Method for mapping casting alloy from process result to material performance in finite element analysis

Abstract

The invention belongs to the technical field of casting alloy material technology and simulation application, and particularly relates to a method for mapping casting alloy from process results to material performance in finite element analysis, which comprises the following steps of: selecting at least one of flat plate material slice cut samples or casting material grade sample piece materials; obtaining the materials and experimental data to calibrate the reference performance of the casting alloy, and developing a corresponding reference material card; selecting physical parts with industrial standards or process parameter ranges meeting requirements to perform casting process simulation so as to obtain distribution of casting process results, such as at least one piece of related information of flow length, solidification time and void ratio; and based on the experimental result in the step 3, performing sample cutting and experiment on different areas of the part to obtain the mechanical properties of the material, such as at least one of strength and toughness. According to the method, the conversion deviation of manual operation is avoided, the modeling efficiency is improved, simulation prediction precision improvement is supported, and engineering application of casting alloy is further promoted.

Description

Technical Field

[0001] The invention belongs to the field of casting alloy material technology and simulation application technology, and particularly relates to a mapping method from process results to material properties of casting alloys in finite element analysis. Background Art

[0002] With the development of material technology, casting alloys have been widely used in automotive parts. Especially in the current wave of automobile electrification, one-piece die-cast aluminum alloy parts are suitable for replacing traditional sheet metal structures to achieve lightweighting due to their mechanical properties, high specific strength and high production and installation efficiency. However, due to the influence of process parameters and mold shape during the casting process, the flow length, solidification time and shrinkage filter of the material are not distributed in the parts at the same time, resulting in non-uniform distribution and obvious random discreteness of the final material properties. The distribution characteristics of the material mechanical properties of this type of casting parts lead to simulation difficulties in the later structural strength or collision safety analysis process. The original finite element analysis results using uniform single material properties have high errors, resulting in strict restrictions on the application and promotion of materials and safety margins, making the conservatively designed product solutions not particularly competitive in terms of cost and weight reduction. Therefore, there is a need for a method that can characterize the relationship between casting process and material mechanics, and map the process parameters obtained from casting simulation to the finite element analysis model to characterize the real mechanical properties of material points, thereby improving the simulation prediction accuracy.

[0003] At present, the main methods for obtaining material properties on parts are software calculation prediction and CT scanning flaw detection. For example, the paper document 1 studied the collision failure simulation of the cast aluminum suspension of the automotive powertrain. Mechanical properties tests of cast aluminum materials were carried out, and based on the CrachFEM toughness failure criterion, a cast aluminum material card that considers the random discreteness of material properties was established. The reliability of the cast aluminum material card was verified by the drop hammer impact test and simulation benchmarking of the suspension bracket. For another example, patent document 2 (CN102117358A) discloses a method for simulating casting defects and microstructures in castings, and provides a system for predicting casting defects and microstructures in the castings of suppliers / sellers for component / system durability analysis without knowing one or more of the specific circumstances of the casting layout, casting gating system and riser design, and casting process parameters. However, this method only discusses, for example, patent document 3 (CN114386171A) discloses a method of obtaining casting defects of materials on parts based on CT flaw detection, and converting them into damage variable parameters of *mat24 and *mat_add_erosion keywords in lsdyna software through a mathematical model of linear interpolation, so as to consider the influence of casting defects in the process of component modeling and analysis.

[0004] The random discreteness method in paper document 1 has limitations and is not suitable for predicting and simulating large fluctuations or relatively stable systematic deviations such as large one-piece die castings. Although patent document 2 predicts the material microstructure and void defects in castings, it does not involve the relationship between defects and material mechanical properties, nor does it discuss the impact safety simulation method of castings. Patent document 3 proposes a general method from CT scanning defects to lsdyna material simulation, but does not explain the calibration method of the linear interpolation formula and the empirical parameters. CT scanning must be implemented under the prerequisite of existing physical parts, which is not applicable to the analysis work in the early design stage when there are no physical parts. Summary of the invention

[0005] The purpose of the present invention is to provide a mapping method from process results to material properties of casting alloys in finite element analysis, thereby avoiding conversion deviations caused by manual operations, improving modeling efficiency, supporting the improvement of simulation prediction accuracy, and further promoting the engineering application of casting alloys.

