Multi-scale prediction method and system for microstructure of ultrasonic-assisted friction stir welding

Abstract

The present invention belongs to the field of friction stir welding, and provides a multi-scale prediction method and system for the microstructure of an ultrasonic-assisted friction stir weld, including obtaining the temperature, strain and strain rate in the friction stir welding process based on a macroscopic prediction model of friction stir welding; establishing a microstructure evolution model based on the characteristics of dynamic recrystallization and grain growth of weld metal, considering the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries; simulating the evolution process of the microstructure of the friction stir weld using the microstructure evolution model according to the temperature, strain and strain rate in the friction stir welding process, predicting the distribution and change process of grain size and microstructure in the welding process under different process parameters according to the evolution results, and guiding the optimization control of the welding process. The method of the present invention can realize the rapid prediction of the microstructure of the ultrasonic-assisted friction stir weld of aluminum alloy.

Description

Technical Field

[0001] The invention belongs to the technical field of friction stir welding, and in particular relates to a multi-scale prediction method and system for the microstructure of an ultrasonic-assisted friction stir weld. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Friction stir welding is a solid phase joining technology invented by the British Welding Institute in 1991. Compared with traditional fusion welding, the welding heat input is low, the material does not undergo a melting process, and defects such as pores, thermal cracks, and slag inclusions can be effectively avoided. In addition, it also has the advantages of low residual stress in the welded joint, small deformation of the workpiece after welding, high joint strength, and green and environmentally friendly welding process. It is particularly suitable for the connection of lightweight metals such as aluminum alloys and magnesium alloys.

[0004] The microstructure of the friction stir weld metal directly affects the service performance of the welded joint. During the welding process, the metal in the weld achieves metallurgical bonding through diffusion and dynamic recrystallization. Therefore, studying the grain structure, distribution and evolution of the weld metal during friction stir welding is helpful to optimize the process parameters and regulate the microstructure and properties of the welded joint, which is of great significance for the engineering application of friction stir welding.

[0005] It is difficult to observe the dynamic recrystallization process in stir friction welding in real time by experimental means. Accurately predicting the dynamic recrystallization process of materials in stir friction welding is the premise for predicting joint performance and optimizing and controlling joint performance. However, it is difficult to accurately predict the microstructure of stir friction welds by simple experimental methods, which is one of the key factors limiting the regulation of joint organization and performance. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a multi-scale prediction method and system for the microstructure of ultrasonic-assisted friction stir welds. The present invention can predict the microstructure morphology of aluminum alloy friction stir welds by combining macro and micro models.

[0007] According to some embodiments, a first solution of the present invention provides a multi-scale prediction method for the microstructure of an ultrasonic-assisted friction stir weld, using the following technical solution:

[0008] The multi-scale prediction method of ultrasonic-assisted friction stir weld microstructure includes:

[0009] Based on the macro prediction model of friction stir welding, the temperature, strain and strain rate during the friction stir welding process are obtained;

[0010] Based on the characteristics of dynamic recrystallization and grain growth of weld metal, the microstructure evolution model is established by considering the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries.

[0011] According to the temperature, strain and strain rate in the friction stir welding process, the microstructure evolution model is used to simulate the evolution of the microstructure of the friction stir weld, and the distribution and change process of the grain size and microstructure in the welding process under different process parameters are predicted.

[0012] Furthermore, the friction stir welding macro prediction model obtains the temperature, strain and strain rate during the friction stir welding process, specifically:

[0013] According to the actual shape characteristics and relevant dimensions of the welding workpiece and stirring head, a welding numerical model identical to the actual working conditions is established;

[0014] The welding numerical model is imported into the friction stir welding macro prediction model, and meshing is performed to obtain the mesh model of the workpiece;

[0015] Boundary conditions are set according to the actual process parameters and working environment, while considering the influence of ultrasonic action, including ultrasonic thermal effect and softening effect:

[0016] Perform numerical analysis to calculate temperature, strain, and strain rate during welding.

[0017] Furthermore, the ultrasonic softening effect is that ultrasonic vibration reduces the deformation activation energy, and the ST constitutive equation is modified to establish a material constitutive model including ultrasonic softening effect;

[0018] The ultrasonic thermal effect is the frictional heat generation at the contact point between the ultrasonic tool head and the workpiece.

