Method and system for predicting precipitation phase of additive aluminum alloy based on non-equilibrium phase change model

Abstract

The invention relates to the technical field of metal additive manufacturing, in particular to a method and system for predicting an additive aluminum alloy precipitation phase based on a non-equilibrium phase change model. The method comprises the steps that initial conditions are obtained, and a three-dimensional finite element model of the laser additive manufacturing process is established; the three-dimensional finite element model is operated, feature point data and a thermal cycle curve are extracted, and the feature point data comprise the solidification speed R and the temperature gradient G of the front edge of the solid-liquid interface of the molten pool; based on a rapid solidification theory, considering a solute capture effect, and calculating initial supersaturated solid solubility according to the feature point data; taking the thermal cycle curve and the initial supersaturation solid solubility as input conditions, performing numerical solution by adopting an unbalanced phase change model, and predicting a critical nucleation radius rppt * and a volume fraction fppt; and S2, verifying the critical nucleation radius and the volume fraction prediction result of the Si precipitation particles, and returning to the step S1 when the difference value is greater than a preset error. And the quantitative prediction of the size distribution and the volume fraction of the nano Si precipitated phase is realized.

Description

Technical Field

[0001] This invention relates to the field of laser additive manufacturing (LAM) technology, and particularly to a method and system for predicting precipitated phases in additive aluminum alloys based on a non-equilibrium phase transformation model. More specifically, it relates to a quantitative prediction method that integrates thermal cycling simulation and a non-isothermal aging model, and a system for feedback control of laser additive manufacturing process parameters based on the prediction results, so as to achieve precise optimization of the microstructure and properties of additive aluminum alloys. Background Technology

[0002] Laser additive manufacturing technologies (such as laser directed energy deposition (L-DED) and laser powder bed fusion (L-PBF) use high-energy laser beams to melt metal powder layer by layer, enabling the rapid forming of complex structural parts. Al-Si alloys (such as AlSi7Mg, AlSi10Mg, and Al12Si) have become the most widely used aluminum alloys in scientific research and engineering applications in laser additive manufacturing due to their good fluidity, low thermal cracking tendency, and high specific strength.

[0003] In laser additive manufacturing, the molten pool undergoes extremely high cooling rates (up to 10^6-10^8 K / s), resulting in the large-scale trapping of solute elements (mainly Si) within the α-Al matrix, forming a supersaturated solid solution. Subsequently, under the cyclic thermal effects of subsequent deposits, this supersaturated solid solution undergoes non-isothermal dynamic aging, leading to the in-situ precipitation of nanoscale precipitates. The size, distribution, volume fraction, and evolution of these precipitates significantly affect the final mechanical properties (such as strength and plasticity) of the formed part.

[0004] Current research indicates that in LAM-formed Al-Si alloys, the precipitated phase in the α-Al matrix is ​​primarily Si. Approximately 90% of these Si particles are spherical, and 10% are non-spherical (lamellar or needle-like), forming a semi-coherent interface with the α-Al matrix. Their precipitation behavior is influenced by both initial melt pool solidification conditions (solidification rate R and temperature gradient G) and subsequent thermal cycling history. However, current research is largely limited to qualitative observation of the deposited microstructure, lacking quantitative prediction of the non-isothermal precipitation kinetics of the precipitated phase, making it difficult to actively control the microstructure and mechanical properties of Al-Si alloys through process parameters.

[0005] Existing precipitate prediction models (such as the Johnson-Mehl-Avrami-Kolmogorov, JMAK, Monte Carlo, and horizontal field models) have limitations in their applicability when dealing with LAM processes, where the peak temperature is much higher than the dissolution temperature and the process involves complex non-isothermal thermal histories. The Kampmann-Wagner Numerical (KWN) non-equilibrium phase transformation model can simultaneously describe the nucleation, growth, coarsening, and dissolution processes of precipitates, and is particularly suitable for simulating non-isothermal aging processes, making it a better choice for predicting the evolution of Si precipitates during LAM. However, there is currently no quantitative prediction study on the precipitation behavior of nano-precipitates in LAM aluminum alloys, nor is there an active control mechanism based on the prediction model.

