Mechanical property sampling method for integrated thin-wall complex die-casting part

Abstract

The invention relates to the field of integrated thin-wall complex die-casting parts, and discloses an integrated thin-wall complex die-casting part mechanical property sampling method which comprises the following steps: step 1, preparing a flat plate die made of the same material as the part, and carrying out basic mechanical property test on the flat plate die to obtain reference mechanical property; 2, three-point bending sample sampling is carried out at different positions of the part, three-point bending testing is carried out on the three-point bending sample, and actually-measured mechanical properties are obtained; the step 2 and the step 1 are in no order; step 3, inputting the reference mechanical property into the material card, simulating the three-point bending sample based on the material card, and adjusting parameters of the material card, so as to benchmark the actually measured mechanical property and the reference mechanical property; and step 4, training a machine learning model, wherein the input characteristics of the machine learning model are process parameters of the parts, and the target variables are material card parameters. The problem of detection blind areas of complex parts is solved.

Description

Technical Field

[0001] This invention relates to the field of integrated thin-walled complex die-cast parts, and specifically to a method for sampling the mechanical properties of integrated thin-walled complex die-cast parts. Background Technology

[0002] In die-casting engineering design, traditional engineering methods typically rely on the assumption of uniform distribution of material mechanical properties for simulation analysis and optimization design. However, during the actual die-casting process, due to differences in filling flow, cooling rate, and local solidification behavior, casting defects such as porosity and shrinkage often occur inside the casting, resulting in significant spatial heterogeneity in its mechanical properties—that is, different regions exhibit different strength, plasticity, and fracture characteristics. Ignoring this non-uniformity of property distribution during the design phase will lead to a large deviation between simulation results and measured data, severely affecting the accuracy of component reliability assessment and lightweight design.

[0003] To obtain the true performance distribution of die-cast parts, current methods rely on local sampling tests. However, this method has significant limitations: for die-cast parts with complex thin-walled and multi-ribbed structures, obtaining representative samples is difficult and the number is limited, making it difficult to comprehensively reflect the performance changes across the entire component. To overcome this bottleneck, the key lies in developing a non-destructive, global performance inversion method to replace or supplement the infeasible extensive sampling tests. Summary of the Invention

[0004] This invention aims to provide an integrated method for sampling the mechanical properties of thin-walled complex die-cast parts, in order to solve the problem of blind spots in the inspection of complex parts. To achieve the above objectives, the present invention adopts the following technical solution: a method for sampling the mechanical properties of integrated thin-walled complex die-cast parts, comprising the following steps: Step 1: Prepare a flat mold made of the same material as the part, and conduct basic mechanical property tests on the flat mold to obtain reference mechanical properties; Step 2: Take three-point bend samples at different locations on the part, perform three-point bend tests on the samples, and obtain the measured mechanical properties; Step 2 and Step 1 are not in any particular order. Step 3: Input the reference mechanical properties into the material card, simulate the three-point bend specimen based on the material card, adjust the parameters of the material card, and thus align the measured mechanical properties with the reference mechanical properties.

[0005] The beneficial effects of this plan are: 1. Since it is difficult to prepare standard samples on parts using conventional methods, this solution is to process samples required for basic mechanical property testing on a flat mold of the same material as the parts, and obtain reference material cards through simulation calculations; in order to initially solve the problem of sampling difficulties.

[0006] 2. Take three-point bend samples at different locations on the part and perform three-point bend tests to obtain measured mechanical properties. Simulate the three-point bend samples based on the reference material card and benchmark all samples by adjusting the parameters in the material card.

[0007] The simulation results include mechanical property information at various locations, but the accuracy of the data is not high; the test results of the three-point bend specimen are accurate, but the number of specimens is insufficient; therefore, by benchmarking the two sets of data, the material card parameters are adjusted, and the adjusted material card parameters are used for machine learning. The machine learning model is used to predict the mechanical properties at all locations on the part in order to solve the problem of blind spots in the inspection of complex parts.

