A method of modeling microstructure
By obtaining the composition and quantity of mineral structures in spherical cast iron samples, and using finite element simulation to generate microstructure modeling diagrams, the problem of ignoring the diversity of alloy microstructures in existing technologies is solved, and more accurate machining analysis is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHWESTERN POLYTECHNICAL UNIV
- Filing Date
- 2022-09-23
- Publication Date
- 2026-06-05
AI Technical Summary
Existing cutting simulation modeling methods use homogeneous models, which ignore the diversity of alloy microstructures, resulting in the inability to effectively evaluate stress and strain distribution, chip shape and cutting force magnitude during the machining process, as well as the surface morphology after machining.
A microstructure modeling method is provided, which obtains the target component proportion and quantity of mineral structure in spheroidal cast iron sample, and generates target microstructure modeling diagram using finite element simulation, taking into account the property characteristics and distribution of pearlite, ferrite and graphite particles.
It improves the realism and accuracy of microstructure modeling, enabling more accurate analysis of stress and strain distribution and other characteristics during the cutting process.
Smart Images
Figure CN115831272B_ABST
Abstract
Description
Technical Field
[0001] The embodiments of this disclosure relate to the field of computer technology, and more specifically, the embodiments of this disclosure relate to a method for modeling microstructures. Background Technology
[0002] Machining is the most important machining method in mechanical manufacturing. It is a complex thermo-mechanical coupling process characterized by high strain, high strain rate, and high temperature. Studying the impact of machining on alloy properties using traditional experimental methods is extremely time-consuming and labor-intensive, making it impractical for widespread application. With the continuous expansion of computer simulation technology in machining, many scholars are conducting simulation analyses using software such as Abaqus, Deform, and Advantage. This significantly reduces repetitive experimental work while improving the accuracy of machining process analysis, especially in the analysis of surface features in machining, where it demonstrates unparalleled advantages over traditional experimental methods.
[0003] Currently, most cutting simulation models use homogeneous models, which are singular and ignore the compositional diversity of the alloy's own microstructure. This makes it impossible for simulation analysis to effectively evaluate the stress and strain distribution during machining, the shape of the chips, the magnitude of the cutting forces, and the surface morphology after machining.
[0004] This section is intended to provide background or context for the embodiments of this disclosure set forth in the claims, and the description herein is not acknowledged as prior art simply because it is included in this section. Summary of the Invention
[0005] To overcome the problems existing in related technologies, this disclosure provides a microstructure modeling method and apparatus, storage medium, and electronic device.
[0006] According to a first aspect of this disclosure, a microstructure modeling method is provided, applied to the microstructure of ductile iron, the method comprising:
[0007] Based on the mineral structure contained in the ductile iron, the target component proportion and target quantity of the mineral structure in the ductile iron sample are obtained;
[0008] Based on the property characteristics of the mineral structure, the proportion of the target component, and the quantity of the target component, the target shape and target size of the mineral structure used for modeling are determined;
[0009] Using a preset finite element simulation method, the mineral structure is modeled according to the target quantity, target shape, and target size, generating a target microstructure modeling diagram of the ductile iron under a preset modeling style.
[0010] Optionally, the mineral structure includes pearlite, ferrite, and graphite particles, and obtaining the target component proportion and target quantity of the mineral structure in the ductile iron sample includes:
[0011] The compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample was statistically analyzed.
[0012] Based on the component distribution information of the pearlite, the ferrite, and the graphite particles, the first component percentage and first quantity of the ferrite, the second component percentage and second quantity of the graphite particles, and the third component percentage of the pearlite are determined respectively.
[0013] Optionally, the step of statistically analyzing the compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample includes:
[0014] The compositional distribution information of the graphite particles was statistically analyzed using uncorroded ductile iron samples.
[0015] The compositional distribution information of pearlite and ferrite was statistically analyzed using the etched ductile iron sample.
[0016] Optionally, determining the target shape and target size of the mineral structure for modeling based on the property characteristics of the mineral structure, the proportion of the target component, and the target quantity includes:
[0017] Based on the property characteristics of the ferrite and the graphite particles, the first shape of the ferrite and the second shape of the graphite particles are determined.
[0018] Based on the first component proportion and first quantity of the ferrite and the second component proportion and second quantity of the graphite particles, a first size of the ferrite in the first shape and a second size of the graphite particles in the second shape are determined.