[0019] Furthermore, the microstructure evolution model includes a grain growth model and a dynamic recrystallization model, in which the uneven distribution model of deformation storage energy, the pinning effect of the precipitated phase on the grain boundary and the ultrasonic hardening effect are considered;

[0020] The grain growth model discretizes the simulation system according to the MC-Potts model, and each unit is randomly assigned an orientation, and the unit energy is determined by the Hamilton function term and the deformation storage energy term;

[0021] The dynamic recrystallization model first consumes the deformation storage energy through nucleation. If the nucleation process cannot completely consume the excess deformation storage energy, the deformation storage energy is further consumed through the growth of dynamic recrystallization grains and nuclei until the deformation storage energy in the system is less than the storage energy of the material in the rheological state, and the dynamic recrystallization process ends.

[0022] Furthermore, the uneven distribution model of deformation storage energy takes into account the influence of grain size and grain boundary on deformation storage energy, and sets the storage energy position coefficient K for the grid cells at different positions inside the grain, specifically:

[0023]

[0024] Where nn is the number of grid cells adjacent to grid cell j. In the quadrilateral grid division system, nn is 8, Q j is the orientation number of grid cell j, is the orientation number of the grid cells adjacent to grid cell j, δ is the δ function, when hour, when hour, Thus, the deformation storage energy distribution of a grid unit j in a grain simulation system before the dynamic recrystallization stage can be obtained, specifically:

[0025] ΔH j =ΔU·λ 2 ·(1-d j / d m )·K

[0026] Where ΔU is the average increment of deformation storage energy per unit area in the simulation system, λ is the actual size of the grid unit, and d j is the diameter of the grain where the grid unit is located, d m is the average equivalent diameter of the grains in the simulation system.

[0027] Furthermore, the pinning effect of the precipitated phase on the grain boundary is to give a special orientation number to the precipitated phase unit, the orientation number is fixedly set to 1, and the orientation number of the grid unit of the normal grain ranges from 2≤Q i ≤Q, the Hamiltonian function form of general grain growth is corrected, Q i is the orientation number of the randomly selected unit i, and Q is the maximum orientation number.

[0028] Furthermore, the ultrasonic hardening effect is to consider the influence of the sonic hardening effect on the microstructure evolution of the grains of the friction stir welding joint, and to correct the dislocation density to obtain the corrected dislocation generated by work hardening, specifically:

[0029]

[0030] Among them, ρ h is the dislocation caused by work hardening, ε is the shear strain, k 1 is the dislocation storage coefficient, P 0 and P are The initial and saturation values ​​of t vis the duration of ultrasonic vibration, is the proportionality coefficient, ξ is the ultrasonic amplitude, ρ d is the dislocation density.

[0031] According to some embodiments, a second solution of the present invention provides a multi-scale prediction system for the microstructure of an ultrasonic-assisted friction stir weld, which adopts the following technical solution:

[0032] The multi-scale prediction system of ultrasonic-assisted friction stir welding microstructure includes:

[0033] A macro prediction module is configured to obtain temperature, strain and strain rate during the friction stir welding process based on the friction stir welding macro prediction model;

[0034] The micro-prediction model building module is configured to build a microstructure evolution model based on the characteristics of dynamic recrystallization and grain growth of weld metal, taking into account the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries;

[0035] The prediction module is configured to simulate the evolution of the microstructure of the stir friction weld using a microstructure evolution model based on the temperature, strain and strain rate during the stir friction welding process, and predict the distribution and change process of the grain size and microstructure during the welding process under different process parameters.

[0036] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the multi-scale prediction method for the microstructure of an ultrasonic-assisted friction stir weld as described in the first aspect above.

[0038] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0039] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the multi-scale prediction method of the microstructure of the ultrasonic-assisted friction stir weld as described in the first aspect above are implemented.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention combines macro and micro models. The macro model can use software such as Deform, Fluent, folw-3D, ABAQUS, etc. to calculate the temperature, strain, and strain rate in the aluminum alloy stir friction welding process, and use them as input parameters of the micro model; the micro model can be implemented using Matlab, and the microstructure morphology in the aluminum alloy stir friction weld can be obtained, aiming to provide guidance for the organizational performance analysis of the welded joint.

[0042] The present invention sets the position constants of different grid units by considering the uneven distribution of deformation storage energy during material deformation. The purpose of this storage energy model is to consider the influence of grain size and grain boundary on deformation storage energy and obtain more accurate organizational evolution results.

[0043] The establishment of the precipitate phase pinning grain boundary model of the present invention takes into account the influence of the precipitate phase on the grain boundary migration and limits the orientation conversion of the precipitate phase during general grain growth. The result is more accurate and can more comprehensively establish the connection between process parameters and microstructure.