[0008] Furthermore, in step one, the method for manufacturing the flat plate mold is as follows: during part production, a homogeneous flat plate mold is simultaneously die-cast using the same process. This setup initially ignores casting defects and allows for preliminary acquisition of mechanical property data.

[0009] Furthermore, in step one, the reference mechanical properties include the reference hardening curve. In step two, the measured mechanical properties include the measured hardening curve. In step three, the material card parameters include the hardening curve scaling factor. satisfy: .

[0010] Furthermore, in step one, the reference mechanical properties include ε * The parameters include stress triaxiality η, material strain rate sensitivity coefficient D4, and material failure parameters D1, D2, and D3; in step two, the measured mechanical properties include fracture surface strain. ; In step three, the material card parameters include the fracture surface scaling factor, and the fracture surface scaling factor... satisfy: .

[0011] Furthermore, in step three, the material card parameters include Young's modulus.

[0012] Furthermore, in step one, a flat plate mold is used as a sampling carrier to obtain standard specimens for uniaxial tension, notch, shear, and cupping tests, and the basic mechanical properties of the standard specimens are tested.

[0013] Furthermore, in step one, the cross-sectional dimensions of the three-point bend specimen are uniformly 40mm × 30mm.

[0014] Furthermore, it also includes step four: training a machine learning model. The input features of the machine learning model are the process parameters of the part, and the target variable is the material card parameters.

[0015] This solution also has the following effects: 1. The measured hardening curve obtained from the three-point bending force test can be divided into four stages: elastic deformation stage, yielding stage, uniform plastic deformation stage, and necking stage. To improve simulation benchmarking efficiency, in step three, three parameters were selected for mechanical property benchmarking: Young's modulus (controlling the elastic deformation stage), hardening curve scaling factor (regulating plastic deformation behavior), and fracture surface scaling factor (determining fracture characteristics). Subsequently, a machine learning model is used to predict the mechanical response at different locations of the part, thereby solving the problem of blind spots in the detection of complex parts.

[0016] 2. The formula satisfied by the fracture surface scaling factor is based on the JC criterion, which is the Johnson-Cook fracture criterion, an empirical formula used to describe the fracture behavior of materials under dynamic loading conditions.

[0017] 3. After training, the machine learning model can obtain the corresponding material card parameters based on different process parameters (such as porosity and solidification time); Then, based on the material card parameters and the flat plate mold, basic mechanical property tests are performed to obtain the measured mechanical properties, and the mechanical property data at any location are calculated. Attached Figure Description

[0018] Figure 1 Flowchart for an embodiment; Figure 2 This is a schematic diagram illustrating the range of influence of the material card parameters on the hardening curve in an embodiment. Figure 3 A sample image of a three-point bend specimen on the front of a part, as shown in the embodiment. Figure 4 The sample images are taken from the three-point bend test specimens on the reverse and side surfaces of the part used in the embodiment. Figure 5 Design drawing for sampling of flat plate mold. Detailed Implementation

[0019] The following detailed description illustrates the specific implementation method: Example The sampling method for mechanical properties of integrated thin-walled complex die-cast parts is illustrated in the flowchart below. Figure 1 As shown: Includes the following steps: Step 1: Prepare a flat mold made of the same material as the part. The method for making the flat mold is as follows: when producing the part, use the same process to die-cast a homogeneous flat mold. Using a flat mold as a sampling carrier, standard specimens for uniaxial tension, notch, shear, and cupping tests are obtained. The standard specimens are as follows: Figure 5As shown, basic mechanical property tests were performed on the standard specimen to obtain reference mechanical properties, which include a reference constitutive curve and a reference fracture surface. The reference constitutive curve includes a reference hardening curve. Dimensionless strain rate ε * Stress triaxiality η, material strain rate sensitivity coefficient D4, and material failure parameters D1, D2, and D3; The Johnson-Cook model is an empirical failure model widely used for metallic materials and certain polymers under large deformation and high strain rate scenarios such as high-speed impact, explosion, and cutting. It describes how the failure strain of a material (i.e., the strain at which the material begins to fracture) is affected by stress state, strain rate, and temperature.

