center line of the deposition direction inside the titanium alloy sample, and the temperature history data of the five test points are extracted. The five test points uniformly arranged from the bottom to the top of the titanium alloy sample are layer 1, layer 25, layer 50, layer 75, and layer 95. The characteristic values ​​of the temperature history data include peak temperature, cooling rate, heating rate, effective time of phase transformation interval, and thermal accumulation effect.

4. The method for predicting the microhardness of laser-added titanium alloys according to claim 3, characterized in that, The thermal cycling curves of each test point are imported into a non-isothermal JMAK model to calculate the first martensite formation of the virtual-formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The width of the lath, forming the final phase composition of the virtual shaped titanium alloy sample at different test points, includes: The thermal cycling curve is used as the input of the non-isothermal JMAK model to establish the corresponding JMAK equation for the phase transformation behavior of the virtual formed titanium alloy sample in the laser additive manufacturing process, and to predict the evolution of the microstructure. The phase transition behavior includes: The phase transforms into acicular martensite during rapid cooling. Phase transition, and in the subsequent thermal cycle Phase decomposition and Phase formation.

5. The method for predicting the microhardness of laser-added titanium alloys according to claim 4, characterized in that, The non-isothermal JMAK model includes: 1) Cooling process The lath forming model is as follows: in, The net basket α at time n+1 w Phase volume fraction; It is the grain boundary α at time n+1. gb Phase volume fraction; k α and N α These are material dynamic parameters used to constrain the transformation of the JMAK equation; It is the phase equilibrium fraction of the α phase at time n+1, which depends on the temperature. It is the volume fraction of phase β at time n; At time n, the net basket α w Phase volume fraction; It is the grain boundary α at time n. gb Phase volume fraction; Δt expresses the actual time elapsed for the phase transition reaction in each minute interval; t c To establish a fictitious equivalent time, the initial states of the two phases before each phase transition increment are determined. Fictitious equivalent time The calculation process is as follows: 2) The model for the formation of the martensite α′ phase during the cooling process is as follows: in, It is the volume fraction of the martensite α′ phase at time n+1; T1 is the volume fraction of martensite α′ phase at time n; T2 is the temperature at time n-1; T3 is the temperature at time n+1; T4 is the temperature at time n+1; T5 is the temperature at time n+1; T6 is the temperature at time n-1; T7 is the temperature at time n+1; T8 is the temperature at time n-1; T9 is the temperature at time n+1; T1 is the temperature at time n-1; T2 is the temperature at time n+1 ms It is the martensitic initiation temperature; b KM These are the physical parameters of the materials used in the constraint equations; It is the volume fraction of phase β at time n; It is the phase equilibrium fraction of the β phase at time n+1, which depends on the temperature. 3) Martensite α′ phase recovery model during heating process: in, Let be the volume fraction of martensite α phase at time n. The volume fraction increment of the martensite α phase at time n; It is the temperature-dependent phase equilibrium fraction of the martensitic phase at time n+1; k αm and N αm These are material dynamic parameters used to constrain the transformation of the JMAK equation; It is the volume fraction of phase β at time n; It is the phase equilibrium fraction of the β phase at time n+1, which depends on the temperature. It is the increase in the volume fraction of phase β at time n; Fictional Time The calculation process is as follows: 4) Model for α-phase dissolution during heating process: In the embodiments of this application f diss (T) = 2.2 × 10 -31 T 9.89 T represents the current Kelvin temperature. The calculation process for the equivalent virtual time is as follows: The calculation process for determining whether the decomposition has ended is as follows: 5) Lath microstructure dimension calculation model: in, This indicates the lath structure dimensions at the next time step t+Δt; This represents the lath weave size at the current time t; This represents the α-phase volume fraction at the current time t; This represents the α-phase volume fraction at the next time step t+Δt; This represents the volume fraction of phase α′ at the current time t; This represents the volume fraction of phase α′ at the next time step t+Δt; and Both R and R depend on the material's calculation parameters, among which R represents the statistically average geometric width of the slats at various temperatures under equilibrium conditions, where R represents the activation temperature.