[0006] Therefore, given the rapid development of laser additive manufacturing technology and the increasingly urgent demand for high-performance aluminum alloy components, existing technologies are unable to accurately quantify and actively control the dynamic precipitation behavior of precipitated phases during the additive manufacturing process. This has become a bottleneck restricting the further application of this technology in the field of high-performance component manufacturing. To address these issues, it is urgent to develop a new method for predicting precipitated phases that closely aligns with the non-equilibrium and non-isothermal process characteristics of additive manufacturing, and to construct a real-time process control system linked to this method.

[0007] The purpose of this invention is to fill the technological gap in the key link from "quantitative prediction" to "active control". Its core innovation lies in the first deep integration of macroscopic thermal cycle simulation and microscopic non-isothermal precipitation kinetic model, which establishes a closed-loop technology system that can accurately predict and actively control the characteristics of nano-precipitated phases in additive aluminum alloys. This is of great significance for the precise design and manufacturing of component microstructure and properties. Summary of the Invention

[0008] Purpose of the invention: To provide a method and system for predicting precipitated phases in additive aluminum alloys based on a non-equilibrium phase transformation model, aiming to solve the problem in the prior art that it is impossible to quantitatively predict and actively control the precipitation behavior of nano-precipitated phases in laser additive manufacturing aluminum alloys.

[0009] Technical solution: A method for predicting precipitated phases in additive aluminum alloys based on a non-equilibrium phase transformation model includes: Step S1: Obtain initial conditions and establish a three-dimensional finite element model of the laser additive manufacturing process; Step S2: Run the three-dimensional finite element model and extract feature point data and thermal cycle curve T(t). The feature point data includes: solidification velocity R and temperature gradient G at the front of the solid-liquid interface of the molten pool. Step S3: Based on the theory of rapid solidification and considering the solute trapping effect, calculate the initial supersaturated solid solubility according to the characteristic point data. ; Step S4: Combine the thermal cycling curve T(t) and the initial supersaturated solid solubility. As input conditions, a non-equilibrium phase transition model is used for numerical solution to predict the critical nucleation radius. r ppt * and volume fraction f ppt ; Step S5: Verify the predicted results of critical nucleation radius and volume fraction of Si precipitate particles. If the difference is greater than the predetermined error, return to step S1.

[0010] Further, step S1 specifically includes: Obtain alloy composition, powder properties, and initial substrate temperature; Based on the alloy composition, powder properties and initial temperature of the substrate, a three-dimensional finite element model is established. The three-dimensional finite element model includes the substrate and the sample entity, and is used to simulate the transient thermal cycle of the LAM process. In the three-dimensional finite element model, the laser heat source and the initial process parameter set of the laser heat source are set.

[0011] Further, step S1 specifically includes: Define a laser heat source based on a double ellipsoid or Gaussian surface heat source model, and set an initial set of process parameters including laser power, scanning speed, scanning spacing, layer thickness, and scanning strategy.

[0012] Further, step S2 specifically includes: Based on the initial process parameter set of the laser heat source, a three-dimensional finite element model is run to simulate the entire additive manufacturing process and extract the solidification velocity R and temperature gradient G at the front of the solid-liquid interface of the molten pool. Extract the thermal cycle curve T(t) of specific feature points experienced during the solid simulation construction process from the temperature field evolution throughout the entire additive manufacturing process.

[0013] Furthermore, in step S3, the initial supersaturated solid solubility for: ,in, C 0 represents the nominal content of Si element, which is 10 wt.%. k r The coefficients are for unbalanced distribution. I ( P e ) is the Ivantsov function; P e The Peckle number is related to the solidification rate R and the temperature gradient G.

[0014] Furthermore, in step S4, the critical nucleation radius of the Si particles... r ppt * for: ,in, This represents the interfacial energy per unit area at the interface between the Si precipitate particles and the α-Al matrix. denoted as the molar volume of the Si particle; This represents the average solute concentration in the matrix. This represents the equilibrium solute concentration between the Si precipitate particles and the α-Al matrix. It is the gas constant; This refers to absolute temperature.