[0020] These three parameters, D1, D2, and D3, are the core failure parameters of this model. Together, they determine the dependence of the failure strain of the material on the stress triaxiality under quasi-static, room temperature conditions.

[0021] The specific definitions of the three parameters are as follows: D1: An approximate failure strain under pure shear (zero-stress triaxiality); D1 can be regarded as the toughness benchmark of a material under shear-dominated failure modes (such as perforation and shearing). It is a fitting parameter, but its value is close to the failure strain measured in a pure shear test.

[0022] D2: The attenuation coefficient of failure strain under high stress triaxiality; the larger the D2, the greater the attenuation of failure strain relative to the shear state (D1) under high stress triaxiality.

[0023] D3: Controls the sensitivity of failure strain to stress triaxiality; D3 determines the "steepness" of the failure strain curve as stress triaxiality changes. The larger the absolute value of D3, the steeper the curve drops, meaning that the material is more "sensitive" to stress triaxiality.

[0024] D2 and D3 work together to describe the change in failure strain with triaxiality. D2 and D3 are strongly coupled and cannot be understood separately. Together, they fit the shape of the curve of failure strain as a function of triaxiality.

[0025] The hardening curve is a curve that describes the relationship between flow stress and deformation during plastic deformation of a material; Dimensionless strain rate is usually defined as the ratio of the current strain rate to the reference strain rate; Stress triaxiality is defined as the ratio of mean stress to equivalent stress, and is a key indicator for measuring whether the stress state is "tension" or "compression". Material failure parameters and material strain rate sensitivity coefficients are constants that need to be determined through a series of mechanical experiments (such as tensile tests and shear tests with different notch radii).

[0026] Step 2: Take three-point bend samples at different locations on the part, such as... Figure 3 , Figure 4 As shown, the blue area indicates the sampling location. The cross-sectional dimensions of the three-point bend specimens are uniformly 40mm × 30mm, and the thickness is adjusted according to the sampling location. The span during testing is set to 30mm. Three-point bend tests are performed on the specimens to obtain the measured mechanical properties, including the measured hardening curve. and fracture surface strain ; Step two and step one are not in any particular order; Data cleaning and analysis are performed on the mechanical parameters, solidification time and porosity obtained in steps one and two. Data cleaning involves analyzing the expected value and standard deviation of the original data, and then removing data outside the distribution band formed by the expected value and standard deviation (the range of mean ± K times the standard deviation, where K is usually 2 or 3). The data analysis assumes that the data follows a normal distribution, meaning that the values ​​of the ordinates under the same x-axis follow a normal distribution. A smoothing window normal distribution probability weighting method is proposed. Specifically, the data is divided into several equal intervals according to the x-axis range. At the midpoint of each interval, the expected value and variance of all data points within that interval are calculated. Using the normal distribution assumption, the probability of each data point is calculated. The probabilities of all data points within the interval are normalized into weights, and the final weighted value is output. This analytical method, through probability weighting, eliminates the interference of data randomness and provides an intuitive predictive trend for the influence of solidification time and porosity on the constitutive shrinkage factor.

[0027] Step 3: Input the reference mechanical properties into the material card, simulate the three-point bend specimen based on the material card, and adjust the parameters of the material card to align the measured mechanical properties with the reference mechanical properties. Specifically, the following steps are included: 3.1 Solid modeling was performed on all three-point bend specimens. The reference mechanical properties were input into the material card. The solid model was simulated based on the material card. The parameters of the material card were adjusted to align the measured mechanical properties with the reference mechanical properties. The material card parameters include Young's modulus, hardening curve shrinkage factor, and fracture surface shrinkage factor. Young's modulus is a physical quantity that describes the ability of a solid material to resist deformation, and is defined as the ratio of stress to strain.