[0006] The technical solution adopted by the present invention is as follows: The mapping method from process results to material properties of casting alloys in finite element analysis includes the following steps: Step 1, selecting at least one of the flat sheet cutting sample or the casting material grade sample material; Step 2: Obtain the above materials and experimental data to calibrate the benchmark performance of the casting alloy, and develop a corresponding benchmark material card; Step 3: Select physical parts that meet the industry standards or process parameter ranges to perform casting process simulation to obtain distribution of casting process results, such as at least one of flow length, solidification time and porosity; Step 4: Based on the experimental results in step 3, different regions of the part are cut and tested to obtain mechanical properties of the material, such as at least one of strength or toughness; Step 5: normalize the reference material performance parameters in step 2 based on the mechanical property distribution of the parts obtained in step 4; Step 6: Use the normalization coefficient in step 5 as the dependent variable, and the part process result distribution obtained in step 3 as the independent variable, and use mathematical means to perform regression fitting to obtain the mapping relationship between the two.

[0007] In a preferred embodiment, the material experiments in step one include but are not limited to one or a combination of the following: static uniaxial stretching, dynamic uniaxial stretching, static / dynamic stretching of notched specimens, static / dynamic stretching of circular hole specimens, three-point bending static / dynamic experiments, static shear experiments, and static perforation experiments.

[0008] In a preferred embodiment, based on the experimental data calibrated in step 2, the mechanical properties of the material are divided into two parts: strength and toughness. Different types of experimental results in step 1 correspond to the yield strength and failure strain under different stress states. By selecting a reasonable constitutive model such as mat24 / mat124 / MF GenYld and failure criterion Gissmo / Diem / CrachFEM, and identifying or fitting material parameters in a forward or reverse manner, the card parameters are obtained.

[0009] In a preferred embodiment, commonly used hardening flow criteria include: Swift, Voce, Ghosh and Hockett-Sherby; Commonly used failure criteria include: Johnson-Cook (JC), modified Mohr-Coulomb (MMC), CrachFEM-DNF, CrachFEM-DSF; Among them, finite element modeling is performed based on the benchmark material card to calculate all experimental conditions, and the ISO standard similarity between the simulation and experimental load curves is required to be greater than 90%.

[0010] In a preferred embodiment, in step 3, the output process result is , subscript and Representative The first material process analysis point, unit or integration point process parameters, a total of Material points and The process parameters consist of dimensional process parameter distribution matrix, which is as follows: .

[0011] In a preferred embodiment, based on step 4, a uniaxial tensile test is defined as a necessary test for obtaining the yield strength. The yield strength of the corresponding cutting point can be directly obtained by converting the stress-strain curve through the uniaxial tensile force-displacement curve, which is recorded as , the same as in claim 5 definition, Representative corresponding to The measured yield strength of each material process analysis point; Three-point bending is defined as a necessary experiment to obtain failure toughness. The failure strain of the corresponding cutting point under the plane strain state can be obtained by measuring the strain on the crack surface through DIC or finite element inversion, which is denoted as , the same as in claim 5 Definition, corresponding to The measured failure strain of each material process analysis point; The corresponding material strength and toughness performance distribution n-dimensional vector is: .

[0012] In a preferred embodiment, the standard value of the material strength calibrated in step 2 is , the standard value of toughness (failure strain) is , then the normalized coefficient of measured strength and toughness is and The calculation formula is:

[0013] ; The normalized coefficient matrix is: .

[0014] In a preferred embodiment, based on step 6, Representative corresponding to The estimated normalization coefficient of the yield strength of the measured point of the material process analysis point is estimated by the following fitting formula. Process parameters of each material process analysis point Related: ; Among them, and is the fitting coefficient, and exponential of the fitted equation; Similarly, suppose Representative corresponding to The estimation of the normalized coefficient of failure strain at the measured point of each material process analysis point can be expressed as: ; The target deviation can be expressed as: ; By solving the minimum value of the above formula through optimization method, the coefficient of the fitting equation is obtained and , and the index and .

[0015] In a preferred embodiment, after step 6, the basic material card of the corresponding grade of casting alloy and the mapping relationship between the casting process and the material properties are obtained, and the process mapping and simulation calculation of the material properties of the casting parts using the same grade of alloy can be performed to improve the prediction accuracy, as follows: First, complete the casting process simulation of the new part, obtain the process information corresponding to all material points, and export it into the process result distribution matrix in step three: ; Then, the normalization coefficients of strength and toughness, i.e. the proportional relationship between the actual performance of the material point and the performance of the standard material, are calculated according to the fitting formula determined in step 6: Normalization factor of yield strength: ; Failure strain normalization coefficient: ; Finally, the true yield strength and failure toughness of each material point can be calculated by the normalization coefficient in the same way as step 5. The formula is as follows: ; The vector form is: .