[0015] Further, in step S4, the volume fraction of the Si precipitate particles... f ppt , can be represented as: ,in, Let be the radius of the Si precipitate particles within the i-th control unit; The number of Si precipitate particles nucleated within the i-th control unit; The nominal content of Si element is 10 wt.%; This represents the average solute concentration in the matrix. The concentration of Si in the precipitated phase is given.

[0016] Further, step S5 specifically includes: The average size, size distribution, and volume fraction of Si particles in deposited samples under different process parameters were quantitatively characterized by TEM and SEM experiments. Compare the experimental results with the model prediction results in step S4. If the difference is greater than the predetermined error, return to step S1.

[0017] A system for controlling the precipitated phase of Al-Si alloys in laser additive manufacturing, comprising: Modeling module: Obtain initial conditions and establish a three-dimensional finite element model of the laser additive manufacturing process; Parameter extraction module: Run the three-dimensional finite element model to extract feature point data and thermal cycle curve T(t). The feature point data includes: solidification velocity R and temperature gradient G at the front of the solid-liquid interface of the molten pool. Calculation module: Based on the theory of rapid solidification and considering the solute trapping effect, it calculates the initial supersaturated solid solubility based on characteristic point data. ; Prediction module: This module combines the thermal cycling curve T(t) and the initial supersaturated solid solubility. As input conditions, a non-equilibrium phase transition model is used for numerical solution to predict the critical nucleation radius. r ppt* and volume fraction f ppt ; Comparison and verification module: Verifies the predicted results of critical nucleation radius and volume fraction of Si precipitate particles. If the difference is greater than the predetermined error, it returns to the modeling module.

[0018] Beneficial effects: 1. High accuracy in quantitative prediction: For the first time, the non-equilibrium phase transformation model has been successfully applied to the quantitative simulation of the non-isothermal dynamic aging process of LAM aluminum alloy. It can accurately predict the critical nucleation size and final volume fraction of nano-Si precipitate phase with a prediction error of less than 5%, overcoming the limitations of traditional isothermal aging models in this field.

[0019] 2. Clear physical mechanism: The model profoundly reveals the sequential relationship between "rapid solidification to form a supersaturated solid solution" and "non-isothermal aging caused by reciprocating thermal cycles" in the LAM process, and clarifies that the precipitate phase undergoes a complex kinetic process of "repeated nucleation-growth-dissolution".

[0020] 3. Strong proactive control capability: It breaks through the limitation of only being able to observe the structure after the fact. By establishing a quantitative mapping relationship between process parameters and precipitate phase characteristics, it is possible to deduce the optimal process window required to obtain a specific target structure (such as finer particles or higher particle fraction), realizing a leap from "passive forming" to "proactive design".

[0021] 4. High system integration: It provides a complete system solution from simulation prediction, optimization decision-making to execution control. The modules work together, and the process planning can be carried out offline or adaptively adjusted online, which greatly improves the process controllability and microstructure-property consistency of LAM aluminum alloy.

[0022] 5. Good versatility: The method and core framework of the system are not only applicable to alloys such as AlSi10Mg, AlSi7Mg, and Al12Si, but can also be extended to other additive manufacturing alloy systems with precipitation strengthening effect (such as Sc / Zr modified Al alloys, some high-temperature alloys, etc.) after appropriate model parameter adjustments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1: Schematic diagram of finite element model: (a) Establishment of deposition sample model; (b) Extraction of thermal cycling temperature field information.

[0025] Figure 2 Thermal cycling curves at the center point of different sedimentary layers (layers 1, 25, 50, and 75): (a) Temperature field curves experienced by the center point of sedimentary layers 1, 25, 50, and 75; (b) Enlarged view of the temperature field curve experienced by the center point of layer 50; (c) Thermal input curve of the laser heat source to the center point of layer 50 when scanning and moving in layer 51.