[0028] like Figure 2As shown, the measured hardening curve obtained from the three-point bending test can be divided into four stages: elastic deformation stage, yielding stage, uniform plastic deformation stage, and necking stage. To improve simulation benchmarking efficiency, three parameters were selected for material card parameter benchmarking: Young's modulus (controlling the elastic deformation stage), hardening curve scaling factor (regulating plastic deformation behavior), and fracture surface scaling factor (determining fracture characteristics), corresponding to... Figure 2 The three regions are divided by the dashed line.

[0029] Hardening curve scaling factor Satisfies (based on MAT24 material card): ; Fracture surface scaling factor Satisfies (based on JC principles): ; 3.2 Shell modeling was performed on the three-point bend specimen with a thickness less than the design thickness. The shell model was simulated based on the material card parameters obtained in step 3.1. The material card parameters also include the scaling factor of the shell model hardening curve and the scaling factor of the shell model fracture surface. The material card parameters were adjusted a second time based on the following formula: ; ; -Shell model hardening curve scaling factor; - Scaling factor of the fracture surface in the shell model; λ1 and λ2 are both second-order correction coefficients.

[0030] Step 4: Normalize the scaling factors of the hardening curve and the fracture surface, and train the machine learning model. The input features of the machine learning model are the process parameters (solidification time, porosity) of the part obtained through mold flow simulation, and the target variable is the material card parameters.

[0031] Example 2 Based on Example 1: In step two, samples of different thicknesses are selected for testing by sampling method; in step 3.2, after obtaining the material card parameters of samples of different thicknesses, the material card parameters of the shell model of the intermediate thickness sample are derived by interpolation method.

[0032] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for sampling mechanical properties of an integrated thin-walled complex die-cast part, characterized in that, Includes the following steps: Step 1: Prepare a flat mold made of the same material as the part, and conduct basic mechanical property tests on the flat mold to obtain reference mechanical properties; Step 2: Take three-point bend samples at different locations on the part, perform three-point bend tests on the samples, and obtain the measured mechanical properties; Step 2 and Step 1 are not in any particular order. Step 3: Input the reference mechanical properties into the material card, simulate the three-point bend specimen based on the material card, adjust the parameters of the material card, and thus align the measured mechanical properties with the reference mechanical properties.

2. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 1, characterized in that: In step one, the method for making the flat plate mold is as follows: during the production of the parts, a homogeneous flat plate mold is simultaneously die-cast using the same process.

3. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 1, characterized in that: In step one, the reference mechanical properties include the reference hardening curve. ; In step two, the measured mechanical properties include the measured hardening curve. In step three, the material card parameters include the hardening curve scaling factor. satisfy: 。 4. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 3, characterized in that: In step one, the reference mechanical properties include the dimensionless strain rate ε. * The parameters include stress triaxiality η, material strain rate sensitivity coefficient D4, and material failure parameters D1, D2, and D3; in step two, the measured mechanical properties include fracture surface strain. ; In step three, the material card parameters include the fracture surface scaling factor, and the fracture surface scaling factor... satisfy: 。 5. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 4, characterized in that: In step three, the material card parameters include Young's modulus.

6. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 1, characterized in that: In step one, a flat plate mold is used as a sampling carrier to obtain standard specimens for uniaxial tension, notch, shear, and cupping tests, and the basic mechanical properties of the standard specimens are then tested.

7. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 1, characterized in that: In step one, the cross-sectional dimensions of the three-point bend specimen are uniformly 40mm × 30mm.

8. The method for sampling the mechanical properties of integrated thin-walled complex die-cast parts according to claim 1, characterized in that: It also includes step four, training a machine learning model. The input features of the machine learning model are the process parameters of the part, and the target variable is the material card parameters.

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