[0019] Optionally, the step of using a preset finite element simulation method to model the mineral structure according to the target quantity, target shape, and target size, and generating a target microstructure modeling diagram of the ductile iron under a preset modeling style, includes:
[0020] The first number of target ferrite, the second number of target graphite particles, and the pearlite of the third component proportion are modeled using the preset finite element simulation method to generate a target microstructure modeling diagram of the ductile iron under the preset modeling style; the target ferrite is ferrite of the first size and the first shape, and the target graphite particles are graphite particles of the second size and the second shape.
[0021] According to a second aspect of this disclosure, a microstructure modeling apparatus is provided for application to the microstructure of ductile iron, the apparatus comprising:
[0022] The acquisition module is used to obtain the target component proportion and target quantity of the mineral structure in the ductile iron sample based on the mineral structure contained in the ductile iron.
[0023] The determination module is used to determine the target shape and target size of the mineral structure for modeling based on the property characteristics of the mineral structure, the proportion of the target component, and the target quantity;
[0024] The generation module is used to model the mineral structure according to the target quantity, target shape and target size using a preset finite element simulation method, and generate a target microstructure modeling diagram of the ductile iron under a preset modeling style.
[0025] Optionally, the mineral structure includes pearlite, ferrite, and graphite particles, and the acquisition module is further configured to:
[0026] The compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample was statistically analyzed.
[0027] Based on the component distribution information of the pearlite, the ferrite, and the graphite particles, the first component percentage and first quantity of the ferrite, the second component percentage and second quantity of the graphite particles, and the third component percentage of the pearlite are determined respectively.
[0028] Optionally, the acquisition module is further configured to:
[0029] The compositional distribution information of the graphite particles was statistically analyzed using uncorroded ductile iron samples.
[0030] The compositional distribution information of pearlite and ferrite was statistically analyzed using the etched ductile iron sample.
[0031] Optionally, the determining module is further configured to:
[0032] Based on the property characteristics of the ferrite and the graphite particles, the first shape of the ferrite and the second shape of the graphite particles are determined.
[0033] Based on the first component proportion and first quantity of the ferrite and the second component proportion and second quantity of the graphite particles, a first size of the ferrite in the first shape and a second size of the graphite particles in the second shape are determined.
[0034] Optionally, the generation module is further configured to:
[0035] The first number of target ferrite, the second number of target graphite particles, and the pearlite of the third component proportion are modeled using the preset finite element simulation method to generate a target microstructure modeling diagram of the ductile iron under the preset modeling style; the target ferrite is ferrite of the first size and the first shape, and the target graphite particles are graphite particles of the second size and the second shape.
[0036] According to one aspect of this disclosure, a storage medium is provided on which a computer program is stored, wherein the computer program, when executed by a processor, is the microstructure modeling method described above.
[0037] According to one aspect of this disclosure, an electronic device is provided, comprising:
[0038] Processor; and
[0039] Memory for storing the executable instructions of the processor;
[0040] The processor is configured to execute the microstructure modeling method described above by executing the executable instructions.
[0041] In summary, the microstructure modeling method provided in this disclosure first obtains the target component proportion and target quantity of the mineral structure contained in the ductile iron sample based on the mineral structure. Then, based on the property characteristics of the mineral structure and the target component proportion and target quantity, the target shape and target size of the mineral structure for modeling are determined. Using a preset finite element simulation method, the mineral structure is modeled according to the target quantity, target shape, and target size, generating a target microstructure modeling diagram of the ductile iron under a preset modeling style. In this way, determining the distribution components and quantity of the mineral structure based on a real ductile iron sample makes the distribution and quantity of mineral components more consistent with the characteristics of real ductile iron, improving the accuracy of determining the component distribution and quantity. Furthermore, using finite element simulation to model according to the shape and component distribution of the mineral structure makes the resulting ductile iron microstructure modeling diagram more closely resemble the actual microstructure of ductile iron, improving the realism of the microstructure modeling. This allows for accurate analysis of stress and strain distribution and other characteristics during subsequent cutting based on the microstructure modeling diagram. Attached Figure Description
[0042] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which:
[0043] Figure 1This is a flowchart illustrating the steps of a microstructure modeling method provided in an embodiment of this disclosure;
[0044] Figure 2 This is a flowchart illustrating the steps for determining the proportion of mineral structural components according to an embodiment of this disclosure;
[0045] Figure 3 This is a microscopic schematic diagram of a ductile iron according to an embodiment of this disclosure;
[0046] Figure 4 This is a flowchart illustrating the steps for determining the modeling shape of a mineral structure according to an embodiment of this disclosure;
[0047] Figure 5 This is a schematic diagram of the graphite particle diameter frequency distribution provided in this disclosure.