[0026] Figure 3 Flowchart of non-equilibrium phase transition model calculation.

[0027] Figure 4 : Si element solid solution content in α-Al matrix: (a) Solid solution content in the initial solidified matrix ( ); (b) Solid solution content in the matrix of the molded sample ( ).

[0028] Figure 5 : Flowchart of the prediction and control method provided in the embodiments of the present invention.

[0029] Figure 6 Comparison of tensile properties of deposited specimens (Sample-1, 2, 3) with three different process parameters: (a) Engineering stress-strain curves; (b) True stress-strain curves. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The features and illustrative embodiments of various aspects of the present invention will now be described in detail. Numerous specific details are set forth in the following detailed description to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be practiced without requiring some of these specific details. The following description of embodiments is merely intended to provide a better understanding of the invention by illustrating examples of the invention. The invention is by no means limited to any specific setups and methods set forth below, but covers any improvements, substitutions, and modifications to structures, methods, and devices without departing from the spirit of the invention. Well-known structures and techniques are not shown in the drawings and the following description to avoid unnecessarily obscuring the invention.

[0032] In the description of this invention, it should be noted that the directions or positional relationships indicated by terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing and simplifying the invention, and should not be construed as limiting the invention. Furthermore, the use of ordinal numbers (e.g., "first and second," etc.) is for distinguishing objects and is not limited to this order, and should not be construed as indicating or implying relative importance.

[0033] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly, encompassing both direct connection and indirect connection via an intermediate medium. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances.

[0034] It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other, and the various embodiments can be referenced and cited in each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0036] The purpose of this invention is to fill the technological gap in the key link from "quantitative prediction" to "active control". Its core innovation lies in the first deep integration of macroscopic thermal cycle simulation and microscopic non-isothermal precipitation kinetic model, which establishes a closed-loop technology system that can accurately predict and actively control the characteristics of nano-precipitated phases in additive aluminum alloys. This is of great significance for the precise design and manufacturing of component microstructure and properties.

[0037] This invention provides a method and system for predicting precipitated phases in additive aluminum alloys based on a non-equilibrium phase transformation model, to achieve: 1. Accurately quantify and predict key characteristic parameters such as critical nucleation size and volume fraction of Si precipitate phase in α-Al matrix during LAM process.

[0038] 2. Based on the prediction results, an optimization strategy for laser process parameters (such as laser power, scanning speed, substrate temperature, and scanning strategy) is developed.

[0039] 3. Ultimately, it achieves the collaborative design and active control of the microstructure (precipitated phase) and macroscopic mechanical properties of the formed parts.

[0040] This invention uses high-fidelity three-dimensional transient temperature field finite element simulation to accurately capture the non-isothermal thermal cycle history experienced at specific locations during additive manufacturing. Combined with rapid solidification solute capture theory, it quantitatively calculates the initial supersaturated solid solubility of the α-Al matrix. It innovatively introduces and modifies a non-equilibrium phase transformation kinetic model, deeply integrating it with the aforementioned initial thermal-mass conditions. A numerical iterative approach is used to solve the entire process of nucleation, growth, coarsening, and dissolution of the precipitate phase in the complex thermal history, ultimately achieving quantitative prediction of the critical size and volume fraction of the nano-Si precipitate phase. Based on this prediction model, a closed-loop control system integrating process reverse optimization, multi-objective decision-making, and online control is further constructed. This system can proactively generate the optimal set of process parameters based on the target mechanical properties and drive the manufacturing equipment to execute them. This overturns the traditional passive manufacturing mode of "trial and error-testing," realizing the proactive design and precise manufacturing of the microstructure and mechanical properties of LAM aluminum alloys, which is of great significance for promoting the performance customization of high-end key components.

[0041] This invention provides a method and feedback control system for predicting precipitated phases in LAM aluminum alloys, comprising the following steps: Step S1: Obtain initial conditions and establish a three-dimensional finite element model of the laser additive manufacturing process. 1. Obtain the alloy composition (e.g., the nominal composition of AlSi10Mg), powder properties, and initial substrate temperature.