[0044] The present invention utilizes a computer to implement multi-scale analysis of the microstructure in the aluminum alloy friction stir weld, quantitatively predicts the grain size and morphology distribution characteristics at different positions of the weld joint, has the characteristics of low cost, no pollution, and high efficiency, and can reduce the experimental workload and unnecessary material waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0046] Figure 1 Flow chart of a multi-scale prediction method for the microstructure of a friction stir weld according to an embodiment of the present invention;

[0047] Figure 2 A mesh model of the friction stir welding process in an embodiment of the present invention;

[0048] Figure 3 It is the basic process of Monte Carlo dynamic recrystallization simulation in the embodiment of the present invention;

[0049] Figure 4 A schematic diagram of determining the storage energy position coefficient K in a simulation system in an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of determining the orientation number of the precipitated phase in an embodiment of the present invention;

[0051] Figure 6 Schematic diagram of extraction position C1 of welding temperature, strain and strain rate in an embodiment of the present invention;

[0052] Figure 7 is the initial grain morphology simulated by MC in the embodiment of the present invention;

[0053] Figure 8 A schematic diagram of the position of extracting the simulation result of the weld nugget zone in an embodiment of the present invention;

[0054] Fig. 9 The microstructure prediction result of the weld nugget zone of conventional friction stir welding under the process parameters of 1000rpm-120mm / min in the embodiment of the present invention;

[0055] Fig.10 The microstructure prediction result of the weld nugget zone of ultrasonic assisted friction stir welding under the process parameters of 1000rpm-120mm / min in the embodiment of the present invention;

[0056] Fig.11 This is a comparison diagram of the grain size distribution at the weld nugget zone C1 position after conventional friction stir welding and ultrasonic-assisted friction stir welding under the process parameters of 1000rpm-120mm / min in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0058] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0059] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0060] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0061] Embodiment 1

[0062] This embodiment provides a multi-scale prediction method for the microstructure of an ultrasonic-assisted friction stir weld.

[0063] In this embodiment, the method includes the following steps:

[0064] Based on the macro prediction model of friction stir welding, the temperature, strain and strain rate during the friction stir welding process are obtained;

[0065] Based on the characteristics of dynamic recrystallization and grain growth of weld metal, the microstructure evolution model is established by considering the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries.

[0066] According to the temperature, strain and strain rate in the friction stir welding process, the microstructure evolution model is used to simulate the evolution of the microstructure of the friction stir weld, and the distribution and change process of the grain size and microstructure in the welding process under different process parameters are predicted.

[0067] It can be understood that the real-time observation of the dynamic recrystallization process in stir friction welding is mainly the observation of the dynamic recrystallization process during the welding process, and then the welding process is regulated according to the simulation results of the organization.

[0068] The aluminum alloy friction stir welding macro prediction model includes: meshing, setting boundary conditions, and the model simultaneously considers the influence of ultrasonic action, which includes ultrasonic thermal effect and ultrasonic softening effect.

[0069] That is to say, the macro prediction model mentioned here can use software such as Deform, Fluent, folw-3D, ABAQUS, etc. to calculate the temperature, strain, and strain rate during the aluminum alloy stir friction welding process. However, the aluminum alloy stir friction welding macro prediction model obtained by adding the influence of ultrasonic action is not limited to the specific software used, and the appropriate software can be selected as needed.

[0070] The ultrasonic softening effect includes that ultrasonic vibration reduces the deformation activation energy, and the ST constitutive equation is modified to establish a material constitutive model including the ultrasonic softening effect.

[0071] The expression for reducing the deformation activation energy is:

[0072]

[0073] In the formula, g 0 is the pre-factor, μ m is the shear elastic modulus, b is the modulus of the Burgers vector, β and c are experimentally determined parameters, k B is the Boltzmann constant, M is the Taylor factor, E r is the sound energy density, is the threshold thermal stress, Q S is the activation energy of plastic deformation.

[0074] The modified ST constitutive equation is:

[0075]

[0076] In the formula, is the strain rate, R is the ideal gas constant, T is the absolute temperature, Q' S is the activation energy of plastic deformation, n is the stress exponent, and A and α are constants related to the material.

[0077] The ultrasonic thermal effect is:

[0078] Ignore the heat generated by plastic deformation at the contact surface between the workpiece and the ultrasonic tool head, only consider the friction heat generated, and assume that the friction heat generated is evenly distributed at the contact surface between the ultrasonic tool head and the workpiece. The ultrasonic thermal effect is mainly the friction heat generated at the contact point between the ultrasonic tool head and the workpiece, which is the setting of the thermal effect in the calculation process.