[0048] Figure 6 This is a schematic diagram of a target microstructure modeling diagram provided in this disclosure;
[0049] Figure 7 This is a block diagram of a microstructure modeling apparatus according to an embodiment of the present disclosure;
[0050] Figure 8 This is a schematic diagram of a storage medium according to an embodiment of the present disclosure; and
[0051] Figure 9 This is a block diagram of an electronic device according to embodiments of the present disclosure.
[0052] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation
[0053] The principles and spirit of this disclosure will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.
[0054] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. The data involved in this disclosure can be data authorized by the user or fully authorized by all parties.
[0055] In this document, any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.
[0056] The principles and spirit of this disclosure are explained in detail below with reference to several representative embodiments.
[0057] Figure 1 This is a flowchart illustrating the steps of a microstructure modeling method provided in this disclosure embodiment, such as... Figure 1 As shown, the method applied to the microstructure of ductile iron can include:
[0058] Step S101: Based on the mineral structure contained in the ductile iron, obtain the target component proportion and target quantity of the mineral structure in the ductile iron sample.
[0059] In this embodiment of the disclosure, the ductile iron sample can be a mineral sample used for metallographic observation of ductile iron, such as a laboratory mineral sample or an artificially created mineral sample. The mineral structure contained in the ductile iron can be determined based on the mineral characteristics of ductile iron, and the mineral structure contained in the ductile iron can include multiple mineral aggregates. Obtaining the target component proportion and target quantity of the mineral structure in the ductile iron sample can be achieved by taking the component proportion and the number of each mineral structure in the ductile iron sample as the target component proportion and target quantity of that mineral structure when multiple mineral structures are included.
[0060] Step S102: Based on the property characteristics of the mineral structure, the proportion of the target component, and the target quantity, determine the target shape and target size of the mineral structure for modeling.
[0061] In this embodiment of the disclosure, the property characteristics of the mineral structure can be the shape corresponding to the mineral structure in ductile iron, or the size range of the mineral structure in ductile iron, etc. Based on the property characteristics of the mineral structure, the proportion and quantity of the target components, the target shape and target size of the mineral structure for modeling are determined. This can be achieved by determining the target shape of the mineral structure for modeling based on its property characteristics, and then determining the target size corresponding to the mineral structure when it appears in the target shape based on the proportion and quantity of the target components.
[0062] Step S103: Using a preset finite element simulation method, model the mineral structure according to the target quantity, target shape and target size, and generate a target microstructure modeling diagram of the ductile iron under the preset modeling style.
[0063] In this embodiment, the preset finite element simulation method can be a finite element engineering simulation analysis method, such as ABAQUS software or other engineering simulation software. The preset modeling style can be a pre-set graphic size for modeling ductile iron, for example, the preset modeling style can be 100×50mm. Given the preset modeling style, the preset finite element simulation method can be used to model according to the target quantity, target shape and target size contained in each mineral structure, to obtain the target microstructure modeling diagram of ductile iron under the preset modeling style. Since ductile iron is a highly heterogeneous material, mainly composed of matrix and graphite particles, and spheroidal graphite is often randomly arranged in the cast iron matrix, the existing simple homogeneous modeling diagram is no longer suitable for the cutting simulation of alloys such as ductile iron. This embodiment determines the distribution and quantity of each mineral structure based on real ductile iron samples, making the distribution and quantity of mineral components more consistent with the characteristics of real ductile iron, and improving the accuracy of determining the component distribution and quantity.
[0064] In summary, the microstructure modeling method provided in this disclosure first obtains the target component proportion and target quantity of the mineral structure contained in the ductile iron sample based on the mineral structure. Then, based on the property characteristics of the mineral structure and the target component proportion and target quantity, the target shape and target size of the mineral structure for modeling are determined. Using a preset finite element simulation method, the mineral structure is modeled according to the target quantity, target shape, and target size, generating a target microstructure modeling diagram of the ductile iron under a preset modeling style. In this way, determining the distribution components and quantity of the mineral structure based on a real ductile iron sample makes the distribution and quantity of mineral components more consistent with the characteristics of real ductile iron, improving the accuracy of determining the component distribution and quantity. Furthermore, using finite element simulation to model according to the shape and component distribution of the mineral structure makes the resulting ductile iron microstructure modeling diagram more closely resemble the actual microstructure of ductile iron, improving the realism of the microstructure modeling. This allows for accurate analysis of stress and strain distribution and other characteristics during subsequent cutting based on the microstructure modeling diagram.