[0042] 2. Based on the alloy composition (such as the nominal composition of AlSi10Mg), powder characteristics and initial temperature of the substrate, a three-dimensional finite element (FE) model is established. The three-dimensional finite element (FE) model includes the substrate and the sample entity, and is used to simulate the transient thermal cycling of the LAM process.

[0043] 3. Set up the laser heat source (such as a double ellipsoidal heat source or a Gaussian surface heat source) and the initial process parameter set of the laser heat source (laser power P, scanning speed V, scanning spacing H, layer thickness D) in the three-dimensional finite element (FE) model.

[0044] Step S2: Run the three-dimensional finite element (FE) model to extract feature point data and thermal cycle curve T(t), where the feature point data includes: solidification velocity R and temperature gradient G at the front of the solid-liquid interface of the molten pool; 1. Based on the initial process parameters of the laser heat source, run a three-dimensional finite element (FE) model to simulate the entire additive manufacturing process and extract the solidification velocity R and temperature gradient G at the front of the solid-liquid interface of the molten pool.

[0045] 2. Extract the thermal cycling curve T(t) of specific feature points (such as the center point of each deposition layer) during the solid simulation construction process from the temperature field evolution throughout the entire additive manufacturing process. This curve records the repeated temperature surges and drops that occur at this point during the melting of the current layer and the deposition of dozens of subsequent layers. The extracted feature point's thermal cycling curve T(t) refers to the complete temperature-time history data, including multiple temperature surges and drops, experienced by the deposited layer during the deposition of dozens of subsequent layers.

[0046] Step S3: Based on the theory of rapid solidification and considering the solute trapping effect, calculate the initial supersaturated solid solubility according to the characteristic point data.

[0047] 1. Based on the theory of rapid solidification and considering the solute trapping effect, calculate the initial supersaturated solid solubility of Si in the α-Al matrix after rapid solidification of the molten pool. The following equation is used: (1) In the formula, C 0 represents the nominal content of Si element, which is 10 wt.%. k r The coefficients are for unbalanced distribution. I ( P e ) is the Ivantsov function; P e The Peckle number is related to the solidification rate R and the temperature gradient G.

[0048] Step S4: Combine the thermal cycling curve T(t) and the initial supersaturated solid solubility. As input conditions, a non-equilibrium phase transition model is used for numerical solution to predict the critical nucleation radius. r ppt * and volume fraction f ppt : 1. Utilizing initial supersaturated solid solubility Calculate the critical nucleation radius of Si precipitate particles using the thermal cycling curve T(t). : (2) In the formula, This represents the interfacial energy per unit area at the interface between the Si precipitate particles and the α-Al matrix. denoted as the molar volume of the Si particle; This represents the average solute concentration in the matrix. This represents the equilibrium solute concentration between the Si precipitate particles and the α-Al matrix. It is the gas constant; This refers to absolute temperature.

[0049] 2. Utilizing the initial supersaturated solid solubility Calculate the volume fraction of Si precipitate particles using the thermal cycling curve T(t). : A continuity equation describing the evolution of particle number density for different size groups was established using the control volume method, and then discretely solved. The non-isothermal thermal cycle T(t) extracted in step S2 was used as the temperature input to the non-equilibrium phase transition model as the thermodynamic initial condition, coupled with the calculations in step S3. As the initial condition for composition kinetics, an explicit difference method is used for iterative solution until the matrix solid solubility is reached. C Si Reduced to the measured solid solubility in the sedimentary state The output is the volume fraction of Si precipitate particles. f ppt , can be represented as: (3) In the formula, Let be the radius of the Si precipitate particles within the i-th control unit; The number of Si precipitate particles nucleated within the i-th control unit; The nominal content of Si element is 10 wt.%; This represents the average solute concentration in the matrix. The concentration of Si in the precipitated phase is given.

[0050] The relevant parameters used in the calculation are shown in Table 1: Table 1. Parameters used for calculating the critical nucleation radius and volume fraction of Si precipitate particles.