[0016] In a preferred embodiment, the yield strength and failure toughness calculated from the part process results can be directly used for initialization mapping of the finite element model.

[0017] The technical effects achieved by the present invention are: The present invention is a complete generation method for mapping relationship from casting alloy process parameters to material properties, which abandons the traditional single homogeneous material or block simulation method based on engineer experience, and the analysis process from process import, parameter identification, performance export mapping each stage can be highly automated, avoiding conversion deviation of manual operation, improving modeling efficiency, supporting simulation prediction accuracy improvement, and further promoting the engineering application of casting alloys. It can be used for accurate simulation of die-cast metal parts by general finite elements including lsdyna, radioss, abaqus and pamcrash combined with CrachFEM or other similar material constitutive methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the calibration of the mapping relationship between 'process result' and 'material performance' in the embodiment of the present invention; Figure 2 It is a schematic diagram of a process of establishing a finite element model with process-induced material deviations using a process mapping method in the present invention; Figure 3 It is a schematic diagram of cutting samples of various types of flat sheet materials in the present invention; Figure 4 is a schematic diagram of basic material experimental data obtained by cutting samples of flat sheet material in the present invention; Figure 5 It is a schematic diagram of the hardening curve fitted by the equation of the Hockett-Sherby combination Swift in the present invention; Figure 6 is a schematic diagram of the pressure-anisotropic yield surface model of the present invention; Figure 7 is a schematic diagram of CrachFEM-DNF and DSF failure curves in the present invention; Figure 8 It is a schematic diagram of the process results of flow length, solidification time and porosity in the present invention; Fig. 9 Schematic diagram of cutting samples of flat sheet materials in different regions in the present invention; Fig.10 is a schematic diagram of the strength results in the present invention; Fig.11 is a schematic diagram of the toughness results in the present invention; Fig.12 is a schematic diagram of the first mapping relationship in the present invention; Fig.13 is a schematic diagram of the second mapping relationship in the present invention; Fig.14 It is a schematic diagram of the third mapping relationship in the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0023] Please see attached Figures 1 to 14 As shown, the present invention provides a method for mapping process results to material properties of casting alloys in finite element analysis, the following steps: Step 1: Experimental material selection: select at least one of the flat sheet cutting sample or casting material grade sample material; Based on the material-level experimental results of flat sheet cuttings or casting material-level samples with uniform and stable performance, small-sized casting flat sheets or casting material-level samples have good uniformity and excellent mechanical properties, which can be used as the basic properties of casting alloys for subsequent calibration and analysis.

[0024] Among them, the material experiments in step one include but are not limited to one or a combination of the following: static uniaxial stretching, dynamic uniaxial stretching, static / dynamic stretching of notched specimens, static / dynamic stretching of circular hole specimens, three-point bending static / dynamic experiment, static shear experiment and static perforation experiment.

[0025] Step 2. Calibrate material properties: Obtain the above materials and experimental data to calibrate the benchmark properties of the casting alloy, and develop the corresponding benchmark material card. The mechanical properties of the material are divided into strength and toughness. The different types of experimental results in step 1 correspond to the yield strength and failure strain under different stress states. By selecting a reasonable constitutive model such as mat24 / mat124 / MF GenYld and failure criterion Gissmo / Diem / CrachFEM, and identifying or fitting the material parameters in a forward or reverse manner, the card parameters are obtained.

[0026] Commonly used hardening flow criteria include but are not limited to the following formulas: Swift, Voce, Ghosh and Hockett-Sherby; Commonly used failure criteria include but are not limited to the following formulas: Johnson-Cook (JC), modified Mohr-Coulomb (MMC), CrachFEM-DNF, CrachFEM-DSF, etc. It is worth noting that the above-mentioned elastic-plastic and failure toughness formulas are commonly used formulas in the industry and will not be elaborated on here. They may also be commonly used formulas for developing other basic materials in the industry, mainly to achieve the calculation of the basic properties of flat sheet cuttings or casting material-level samples.

[0027] Among them, finite element modeling is performed based on the benchmark material card to calculate all experimental conditions, and the ISO standard similarity between the simulation and experimental load curves is required to be greater than 90% to meet the calibration parameter accuracy requirements.