[0065] Optionally, in this embodiment of the disclosure, the mineral structure may include pearlite, ferrite, and graphite particles. The operations described above for obtaining the target component proportion and target quantity of the mineral structure in the ductile iron sample are as follows: Figure 2 As shown, it can specifically include:
[0066] Step S1011: Statistically analyze the compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample.
[0067] In this embodiment of the disclosure, the component distribution information of pearlite, ferrite, and graphite particles in a ductile iron sample can be statistically analyzed based on the different display characteristics of pearlite, ferrite, and graphite particles under mirror imaging, using methods such as laboratory metallographic observation. This component distribution information can be the distribution area of pearlite, ferrite, and graphite particles in the metallographic image.
[0068] Example, Figure 3 This is a microscopic schematic diagram of a ductile iron according to an embodiment of this disclosure, as shown below. Figure 3 As shown, the microstructure of ductile iron is mainly composed of three minerals: pearlite, ferrite, and spheroidal graphite. Pearlite and ferrite are usually referred to as the matrix of ductile iron, while spheroidal graphite is generally randomly distributed in the ferrite matrix. Figure 3 Pearlite appears as a darker region in the matrix under a microscope, ferrite as a brighter region, and spheroidal graphite as black dots surrounded by ferrite. Therefore, the compositional distribution of pearlite, ferrite, and graphite particles can be determined based on their distribution area under the microscope.
[0069] Step S1012: Based on the component distribution information of the pearlite, the ferrite, and the graphite particles, determine the first component percentage and first quantity of the ferrite, the second component percentage and second quantity of the graphite particles, and the third component percentage of the pearlite.
[0070] In this embodiment of the disclosure, such as Figure 3 As shown, ferrite and graphite particles have specific shapes and are easily distinguishable from the surrounding matrix, while pearlite has no clear shape and is difficult to distinguish from the matrix. Therefore, it is only necessary to determine the proportion of pearlite in the microscopic image based on the composition distribution information of pearlite, which is taken as the third component proportion of pearlite. Based on the composition distribution information of ferrite and graphite particles, the proportion and number of ferrite and graphite particles in the microscopic image are determined respectively, thus obtaining the first component proportion and first number of ferrite, and the second component proportion and second number of graphite particles.
[0071] In this embodiment, since the size of the modeling image rectangle, the size of ferrite, and the diameter of graphite particles need to be set in actual modeling, the two-dimensional model needs to consider the composition ratio and quantity of pearlite, ferrite, and graphite under real conditions. The composition ratio and quantity can be obtained from the metallographic observation of ductile iron, typically identified according to the standard "Metallographic Examination of Ductile Iron (GB / T 9441-2009)". Then, preset image processing software is used to statistically analyze the area information of the three types of particles: pearlite, ferrite, and graphite. Typically, uncorroded microscopic images are used to statistically analyze the area and quantity of graphite particles, while corroded microstructures are used to statistically analyze the area and distribution information of pearlite and ferrite. After obtaining the area distribution of the mineral particles, the quantity and average area of ferrite, i.e., n1 and n2, can be calculated. Similarly, the number and average area of graphite particles can be defined as n² and n².
[0072] Optionally, the operation of statistically analyzing the compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample described above in this embodiment of the present disclosure may specifically include:
[0073] The compositional distribution information of the graphite particles was statistically analyzed using uncorroded ductile iron samples; the compositional distribution information of the pearlite and the ferrite was statistically analyzed using corroded ductile iron samples.
[0074] In this embodiment, the compositional characteristics of graphite particles, pearlite, and ferrite can be used. For example, graphite particles are easily corroded by acid; therefore, when analyzing graphite particles, uncorroded ductile iron samples are needed to determine their compositional distribution. Pearlite and ferrite, on the other hand, are not easily corroded; therefore, when analyzing pearlite and ferrite, corroded ductile iron samples can be used to determine their compositional distribution. Specifically, identification and analysis can be performed according to the standard "Metallographic Examination of Ductile Iron (GB / T 9441-2009)".