6. The method for predicting the microhardness of laser-added titanium alloys according to claim 5, characterized in that, Determining the second martensite at each test point of the laser additive manufacturing samples under different process parameters. Phase volume fraction, including: Titanium alloy samples were prepared based on the set process parameters and different scanning speeds; The microstructure morphology of the treated titanium alloy sample at different height positions was observed, and SEM images of the titanium alloy sample were obtained. Martensite in the SEM images was analyzed using image analysis software. The width of the lath was quantitatively analyzed to obtain the second martensite at different scanning speeds. Slat width.

7. The method for predicting the microhardness of laser-added titanium alloys according to claim 6, characterized in that, The one based on the second Phase volume fraction, second martensite Phase volume fraction and the second martensite The lath width is used to construct a microhardness prediction model relating microstructure and microhardness, including: Microstructure characterization and microhardness testing were performed on titanium alloy samples prepared under different process parameters to obtain matched correlation data; in the matched correlation data, the microhardness of the titanium alloy sample is correlated with the second martensite. The increase in phase volume fraction, the second The increase in phase volume fraction and the second martensite Related to slat width; The construction steps of the microhardness prediction model include: Establish the Hall-Page relationship between microhardness and the volume fraction of the second α phase: Where HV represents the microhardness obtained experimentally. The volume fraction of the second α phase obtained from the experiment. , These are the fitting coefficients; , Calculated using the least squares method; Establish the Hall-Page relationship between microhardness and the volume fraction of the second α′ phase: Where HV represents the microhardness obtained experimentally. The second one obtained from the experiment Phase volume fraction, , These are the fitting coefficients; , Calculated using the least squares method; Establishing microhardness and the second martensite The Hall-Page relation for slat width is as follows: Where HV represents the microhardness obtained experimentally. The second martensite obtained in the experiment Slat width, , These are the fitting coefficients; , Calculated using the least squares method; Establish a system based on microhardness and the volume fraction of the second α phase, the volume fraction of the second α′ phase, and the second martensite. The microhardness prediction model based on the functional relationship between slat widths is as follows: Among them, HV c The fitting coefficients are HV. c It is calculated using the least squares method.

8. The method for predicting the microhardness of laser-added titanium alloys according to claim 7, characterized in that, The phase composition of the titanium alloy sample was analyzed using electron backscatter diffraction.

9. The method for predicting the microhardness of laser-additive titanium alloys according to any one of claims 1-8, characterized in that, The dimensions of the titanium alloy sample are 2mm × 2mm × 4mm.

10. A microhardness prediction device for laser-added titanium alloys, used to implement the microhardness prediction method for laser-added titanium alloys according to any one of claims 1-9, characterized in that, include: The multiphysics simulation module is used to establish a multiphysics numerical model of the laser additive manufacturing process, simulate the multi-stage and multi-layer forming process of laser additive manufacturing of titanium alloy samples under set process parameters, extract the temperature history data of multiple test points in the deposition direction inside the virtual formed titanium alloy sample, and form a thermal cycling curve for each test point. The microstructure calculation module is used to import the thermal cycling curves of each test point into the non-isothermal JMAK model to calculate the first martensite of the virtual-formed titanium alloy sample during the additive manufacturing process. Phase volume fraction, first Phase volume fraction and first martensite The width of the slats determines the final phase composition of the virtual-formed titanium alloy sample at different test points. The microhardness prediction model construction module is used to determine the second hardness at each test point of the sample prepared by laser additive manufacturing under different process parameters. Phase volume fraction, second martensite Phase volume fraction and second martensite Slat width, and based on the second Phase volume fraction, second martensite Phase volume fraction and the second martensite Lath width is used to construct a microhardness prediction model between microstructure and microhardness; The microhardness prediction module is used to substitute the final phase composition into the microhardness prediction model to predict the microhardness at different locations of the titanium alloy sample.

Images