[0028] Furthermore, for cast aluminum alloys, it is generally recommended to use Hockett-Sherby to fit the plastic flow curve and CrachFEM-DNF to fit the failure curve / surface.

[0029] Step 3: Cast the selected material parts to obtain the results: Select physical parts that meet the industry standards or process parameter ranges to perform casting process simulation to obtain the distribution of casting process results.

[0030] Due to the different casting process software, the output result format will be slightly different, but you can write corresponding scripts to read and standardize the format. Suppose the output process result is , subscript and Representative The first material process analysis point, unit or integration point process parameters, a total of Material points and The process parameters consist of dimensional process parameter distribution matrix, which is as follows: .

[0031] Step 4: Cutting samples to obtain mechanical properties: Based on the experimental results in step 3, different areas of the part are subjected to material-level cutting material experiments to obtain the mechanical properties of the material; Due to the complex geometry of actual parts, it is often difficult to cut larger material samples.

[0032] Uniaxial tensile test is defined as a necessary experiment to obtain yield strength. After converting the stress-strain curve through the uniaxial tensile force-displacement curve, the yield strength of the corresponding cutting point can be directly obtained, which is recorded as , the same as in claim 5 definition, Representative corresponding to The measured yield strength of each material process analysis point; Three-point bending is defined as a necessary experiment to obtain failure toughness. The failure strain of the corresponding cutting point under the plane strain state can be obtained by measuring the strain on the crack surface through DIC or finite element inversion, which is denoted as , the same as in claim 5 Definition, corresponding to The measured failure strain of each material process analysis point; The corresponding material strength and toughness performance distribution n-dimensional vector is: .

[0033] In actual operation, notch or shear tests can be appropriately added as supplementary tests for strength and toughness properties according to the situation, that is, adding vector series.

[0034] Step 5: Normalize the material parameters: Normalize the reference material performance parameters in step 2 based on the mechanical property distribution of the parts obtained in step 4; Assume that the standard value of material strength calibrated in step 2 is , the standard value of toughness (failure strain) is , then the normalized coefficient of measured strength and toughness is and The calculation formula is: ; The normalized coefficient matrix is: .

[0035] Step 6: Use regression fitting to obtain the mapping relationship between the two: use the normalization coefficient in step 5 as the dependent variable, and the part process result distribution obtained in step 3 as the independent variable, and use mathematical means to perform regression fitting to obtain the mapping relationship between the two.

[0036] set up Representative corresponding to The estimated normalization coefficient of the yield strength of the measured point of the material process analysis point is estimated by the following fitting formula. Process parameters of each material process analysis point Related: ; Among them, and is the fitting coefficient, and exponential of the fitted equation; Similarly, suppose Representative corresponding to The estimation of the normalized coefficient of failure strain at the measured point of each material process analysis point can be expressed as: ; The target deviation can be expressed as: ; By solving the minimum value of the above formula through optimization method, the coefficient of the fitting equation is obtained and , and the index and .

[0037] Based on the above steps, since all kinds of process results have a certain influence on material properties, as many types of process results as possible should be considered. However, in practical applications, too many process parameters often mean more complex fitting algorithms and greater calculations, and even overfitting due to too many process parameters and regression parameters and too few measured material performance point data. Therefore, the present invention stipulates that while ensuring the three process results of flow length, solidification time and void ratio, other types of process parameters are appropriately selected as mapping function independent variables.

[0038] Step 7: After step 6, the basic material card of the corresponding grade of casting alloy and the mapping relationship between casting process and material properties are obtained. The process mapping and simulation calculation of material properties of casting parts using the same grade of alloy can be performed to improve the prediction accuracy, as follows: First, complete the casting process simulation of the new part, obtain the process information corresponding to all material points, and export it into the process result distribution matrix in step three: ; Then, the normalization coefficients of strength and toughness, i.e. the proportional relationship between the actual performance of the material point and the performance of the standard material, are calculated according to the fitting formula determined in step 6: Normalization factor of yield strength: ; Failure strain normalization coefficient: ; Finally, the true yield strength and failure toughness of each material point can be calculated by the normalization coefficient in the same way as step 5. The formula is as follows: ; The vector form is: .