[0075] Optionally, in this embodiment of the disclosure, the operation of determining the target shape and target size of the mineral structure for modeling based on the property characteristics of the mineral structure, the proportion of the target component, and the target quantity is as follows: Figure 4 As shown, it can specifically include:
[0076] Step S1021: Determine the first shape of the ferrite and the second shape of the graphite particles based on their property characteristics.
[0077] In this embodiment, since ferrite in cast iron graphite exhibits an irregular polygonal shape, and hexagons are common polygons in nature, the shape of ferrite can be defined as hexagonal in the modeling. In current ductile iron production, most detected graphite particles are circular; therefore, the shape of graphite particles can be defined as circular in the modeling. Furthermore, metallographic observation shows that the geometric center of graphite particles often coincides with the geometric center of ferrite, and one graphite particle exists within one ferrite particle. Therefore, in the modeling, graphite particles can be set as circular particles of different diameters, representing graphite particles of different shapes and sizes, placed within the regular hexagonal ferrite, and sharing a geometric center with it; that is, the geometric centers of ferrite and graphite particles are concurrent. According to metallographic observation, pearlite belongs to the matrix of ductile iron; therefore, in the modeling, the matrix other than ferrite and graphite particles can be represented as pearlite, and the shape and size of pearlite are related to the shape and size of the surrounding ferrite and graphite.
[0078] Step S1022: Based on the first component ratio and first quantity of the ferrite and the second component ratio and second quantity of the graphite particles, determine the first size of the ferrite in the first shape and the second size of the graphite particles in the second shape.
[0079] In this embodiment of the disclosure, the proportion of the first component of ferrite can be determined. After calculating the first quantity n1, the radius R1 of the circumscribed circle of each regular hexagonal ferrite is calculated from the average area. This determines the first dimension of the ferrite within the regular hexagon. Since the radius of graphite particles is almost continuous after statistical analysis according to the standard "Metallographic Examination of Ductile Iron (GB / T 9441-2009)," it's impossible to statistically determine the radius of every single graphite particle. Therefore, the radii of the graphite particles can be grouped. The number of groups is determined according to simulation requirements, and the average of the radii in each group is taken as the radius R2 representing the graphite particle in that group. It should be noted that since the regular hexagonal ferrite encloses the graphite particles and the ferrite and graphite particles are concentric (i.e., each ferrite contains one graphite particle), the number of ferrite particles and the number of graphite particles can be the same, resulting in n1 = n2. Furthermore, the proportion of the first component of ferrite is greater than the proportion of the second component of graphite particles, which leads to... This allows us to determine the circular shape of graphite particles at different radius orders.
[0080] In this embodiment, after statistical analysis according to the standard "Metallographic Examination of Ductile Iron (GB / T 9441-2009)" and the preset image processing software (ImageJ), the area ratios of the three mineral structures—spheroidal graphite, ferrite, and pearlite—can be determined as shown in Table 1 below. Assuming the rectangular frame size of the modeling image is 1×0.5mm, the number of ferrite particles in the modeling image is determined to be 70, with an average diameter of 0.04mm, and the diameter of the graphite particles is almost entirely concentrated between 0.010 and 0.045mm. Specifically, the diameter of the graphite particles can be grouped according to a spacing of 0.005 mm. That is, the first group has a diameter of 0.010–0.015 mm, the second group has a diameter of 0.015–0.020 mm, the third group has a diameter of 0.020–0.025 mm, the fourth group has a diameter of 0.025–0.030 mm, the fifth group has a diameter of 0.030–0.035 mm, and the sixth group has a diameter of 0.035–0.040 mm. The average diameters of each group are 0.0125 mm, 0.0175 mm, 0.0225 mm, 0.0275 mm, 0.0325 mm, and 0.0375 mm, respectively. (Example) Figure 5 This is a schematic diagram of the graphite particle diameter frequency distribution provided in this disclosure, as shown below. Figure 5 As shown, it can be determined that the graphite particles with a diameter of 0.025–0.030 mm are the most abundant, while those with a diameter of 0.010–0.015 mm are the least abundant.