[0051] Step S5: Verify the predicted results of critical nucleation radius and volume fraction of Si precipitate particles. If the difference is greater than the predetermined error (10%), return to step S1. 1. The average size and volume fraction of Si precipitate particles in deposited samples under different process parameters (especially different cooling rates / scanning rates) were quantitatively characterized by TEM and SEM experiments.

[0052] 2. Compare the experimental results with the model prediction results in step S4. If the difference is greater than the predetermined error (10%), return to step S1.

[0053] 3. Based on the validated model, systematically study the effects of key process parameters (such as laser power P, scanning speed V, and substrate preheating temperature T) on the predicted precipitate phase characteristics. r ppt * , f ppt To investigate the influence of process parameters, thermal history, and precipitate phase characteristics, a quantitative relationship database was established.

[0054] Step S6: Establishment of Additive Manufacturing Aluminum Alloy Precipitation Phase Control System 1. Data Processing and Modeling Module: Used to input material parameters, geometric models, and initial process parameters; perform finite element simulation of the temperature field; extract the solidification velocity R and temperature gradient G at the solid-liquid interface front of the molten pool, as well as the thermal cycle data T(t) of characteristic points in each layer of the deposition layer; calculate the initial non-equilibrium solid solubility. .

[0055] 2. KWN Prediction Module: Includes a built-in non-equilibrium phase transition model solver. Receives T(t) and... from the upstream module. Data, run numerical calculations, and output the predicted precipitate phase size distribution ( r ppt * ) and volume fraction ( f ppt ).

[0056] 3. Knowledge Base and Optimization Decision Module: Stores verified process parameters, thermal history, and precipitate phase characteristic relationship data. Receives output from the KWN prediction module or user-defined target performance indicators (such as the required target intensity). f ppt Based on a preset artificial neural network optimization algorithm, inverse optimization is performed to recommend one or more sets of optimized process parameters (laser power P, scanning speed V, scanning spacing H, layer thickness D, etc.) that can achieve the target microstructure.

[0057] 4. Control Execution Module: This module sends the optimized process parameter set generated by the optimization decision module to the control system of the additive manufacturing equipment, driving the actuators such as the laser, galvanometer, powder feeder, and substrate heater to complete the subsequent printing process with the new parameters.

[0058] 5. Optional online monitoring and feedback module: Integrates molten pool monitoring (high-speed camera, infrared thermal imager) and / or interlayer cooling rate monitoring system. Real-time monitoring data (such as actual cooling rate) is fed back to the data processing and modeling module for real-time correction of the FE model or non-equilibrium phase change model input, achieving adaptive closed-loop control.

[0059] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0060] Step S1: Initial Condition Acquisition and Modeling 1. Materials: Gas-atomized AlSi10Mg alloy powder; substrate is 6061 aluminum alloy.

[0061] 2. Finite element geometric model: Establishing such as Figure 1 The three-dimensional finite element model shown has a substrate size of 100 × 100 × 20 mm. 3 The sample size is 70 × 70 × 15 mm. 3 (100 layers), consistent with the actual size of the deposited sample.

[0062] 3. Input parameters, as shown in Table 2: Table 2 Key process parameters for molded specimens

[0063] 4. Heat source model: A double ellipsoid or Gaussian surface heat source model is adopted.

[0064] Step S2: Thermal history calculation and feature point extraction 1. Run transient heat conduction finite element analysis to calculate the temperature field throughout the entire construction process, such as... Figure 1 As shown.

[0065] 2. Extract the solidification rate R and temperature gradient G at the solid-liquid interface front of the molten pool.

[0066] 3. Extract the temperature-time history data T(t) from the center points of layers 1, 25, 50, and 75, as follows: Figure 2 As shown, all points undergo dozens of intense thermal cycles, with peak temperatures exceeding the solidus line and even the melting point, before rapidly cooling to near the substrate temperature.