[0039] The yield strength and failure toughness calculated from the above part process results can be directly used for the initialization mapping of the finite element model. It is worth noting that steps one to six describe the method flow for obtaining the mapping relationship between process results and material mechanical properties through the development of target casting alloy benchmark materials and typical parts cutting experiments, and step seven describes how to apply the mapping relationship obtained in the first six steps to perform process mapping modeling on other types of parts using the casting alloy.

[0040] In order to better illustrate the purpose and advantages of the present invention, the present invention is further described below in conjunction with specific implementations: Taking the lsdyna general finite element solver and CrachFEM material constitutive method as examples, CrachFEM software provides an interface for die-casting aluminum alloy material performance process mapping. Taking LSDYNA as an example, the *INITIAL_STRESS_SHELL / SOLID keyword can be used to map and initialize the unit points.

[0041] For specific card settings, please refer to the CrachFEM user manual. The mapping framework process and card settings are as follows: As attached Figure 3-Figure 4 As shown, material-level experiments are conducted on flat sheet samples with uniform and stable performance to obtain basic material experimental data.

[0042] As attached Figure 5-Figure 7 As shown, based on the material-level experimental data obtained in the above steps, the benchmark properties of the casting alloy are calibrated, and the corresponding benchmark material is developed.

[0043] Here, the Hockett-Sherby combined Swift equation was selected to fit the hardening curve and the compression anisotropic yield surface model according to the material properties in the case, and the CrachFEM-DNF and DSF failure curves were calibrated according to the measurement results of the failure fracture. The above parameters will serve as the basic card data for the casting alloy material.

[0044] As attached Figure 8 As shown, a physical part that meets a certain standard or process parameter range is selected to perform casting process simulation to obtain the distribution of casting process results; Here, a typical die-casting part is selected for die-casting process simulation analysis. The analysis results output flow length, solidification time and porosity as process results for mapping relationship calibration; The process results are recorded in matrix form as follows: ; As attached Figure 9-11 As shown, in step three, material-level cutting material experiments are performed on different areas of the physical part to obtain the mechanical properties of the material.

[0045] The strength and toughness results are expressed in vector form:

[0046] ; The normalization coefficient in step five is used as the dependent variable, and the distribution of the part process results obtained in step three is used as the independent variable. Mathematical means are used to perform regression fitting to obtain the mapping relationship between the two.

[0047] No. Estimation of normalization coefficient of yield strength at measured point: ; No. Estimation of normalized coefficient of failure strain at measured point: ; The target deviation is: ; If k1=k2=1 is selected, linear regression can be used to obtain Figure 12-14 The mapping relationship.

[0048] Finally, after the above steps, the basic material card of the brand of casting alloy and the mapping relationship from casting process to material properties are obtained. The process mapping and simulation calculation of material properties of casting parts using the same brand of alloy can be performed to improve the prediction accuracy.

[0049] The working principle of the present invention is as follows: first, the benchmark performance of the casting alloy and the benchmark material card are calibrated based on the material-level experimental results of the flat sheet cutting sample or the casting material-level sample with uniform and stable performance; secondly, a physical part that meets the requirements of a certain standard or process parameter range is selected for casting process simulation to obtain the distribution of casting process results (such as flow length, solidification time and porosity), and a material-level cutting experiment is performed in different areas of the above-mentioned parts to obtain the distribution of the mechanical properties (strength and toughness) of the material. Then, the above-mentioned mechanical property distribution is normalized to the performance parameters of the benchmark card and used as the dependent variable, and the process result distribution is used as the independent variable, and mathematical means are used for regression fitting to find the mapping relationship between the two; finally, the distribution of material properties on other parts cast with this alloy can be analyzed through the above-mentioned mapping relationship, and the result information generated by the casting simulation can also be completely and accurately converted into material mechanical properties and mapped to the finite element analysis model to simulate structural strength or collision safety.

[0050] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A method for mapping process results to material properties of casting alloys in finite element analysis, characterized in that: The following steps are involved: Step 1, selecting at least one of the flat sheet cutting sample or the casting material grade sample material; Step 2: Obtain the above materials and experimental data to calibrate the benchmark performance of the casting alloy, and develop a corresponding benchmark material card; Step 3: Select physical parts that meet the industry standards or process parameter ranges to perform casting process simulation to obtain distribution of casting process results, such as at least one of flow length, solidification time and porosity; Step 4: Based on the experimental results in step 3, different regions of the part are cut and tested to obtain mechanical properties of the material, such as at least one of strength or toughness; Step 5: normalize the reference material performance parameters in step 2 based on the mechanical property distribution of the parts obtained in step 4; Step 6: Use the normalization coefficient in step 5 as the dependent variable, and the part process result distribution obtained in step 3 as the independent variable, and use mathematical means to perform regression fitting to obtain the mapping relationship between the two.