[0079] On the contact interface, the average velocity of the ultrasonic tool head vibration is expressed as:

[0080] V m =4ξ 0 fcosω

[0081] In the formula, ξ 0 is the ultrasonic amplitude, f is the ultrasonic frequency, and ω is the angle between the ultrasonic tool head and the horizontal plane. The heat generation rate at the contact interface between the ultrasonic tool head and the workpiece is:

[0082]

[0083] In the formula, μ tool is the friction coefficient between the ultrasonic tool head and the workpiece, F h is the axial clamping force applied to the ultrasonic tool head, R h is the radius of the contact interface between the ultrasonic tool head and the workpiece.

[0084] The Monte Carlo microstructure evolution model involves grain growth and dynamic recrystallization processes, and takes into account the uneven distribution of deformation storage energy, the pinning of grain boundaries by precipitated phases, and the ultrasonic hardening effect.

[0085] The grain growth model discretizes the simulation system according to the MC-Potts model. Each unit is randomly assigned an orientation, and the unit energy is determined by the Hamilton function, which is expressed as:

[0086]

[0087] In the formula, E i is the unit energy, J is the grain boundary energy constant, Q i is the orientation number of randomly selected unit i, Q jis the number of orientations of the neighboring unit j of unit i, and n is the number of neighboring units around unit i. For a quadrilateral grid system, the value of n is equal to 8. δ is the δ function, when Q i =Q j hour, When Q i ≠Q j hour,

[0088] The dynamic recrystallization model is as follows: During the plastic deformation process, work hardening causes the deformation storage energy U to increase continuously. When it is greater than the storage energy U of the material in the rheological state, sat The dynamic recrystallization process occurs. First, the deformation storage energy is consumed by nucleation. If the nucleation process cannot completely consume the excess deformation storage energy, the deformation storage energy is further consumed by the growth of dynamic recrystallization grains and nuclei until the deformation storage energy in the system is less than U sat , the dynamic recrystallization process ends.

[0089]

[0090] In the formula, ρ sat is the storage energy U under rheological stress state sat The corresponding dislocation density, ρ h is the dislocation caused by work hardening, ρ s is the dislocation consumed by dynamic recrystallization. h Expressed as:

[0091]

[0092] Where G is the shear modulus, G 0 is the unit conversion constant, M is the Taylor factor, and b is the modulus of the Burgers vector. The deformation storage energy can be calculated from the dislocation density:

[0093] U=ρ d aG

[0094] Where U is the deformation storage energy per unit volume, a is a constant describing the dislocation type, and ρ d is the dislocation density.

[0095] The nucleation process of dynamic recrystallization consists of two parts: determining the number of nuclei and determining the probability of nucleation. The nucleation rate of the final simulation system is the product of the number of nuclei and the probability of nucleation.

[0096]

[0097] In the formula, is the number of nuclei, and the average deformation storage energy of each unit is The nucleation probability Pn Determination of:

[0098]

[0099] In the formula, is the average subgrain radius. The critical subgrain radius expression is:

[0100]

[0101] Where γ is the grain boundary energy, U G is the deformation storage energy of the grain. Therefore, the nucleation rate It is the product of the number of nuclei and the probability of nucleation:

[0102]

[0103] The Hamilton function form of the grain growth of the dynamic recrystallization is:

[0104]

[0105] In the formula, ∑E S,j is the deformation storage energy term.

[0106] The uneven distribution model of deformation storage energy takes into account the influence of grain size and grain boundary on deformation storage energy. In the stage where dynamic recrystallization does not occur, the increment of deformation storage energy of a grid unit j in a certain grain simulation system can be expressed as:

[0107] ΔH j =ΔU·λ 2 ·(1-d j / d m )·K

[0108] Where ΔU is the average increment of deformation storage energy per unit area in the simulation system, λ is the actual size of the grid unit, and d j is the diameter of the grain where the grid unit is located, d m is the average equivalent diameter of the grains in the simulation system, and K is the position constant of the grid unit j in the grain. The specific expression of the K value of the grid unit at different positions inside the grain is:

[0109]

[0110] Where nn is the number of grid cells adjacent to grid cell j. In the quadrilateral grid division system, nn is 8, Q j is the orientation number of grid cell j, is the orientation number of the grid cells adjacent to grid cell j, δ is the δ function, when hour, when hour, Therefore, the storage energy distribution expression of each grid unit in the simulation system is:

[0111]

[0112] The precipitate phase pinning grain boundary model assigns a special orientation number to the precipitate phase unit, and the orientation number is fixed to 1. The orientation number of the grid unit of the normal grain ranges from 2≤Q i ≤Q, the Hamilton function form of general grain growth is corrected.