[0081] Table 1
[0082]
[0083] In this embodiment of the disclosure, due to human factors in data statistics and selection, the distribution information of each mineral structure in the metallographic statistics cannot be completely matched during the modeling process. Therefore, the final analysis results can be roughly calculated, as shown in Table 2 below. The number of ferrites is 70, the radius of the circumcircle of the regular hexagon is 0.02 mm, and the radius and number of graphite have been described in detail in Table 2 and will not be repeated here.
[0084] Table 2
[0085]
[0086] Optionally, in this embodiment of the present disclosure, the operation of modeling the mineral structure according to the target quantity, the target shape, and the target size using a preset finite element simulation method to generate a target microstructure modeling diagram of the ductile iron under a preset modeling style may specifically include:
[0087] The first number of target ferrite, the second number of target graphite particles, and the pearlite of the third component proportion are modeled using the preset finite element simulation method to generate a target microstructure modeling diagram of the ductile iron under the preset modeling style; the target ferrite is ferrite of the first size and the first shape, and the target graphite particles are graphite particles of the second size and the second shape.
[0088] In this embodiment of the disclosure, the preset finite element simulation method can be finite element software for engineering simulation (ABAQUS). Specifically, a first number of target ferrites, a second number of target graphite particles, and a third component proportion of pearlite can be input into the preset finite element simulation software to generate a target microstructure modeling diagram of randomly distributed mineral structures in a preset modeling style. The target ferrites can be ferrites with a first size and a first shape, and the target graphite particles can be graphite particles with a second size and a second shape.
[0089] Example, Figure 6 This is a schematic diagram of a target microstructure modeling diagram provided in this disclosure, such as... Figure 6 As shown, the microstructure model of ductile iron under the preset modeling style includes graphite particles 11, ferrite 12, and pearlite 13, with the three mineral structures randomly distributed. The ferrite 12 particles are hexagonal in shape, with a maximum number of 240. The graphite particles 11 include eight different graphite radius sizes, and the radius of each graphite particle cannot exceed the circumcircle radius of the regular hexagonal ferrite particle (R1 > R2). Furthermore, the number of graphite particles of each size cannot exceed 30 and must match the number of ferrite particles. It should be noted that the total number of graphite particles 11 must be the same as the total number of hexagonal ferrite particles 12, because the modeling logic assumes that the graphite particles 11 and hexagonal ferrite particles 12 are concentric.
[0090] It should be noted that after obtaining the modeling data of ductile iron, manual modeling is time-consuming, labor-intensive, and inefficient due to the random distribution and varying sizes of the particles, making it unsuitable for widespread use. However, ABAQUS, a commonly used finite element method (FEM) software for engineering simulation, uses Python for command flow control. Therefore, this disclosure provides secondary development modeling code based on the microstructure of ductile iron and creates an ABAQUS graphical user interface (GUI) plugin for convenient, efficient, and rapid modeling. When creating an ABAQUS GUI plugin, the RSG dialog builder or the ABAQUS GUI toolkit is typically used. The former is convenient and fast, while the latter is suitable for efficient modeling with various complex functions. This disclosure uses the RSG dialog builder. In general, ABAQUS plugins consist of a registration file (named xxx_piugin.py), a graphical interface file (named xxxDB.py), and a kernel executable file (usually xxx.py). The registration file checks the validity of various programming data and registers it in the ABAQUS plugins. The graphical interface defines the graphical framework and comments to facilitate user input and improve operational efficiency. The kernel executable file is used to implement the CAE modeling process.
[0091] It should be noted that the microstructure modeling method provided in this disclosure can be executed by a microstructure modeling device, or a control module within that microstructure modeling device for executing the microstructure modeling method. This disclosure uses the microstructure modeling device executing the microstructure modeling method as an example to illustrate the microstructure modeling method provided in this disclosure. Next, refer to... Figure 6 A microstructure modeling apparatus according to an exemplary embodiment of the present disclosure will be described.
[0092] Figure 7 This is a block diagram of a microstructure modeling apparatus according to embodiments of the present disclosure, such as... Figure 7 As shown, the microstructure modeling device 20 may include:
[0093] The acquisition module 301 is used to acquire the target component ratio and target quantity of the mineral structure in the ductile iron sample based on the mineral structure contained in the ductile iron.
[0094] The determining module 302 is used to determine the target shape and target size of the mineral structure for modeling based on the property characteristics of the mineral structure, the proportion of the target component, and the target quantity;
[0095] The generation module 303 is used to model the mineral structure according to the target quantity, the target shape and the target size using a preset finite element simulation method, and generate a target microstructure modeling diagram of the ductile iron under a preset modeling style.