[0067] Step S3: Calculation of non-equilibrium solid solubility 1. Based on the cooling rates of each sample obtained in step S2, the initial Si solid solubility was calculated using equation (1). The calculations yielded the values ​​for Sample-1, 2, and 3. The values ​​were 1.96 wt.%, 3.09 wt.%, and 4.32 wt.%, respectively.

[0068] Step S4: Solving the non-equilibrium phase transition model 1. Set the parameters of the non-equilibrium phase transition model (see Table 1), such as interface energy, Si diffusion coefficient, and Si equilibrium solid solubility.

[0069] 2. Combine T(t) obtained in step S2 with T(t) obtained in step S3. As input, it is substituted into the non-equilibrium phase transition model (see calculation process). Figure 3 Solve for it.

[0070] 3. When the calculation reaches the point where the matrix solid solubility decreases to the level measured by TEM-EDS... (Sample-1: 0.19 wt.%, Sample-2: 0.45 wt.%, Sample-3: 0.51 wt.%) (e.g.) Figure 4 As shown), the calculation terminates.

[0071] 4. Output prediction results: Critical nucleation radius of Si precipitate phase r ppt * and volume fraction f ppt .

[0072] Step S5: Model Validation and Relationship Establishment 1. The average size and volume fraction of Si particles in three groups of samples were measured by TEM bright-field imaging and image statistical analysis.

[0073] 2. Comparing the predicted values ​​with the measured values ​​(see Figure 3 and Table 4), it can be seen that the two are in good agreement, with the error within 5%, which verifies the accuracy of the model.

[0074] Table 3 Critical size of precipitated particles ( Comparison of predicted results and experimental results

[0075] Table 4 Volume fraction of precipitated particles ( Comparison of predicted results and experimental results

[0076] Step S6: Closed-loop control printing and mechanical property testing 1. Molten Pool Monitoring Unit: This unit must include at least one coaxially integrated high-speed camera (frame rate not less than 10kHz) and one infrared thermal imager. The high-speed camera captures the morphology, brightness, and fluctuations of the molten pool; extracted features such as the molten pool area and aspect ratio can be used to indirectly assess the cooling rate. The infrared thermal imager simultaneously measures the absolute temperature field and temperature gradient (G) and solidification rate (R) of the molten pool and its surrounding area.

[0077] 2. Interlayer cooling monitoring unit: may include a non-contact infrared thermometer, used to scan and measure the temperature distribution of the deposited surface after each layer of deposition and before the next layer of powder is laid, so as to obtain the overall cooling process of the layer.

[0078] 3. Feedforward correction mode: The measured cooling rate is used as a new input to refresh the initial supersaturated solid solubility in step S3. The calculation provides initial conditions for the KWN prediction module that are closer to the current actual state.

[0079] 4. Feedback Correction Mode: The actual interlayer temperature T is compared with the predicted value from the finite element simulation. By using a model-based control algorithm (such as PID control algorithm), the boundary conditions (such as equivalent substrate temperature) or heat source parameters in the finite element model are adjusted in reverse, so that the predicted value of the model continuously approaches the measured value, thereby realizing online calibration of the finite element model and improving its prediction accuracy for the next moment.

[0080] 5. Through the above method, the system constitutes a closed-loop negative feedback control system of "sensing-prediction-decision-execution-re-sensing," which can significantly reduce the impact of disturbances such as powder characteristic fluctuations, laser power drift, and environmental changes on the final microstructure, ensuring that even during long-term printing processes, highly consistent and expected precipitated phase characteristics and component mechanical properties (such as...) can be obtained. Figure 5 (As shown).