[0113] The ultrasonic hardening effect is as follows: considering the influence of the sonic hardening effect on the microstructure evolution of the grains of the friction stir welding joint, the dislocation density is corrected.

[0114]

[0115] In the formula, ρ d is the dislocation density, ε is the shear strain, k 1 is the dislocation storage coefficient, k 2 is the dislocation dynamic recovery coefficient. Assume that all additional changes caused by ultrasonic vibration are equivalently included in k 1 , k 2 The dislocation density can be expressed as:

[0116]

[0117] In the formula, are related to the additional dislocation multiplication and annihilation caused by ultrasonic vibration, respectively. Since the softening effect of ultrasound has been considered in the macroscopic numerical model, η k2 During the acoustic hardening process, the dislocation density cannot increase infinitely and there is a saturation phenomenon, which can be described by the Logistic function:

[0118]

[0119] Where P 0 and P are The initial and saturation values ​​of t v is the duration of ultrasonic vibration, is the proportionality coefficient, ξ is the ultrasonic amplitude, ρ d is the dislocation density. Therefore, the dislocations generated by the above work hardening can be expressed as:

[0120]

[0121] In view of the current problems of large workload and complex work in the microstructure detection of aluminum alloy stir friction welds, this embodiment discloses a multi-scale prediction method for the microstructure of aluminum alloy stir friction welds. Through this method, the grain size and microstructure changes of the aluminum alloy during the welding process can be directly observed.

[0122] In this embodiment, the macro and micro models are combined, and the aluminum alloy stir friction welding process is simulated and analyzed based on the macro model to obtain the change information of temperature, strain and strain rate during the welding process, and use it as the input parameter of the Monte Carlo microstructure evolution model to simulate the evolution process of the grain microstructure in each area of ​​the weld during the welding process.

[0123] The specific steps of implementing the multi-scale prediction method for the microstructure of aluminum alloy friction stir weld in this embodiment are as follows: Figure 1 As shown, the specific steps are as follows:

[0124] Step 1: Establish a CAD model that is the same as the actual working condition based on the actual workpiece (material to be welded) and the shape characteristics and related dimensions of the stirring head;

[0125] Step 2: Import the CAD model into the aluminum alloy friction stir welding macro prediction model, perform reasonable mesh division, and obtain the mesh model of the workpiece (such as Figure 2 shown);

[0126] Step 3: Set reasonable boundary conditions according to the actual process parameters and working environment, and consider the influence of ultrasonic action, including ultrasonic thermal effect and softening effect:

[0127] Step 4: Conduct numerical analysis to calculate the temperature, strain, and strain rate during welding;

[0128] Step 5: Initialize the Monte Carlo simulation, import the plastic deformation history information during the welding process, and generate the initial microstructure;

[0129] Step 6, using the Monte Carlo microstructure evolution model, simulate the grain morphology and evolution in the aluminum alloy friction stir weld;

[0130] The Monte Carlo microstructure evolution model in step 6 includes: grain growth, dynamic recrystallization model, which takes into account the uneven distribution of deformation storage energy, the pinning of grain boundaries by precipitation phases, and the ultrasonic hardening effect. The basic process of Monte Carlo dynamic recrystallization simulation (such as Figure 3 shown).

[0131] The uneven distribution model of deformation storage energy in step 6 here takes into account the influence of grain size and grain boundary on deformation storage energy. In the stage where dynamic recrystallization does not occur, the increment of deformation storage energy of a grid unit j in a certain grain simulation system can be expressed as:

[0132] ΔH j =ΔU·λ 2 ·(1-d j / d m )·K

[0133] Where ΔU is the average increment of deformation storage energy per unit area in the simulation system, λ is the actual size of the grid unit, and d j is the diameter of the grain where the grid unit is located, d m is the average equivalent diameter of the grains in the simulation system, and K is the position constant of the grid unit j in the grain. The specific expression of the K value of the grid unit at different positions inside the grain is:

[0134]

[0135] Where nn is the number of grid cells adjacent to grid cell j. In the quadrilateral grid division system, nn is 8, Q j is the orientation number of grid cell j, is the orientation number of the grid cells adjacent to grid cell j, δ is the δ function, when hour, when hour, Therefore, the storage energy distribution expression of each grid unit in the simulation system is:

[0136]

[0137] K is the position constant of the grid unit j in the grain, that is, the determination of the storage energy position coefficient K is as follows: Figure 4 As shown in the figure, taking the grain with orientation number 9 as an example, the storage energy position coefficients K corresponding to grid units 9a, 9b, 9c, and 9d are 13 / 8, 14 / 8, 11 / 8, and 8 / 8, respectively. At this time, for the entire simulation system, the storage energy distribution of a single grain is inversely proportional to its grain size, and the storage energy of small grains is greater than that of large grains; for a single grain, the storage energy at the grain boundary is higher than that inside the grain, and the storage energy at the intersection of three grain boundaries is higher than that of two grain boundaries.