2. The method according to claim 1, characterized in that: The material experiments in step 1 include but are not limited to one or a combination of the following: static uniaxial stretching, dynamic uniaxial stretching, static / dynamic stretching of notched specimens, static / dynamic stretching of circular hole specimens, three-point bending static / dynamic experiment, static shear experiment and static perforation experiment.

3. The method according to claim 1, characterized in that: Based on the experimental data calibrated in step 2, the mechanical properties of the material are divided into two parts: strength and toughness. Different types of experimental results in step 1 correspond to the yield strength and failure strain under different stress states. By selecting a reasonable constitutive model such as mat24 / mat124 / MF GenYld and failure criterion Gissmo / Diem / CrachFEM, and identifying or fitting material parameters in a forward or reverse manner, the card parameters are obtained.

4. The method according to claim 3, characterized in that: Commonly used hardening flow criteria include: Swift, Voce, Ghosh and Hockett-Sherby; Commonly used failure criteria include: Johnson-Cook (JC), modified Mohr-Coulomb (MMC), CrachFEM-DNF, CrachFEM-DSF; Among them, finite element modeling is performed based on the benchmark material card to calculate all experimental conditions, and the ISO standard similarity between the simulation and experimental load curves is required to be greater than 90%.

5. The method according to claim 1, characterized in that: In step 3, the output process result is assumed to be , subscript and Representative The first material process analysis point, unit or integration point process parameters, a total of Material points and The process parameters consist of dimensional process parameter distribution matrix, which is as follows: 。 6. The method according to claim 5, characterized in that: Based on step 4, the uniaxial tensile test is defined as a necessary experiment to obtain the yield strength. The yield strength of the corresponding cutting point can be directly obtained by converting the stress-strain curve through the uniaxial tensile force-displacement curve, which is recorded as , the same as in claim 5 definition, Representative corresponding to The measured yield strength of each material process analysis point; Three-point bending is defined as a necessary experiment to obtain failure toughness. The failure strain of the corresponding cutting point under the plane strain state can be obtained by measuring the strain on the crack surface through DIC or finite element inversion, which is denoted as , the same as in claim 5 Definition, corresponding to The measured failure strain of each material process analysis point; The corresponding material strength and toughness performance distribution n-dimensional vector is: 。 7. The method according to claim 1, characterized in that: The material strength standard value calibrated in step 2 is , the standard value of toughness (failure strain) is , then the normalized coefficient of measured strength and toughness is and The calculation formula is: ； The normalized coefficient matrix is: 。 8. The method according to claim 1, characterized in that: Based on step 6, Representative corresponding to The estimated normalization coefficient of the yield strength of the measured point of the material process analysis point is estimated by the following fitting formula Process parameters of each material process analysis point Related: ; Among them, and is the fitting coefficient, and The exponential of the fitting equation; similarly, let Representative corresponding to The estimation of the normalized coefficient of failure strain at the measured point of each material process analysis point can be expressed as: ; The target deviation can be expressed as: ; By solving the minimum value of the above formula through optimization method, the coefficient of the fitting equation is obtained and , and the index and .

9. The method according to any one of claims 1 to 8, characterized in that: After step 6, the basic material card of the corresponding grade of casting alloy and the mapping relationship between casting process and material properties are obtained. The process mapping and simulation calculation of material properties can be performed on the casting parts using the same grade of alloy to improve the prediction accuracy, as follows: First, complete the casting process simulation of the new part, obtain the process information corresponding to all material points, and export it into the process result distribution matrix in step three: ； Then, the normalization coefficients of strength and toughness, i.e. the proportional relationship between the actual performance of the material point and the performance of the standard material, are calculated according to the fitting formula determined in step 6: Normalization factor of yield strength: ; Failure strain normalization coefficient: ; Finally, the true yield strength and failure toughness of each material point can be calculated by the normalization coefficient in the same way as step 5. The formula is as follows: ; The vector form is: .

10. The method according to claim 9, characterized in that: The yield strength and failure toughness calculated from the part process results can be directly used for the initialization mapping of the finite element model.

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