[0081] 6. Obtain deposited aluminum alloy specimens with the target mechanical properties, including room temperature tensile properties as follows: Figure 6 As shown.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of predicting additive aluminum alloy precipitates based on a non-equilibrium phase transformation model, characterized by, The method comprises the following steps: Step S1: obtaining initial conditions and establishing a three-dimensional finite element model of a laser additive manufacturing process; Step S2: running the three-dimensional finite element model, extracting feature point data and a thermal cycle curve T(t), wherein the feature point data comprises a solid-liquid interface front solidification velocity R and a temperature gradient G; Step S3: Based on the rapid solidification theory, considering the solute trapping effect, the initial supersaturation solid solubility is calculated according to the characteristic point data ; Step S4: applying the thermal cycle curve T(t) and the initial supersaturation solid solubility As input conditions, the non-equilibrium phase transition model is used to predict the critical nucleation radius r ppt * and volume fraction f ppt ; Step S5: verifying the prediction results of the critical nucleation radius and the volume fraction of Si precipitation particles, and returning to step S1 if the difference is greater than a predetermined error.

2. The method of claim 1, wherein, Step S1 specifically comprises: Obtaining alloy composition, powder characteristics and substrate initial temperature; According to the alloy composition, powder characteristics and substrate initial temperature, a three-dimensional finite element model is established, which contains a substrate and a sample entity, and is used to simulate the transient thermal cycle of the LAM process; In the three-dimensional finite element model, a laser heat source and an initial process parameter set of the laser heat source are set.

3. The method of claim 1, wherein, Step S1 specifically comprises: Defining a laser heat source based on a double-ellipsoid or Gaussian surface heat source model, and setting an initial process parameter set including laser power, scanning speed, scanning spacing, layer thickness and scanning strategy.

4. The method of claim 1, wherein, Step S2 specifically comprises: According to the initial process parameter set of the laser heat source, the three-dimensional finite element model is run to simulate the entire additive manufacturing process, and the solid-liquid interface front solidification velocity R and the temperature gradient G are extracted. From the temperature field evolution in the entire additive manufacturing process, the thermal cycle curve T(t) experienced by specific feature points in the entity simulation construction process is extracted.

5. The method of claim 1, wherein, In step S3, the initial supersaturation solid solubility is: wherein C 0 is the nominal composition content of the Si element 10 wt.%; k r is the non-equilibrium partition coefficient; I ( P e ) is the Ivantsov function; P e is the Peclet number related to the solidification velocity R and the temperature gradient G.

6. The method of claim 5, wherein, In step S4, the critical nucleation radius of Si particles r ppt * is: wherein, is the unit area interface energy of the interface between the Si precipitated particles and the α-Al matrix; is the molar volume of the Si particles; is the average solute concentration of the matrix; is the equilibrium solute concentration of the Si precipitated particles and the α-Al matrix; is the gas constant; is the absolute temperature.

7. The method of claim 1, wherein, In step S4, the volume fraction of Si precipitate particles f ppt may be expressed as: wherein, is the radius of the Si precipitate particles in the i-th control unit; is the number of nucleation of the Si precipitate particles in the i-th control unit; is the nominal composition content of the Si element 10 wt.%; is the average solute concentration of the matrix; is the concentration of the precipitate phase Si.

8. The method of claim 7, wherein, Step S5 specifically comprises: Through TEM and SEM experimental means, the average size, size distribution and volume fraction of Si particles in the deposited sample under different process parameters are quantitatively characterized; The experimental results are compared with the model prediction results of step S4, and if the difference is greater than a predetermined error, the method returns to step S1.

9. A system for regulating precipitates of laser additive manufactured Al-Si alloys, characterized in that, The method comprises the following steps: Modeling module: obtaining initial conditions and establishing a three-dimensional finite element model of a laser additive manufacturing process; Parameter extraction module: running the three-dimensional finite element model, extracting feature point data and a thermal cycle curve T(t), wherein the feature point data comprises a solid-liquid interface front solidification velocity R and a temperature gradient G; Calculation module: based on the theory of rapid solidification, considering the solute trapping effect, according to the characteristic point data to calculate the initial supersaturation solid solubility ; Prediction module: from the thermal cycle curve T(t) and the initial supersaturation solid solubility As input conditions, the non-equilibrium phase transition model is used to predict the critical nucleation radius r ppt * And the volume fraction f ppt ; Comparison and verification module: verifying the prediction results of the critical nucleation radius and the volume fraction of Si precipitation particles, and returning to the modeling module if the difference is greater than a predetermined error.

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