[0138] In step 6, the precipitate pinning grain boundary model needs to be simplified according to the changing state of the precipitate phase in the actual welding process and the characteristics of the MC-Potts model:

[0139] 1. Ignore factors such as the size of the precipitate phase and the grain boundary morphology. In the precipitate phase pinning grain boundary model, all precipitates are uniformly defined as size-free particles. In the precipitate phase image model, the precipitate phase is displayed as a 3×3 grid unit.

[0140] 2. The influence of precipitation on the dynamic recrystallization process is not considered for the time being, and only the pinning effect of precipitation on the grain boundary during general grain growth is considered.

[0141] 3. It is believed that the precipitated phase is randomly distributed at the grain boundary during reprecipitation, and the content of the precipitated phase does not change before and after the reprecipitation process. Subsequently, in the process of simulating the pinning of the precipitated phase on the grain boundary, the content and distribution of the precipitated phase remain unchanged.

[0142] The present invention assigns a special orientation number to the precipitation phase unit, the orientation number is fixedly set to 1, and the orientation number of the grid unit of the normal grain has a value range of 2≤Q i ≤Q, the Hamilton function form of the general grain growth mentioned above is modified, and Q is the maximum value of the orientation number.

[0143] The orientation number of the precipitated phase is determined as follows: Figure 5 As shown in the figure, taking the grid unit with an orientation number of 16 adjacent to the precipitated phase as an example, this unit has a total of 6 adjacent non-precipitated phase units, of which 2 non-precipitated phase units have different orientation numbers from it. Therefore, this unit can randomly select 8 or 34 as the new orientation number, and continue to calculate the energy difference before and after the orientation transformation and the probability of orientation re-taken, to complete the orientation transformation process.

[0144] Step 7: Post-processing, statistics and storage of simulation data, and output of simulation results;

[0145] Step eight, based on the predicted results of the aluminum alloy friction stir weld microstructure, actual welding tests are carried out to verify the accuracy of the model, and the model is used to simulate the microstructure of the weld under different process parameters.

[0146] Specific Example 1

[0147] This embodiment takes 2195 aluminum-lithium alloy as an example to simulate the evolution process of the microstructure of the aluminum alloy friction stir weld.

[0148] The diameter of the stirring head shoulder is 8mm, and the inner concave angle of the shoulder is 2°; the stirring needle length is 1.8mm, the root diameter is 3.2mm, and the end diameter is 2.2mm; the aluminum alloy is 2195 aluminum-lithium alloy, and the three-dimensional geometric dimensions are 100mm (length) × 50mm (width) × 2mm (height). The welding process parameters are a rotation speed of 1000rpm and a welding speed of 120mm / min.

[0149] Using the macro prediction model of aluminum alloy friction stir welding, such as Deform, Fluent, Flow-3D, Abaqus and other finite element software, setting the material parameters, mesh division, boundary conditions and other conditions, the numerical analysis of conventional friction stir welding and ultrasonic assisted friction stir welding was carried out to extract the center C1 position of the weld during the welding process (such as Figure 6 The temperature, strain, strain rate and other information of the MC simulation are input into the Monte Carlo microstructure evolution model. Figure 7 As shown, the average grain size is 9.6 μm.

[0150] Matlab software was used to simulate the microstructure of the weld nugget zone during the friction stir welding of aluminum alloys, and the microstructure morphology simulation results of multiple positions in the deformation path from the front of the stirring needle to the back of the stirring needle were extracted. Figure 8 As shown, P9 is the re-precipitation position of the precipitated phase, and P10 is the final structure of the weld core zone at the end of welding.

[0151] Fig. 9 and Fig.10 These are the predicted results of the microstructure of the weld core zone obtained by conventional friction stir welding and ultrasonic-assisted friction stir welding. It can be seen from the figure that as the stirring head approaches, the temperature and strain rate experienced by the material increase, dynamic recrystallization occurs, and a large number of small-sized dynamic recrystallized grains are generated at the grain boundaries. When the material enters the area around the stirring head, the strain rate rapidly expands, the dynamic recrystallization area rapidly expands, and the simulation system is covered by dynamic recrystallized grains. The material in the weld core zone undergoes severe plastic deformation, the DRX process is intense, and eventually evolves into fine equiaxed crystals. The average grain size of the final microstructure of the weld core zone simulated by conventional friction stir welding is 1.52μm, and the average grain size of the final microstructure of the weld core zone simulated by ultrasonic-assisted friction stir welding is 1.36μm. Compared with conventional friction stir welding, the material undergoes a higher degree of dynamic recrystallization during ultrasonic-assisted friction stir welding, the number of recrystallization nuclei also increases significantly, and the equiaxed crystals are refined.

[0152] For a more precise comparison, Fig.11The grain size distribution at the C1 position of the weld core area after conventional friction stir welding and ultrasonic assisted friction stir welding was further statistically analyzed. It can be seen from the figure that the grains with an average size between 0.8μm and 1.8μm accounted for 69.74% in conventional friction stir welding, while the grains with an average size between 0.8μm and 1.8μm accounted for 74.89% in ultrasonic assisted friction stir welding. The grains with an average size between 1.8μm and 4.0μm accounted for about 14.82% in conventional friction stir welding, and the number of large-sized grains was significantly reduced in ultrasonic assisted friction stir welding, with only 7.78% of the grains with an average size between 1.8μm and 4.0μm. It can also be concluded that applying ultrasound during welding can refine the grains.

[0153] Embodiment 2

[0154] This embodiment provides a multi-scale prediction system for ultrasonic-assisted friction stir weld microstructure, including:

[0155] A macro prediction module is configured to obtain temperature, strain and strain rate during the friction stir welding process based on the friction stir welding macro prediction model;

[0156] The micro-prediction model building module is configured to build a microstructure evolution model based on the characteristics of dynamic recrystallization and grain growth of weld metal, taking into account the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries;

[0157] The prediction module is configured to simulate the evolution of the microstructure of the stir friction weld using a microstructure evolution model according to the temperature, strain and strain rate during the stir friction welding process, and predict the distribution and change process of the grain size and microstructure during the welding process under different process parameters based on the evolution results.

[0158] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0159] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0160] The proposed system can be implemented in other ways. For example, the system embodiment described above is only illustrative, and the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0161] Embodiment 3

[0162] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the multi-scale prediction method of the microstructure of the ultrasonic-assisted friction stir weld as described in the first embodiment above are implemented.

[0163] Embodiment 4

[0164] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the multi-scale prediction method of the microstructure of the ultrasonic-assisted friction stir weld as described in the first embodiment are implemented.

[0165] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0166] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0170] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A multi-scale prediction method for the microstructure of ultrasonic-assisted friction stir welds, characterized in that: include: Based on the macro prediction model of friction stir welding, the temperature, strain and strain rate during the friction stir welding process are obtained; Based on the characteristics of dynamic recrystallization and grain growth of weld metal, the microstructure evolution model is established by considering the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries. According to the temperature, strain and strain rate in the friction stir welding process, the evolution process of the microstructure of the friction stir weld is simulated using the microstructure evolution model. The distribution and change process of the grain size and microstructure in the welding process under different process parameters are predicted based on the evolution results to guide the optimization control of the welding process. The microstructure evolution model includes a grain growth model and a dynamic recrystallization model, in which the uneven distribution model of deformation storage energy, the pinning effect of the precipitated phase on the grain boundary and the ultrasonic hardening effect are considered; The grain growth model discretizes the simulation system according to the MC-Potts model, and each unit is randomly assigned an orientation, and the unit energy is determined by the Hamilton function term and the deformation storage energy term; The dynamic recrystallization model first consumes the deformation storage energy through nucleation. If the nucleation process cannot completely consume the excess deformation storage energy, the deformation storage energy is further consumed through the growth of dynamic recrystallization grains and nuclei until the deformation storage energy in the system is less than the storage energy of the material in the rheological state, and the dynamic recrystallization process ends. The ultrasonic hardening effect is to consider the influence of the sonic hardening effect on the evolution of the grain microstructure of the friction stir welding joint, correct the dislocation density, and obtain the corrected dislocation generated by work hardening, specifically: Among them, ρ h is the dislocation generated by work hardening, ε is the shear strain, k1 is the dislocation storage coefficient, P0 and P are The initial and saturation values ​​of t v is the duration of ultrasonic vibration, is the proportionality coefficient, ξ is the ultrasonic amplitude, ρ d is the dislocation density.

2. The multi-scale prediction method for the microstructure of the ultrasonic-assisted friction stir weld according to claim 1, characterized in that: The temperature, strain and strain rate during the friction stir welding process are obtained based on the friction stir welding macro prediction model, specifically: According to the actual shape characteristics and relevant dimensions of the welding workpiece and stirring head, a welding geometric model identical to the actual working conditions is established; The welding geometry model is imported into the friction stir welding macro prediction model, and meshing is performed to obtain a mesh model of the workpiece; Boundary conditions are set according to the actual process parameters and working environment, while considering the influence of ultrasonic action, including ultrasonic thermal effect and ultrasonic softening effect: Perform numerical analysis to calculate temperature, strain, and strain rate during welding.

3. The multi-scale prediction method for the microstructure of the ultrasonic-assisted friction stir weld according to claim 2, characterized in that: The ultrasonic softening effect is that ultrasonic vibration reduces the deformation activation energy, and the ST constitutive equation is modified to establish a material constitutive model including ultrasonic softening effect; The ultrasonic thermal effect is the frictional heat generation between the contact area of ​​the ultrasonic tool head and the workpiece.

4. The multi-scale prediction method for the microstructure of the ultrasonic-assisted friction stir weld according to claim 1, characterized in that: The uneven distribution model of deformation storage energy takes into account the influence of grain size and grain boundary on deformation storage energy, and sets the storage energy position coefficient K for the grid cells at different positions inside the grain, specifically: Where nn is the number of grid cells adjacent to grid cell j. In the quadrilateral grid division system, nn is 8, Q j is the orientation number of grid cell j, is the orientation number of the grid cells adjacent to grid cell j, δ is the δ function, when hour, when hour, Thus, the deformation storage energy distribution of a grid unit j in a grain simulation system before the dynamic recrystallization stage can be obtained, specifically: ΔH j =ΔU·λ 2 ·(1-d j / d m )·K Where ΔU is the average increment of deformation storage energy per unit area in the simulation system, λ is the actual size of the grid unit, and d j is the diameter of the grain where the grid unit is located, d m is the average equivalent diameter of the grains in the simulation system.

5. The multi-scale prediction method for the microstructure of ultrasonic-assisted friction stir weld according to claim 1, characterized in that: The pinning effect of the precipitated phase on the grain boundary is to give a special orientation number to the precipitated phase unit, and the orientation number is fixedly set to 1. The orientation number of the grid unit of the normal grain is in the range of 2≤Q i ≤Q, the Hamiltonian function form of general grain growth is corrected, Q i is the orientation number of the randomly selected unit i, and Q is the maximum orientation number.

6. Ultrasonic-assisted friction stir weld microstructure multi-scale prediction system, characterized by: include: A macro prediction module is configured to obtain temperature, strain and strain rate during the friction stir welding process based on the friction stir welding macro prediction model; The micro-prediction model building module is configured to build a microstructure evolution model based on the characteristics of dynamic recrystallization and grain growth of weld metal, taking into account the uneven distribution of deformation storage energy and the pinning effect of precipitation phase on grain boundaries; A prediction module is configured to simulate the evolution of the microstructure of the friction stir weld using a microstructure evolution model according to the temperature, strain and strain rate during the friction stir welding process, and predict the distribution and change of the grain size and microstructure during the welding process under different process parameters according to the evolution results; The microstructure evolution model includes a grain growth model and a dynamic recrystallization model, in which the uneven distribution model of deformation storage energy, the pinning effect of the precipitated phase on the grain boundary and the ultrasonic hardening effect are considered; The grain growth model discretizes the simulation system according to the MC-Potts model, and each unit is randomly assigned an orientation, and the unit energy is determined by the Hamilton function term and the deformation storage energy term; The dynamic recrystallization model first consumes the deformation storage energy through nucleation. If the nucleation process cannot completely consume the excess deformation storage energy, the deformation storage energy is further consumed through the growth of dynamic recrystallization grains and nuclei until the deformation storage energy in the system is less than the storage energy of the material in the rheological state, and the dynamic recrystallization process ends. The ultrasonic hardening effect is to consider the influence of the sonic hardening effect on the evolution of the grain microstructure of the friction stir welding joint, correct the dislocation density, and obtain the corrected dislocation generated by work hardening, specifically: Among them, ρ h is the dislocation generated by work hardening, ε is the shear strain, k1 is the dislocation storage coefficient, P0 and P are The initial and saturation values ​​of t v is the duration of ultrasonic vibration, is the proportionality coefficient, ξ is the ultrasonic amplitude, ρ d is the dislocation density.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the multi-scale prediction method of the ultrasonic-assisted friction stir weld microstructure as described in any one of claims 1 to 5 are implemented.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the multi-scale prediction method of the ultrasonic-assisted friction stir weld microstructure according to any one of claims 1 to 5 are implemented.

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