[0096] In summary, the microstructure modeling apparatus provided in this embodiment can first obtain the target component proportion and target quantity of the mineral structure contained in the ductile iron sample, and then determine the target shape and target size of the mineral structure for modeling based on the property characteristics of the mineral structure and the target component proportion and target quantity. Using a preset finite element simulation method, the mineral structure is modeled according to the target quantity, target shape, and target size, generating a target microstructure modeling diagram of the ductile iron under a preset modeling style. In this way, determining the distribution components and quantity of the mineral structure based on a real ductile iron sample makes the distribution and quantity of mineral components more consistent with the characteristics of real ductile iron, improving the accuracy of determining the component distribution and quantity. Furthermore, using finite element simulation to model according to the shape and component distribution of the mineral structure makes the resulting ductile iron microstructure modeling diagram more closely resemble the actual microstructure of ductile iron, improving the realism of the microstructure modeling. This allows for accurate analysis of stress and strain distribution and other characteristics during subsequent cutting based on the microstructure modeling diagram.
[0097] Optionally, the mineral structure includes pearlite, ferrite, and graphite particles, and the acquisition module 301 is further used for:
[0098] The compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample was statistically analyzed.
[0099] Based on the component distribution information of the pearlite, the ferrite, and the graphite particles, the first component percentage and first quantity of the ferrite, the second component percentage and second quantity of the graphite particles, and the third component percentage of the pearlite are determined respectively.
[0100] Optionally, the acquisition module 301 is further configured to:
[0101] The compositional distribution information of the graphite particles was statistically analyzed using uncorroded ductile iron samples.
[0102] The compositional distribution information of pearlite and ferrite was statistically analyzed using the etched ductile iron sample.
[0103] Optionally, the determining module 302 is further configured to:
[0104] Based on the property characteristics of the ferrite and the graphite particles, the first shape of the ferrite and the second shape of the graphite particles are determined.
[0105] Based on the first component proportion and first quantity of the ferrite and the second component proportion and second quantity of the graphite particles, a first size of the ferrite in the first shape and a second size of the graphite particles in the second shape are determined.
[0106] Optionally, the generation module 303 is further configured to:
[0107] The first number of target ferrite, the second number of target graphite particles, and the pearlite of the third component proportion are modeled using the preset finite element simulation method to generate a target microstructure modeling diagram of the ductile iron under the preset modeling style; the target ferrite is ferrite of the first size and the first shape, and the target graphite particles are graphite particles of the second size and the second shape.
[0108] Having introduced the microstructure modeling method and apparatus of exemplary embodiments of this disclosure, the following will refer to... Figure 8 The storage medium of the exemplary embodiments of this disclosure will be described.
[0109] refer to Figure 8 As shown, a storage medium 400 for implementing the above-described method according to an embodiment of the present disclosure is described. This medium may be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a device such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0110] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0111] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0112] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0113] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0114] Having described the storage medium of exemplary embodiments of this disclosure, the following references are made. Figure 9 An electronic device according to an exemplary embodiment of the present disclosure will be described.
[0115] Figure 9 The electronic device 500 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0116] like Figure 9 As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.
[0117] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 510 may perform step S101: obtaining the target component proportion and target quantity of the mineral structure in the ductile iron sample based on the mineral structure contained in the ductile iron; step S102: determining the target shape and target size of the mineral structure for modeling based on the attribute characteristics of the mineral structure, the target component proportion, and the target quantity; and step S103: modeling the mineral structure according to the target quantity, the target shape, and the target size using a preset finite element simulation method, generating a target microstructure modeling diagram of the ductile iron under a preset modeling style.
[0118] Storage unit 520 may include volatile storage units, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include read-only memory (ROM) 5203.
[0119] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0120] Bus 530 may include a data bus, an address bus, and a control bus.
[0121] Electronic device 500 can also communicate with one or more external devices 60 (e.g., keyboard, pointing device, Bluetooth device, etc.) via input / output (I / O) interface 550. Electronic device 500 also includes a display unit 540 connected to input / output (I / O) interface 550 for display purposes. Furthermore, electronic device 500 can communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0122] It should be noted that although several modules or sub-modules of the audio playback device and audio sharing device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.
[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.
[0124] The embodiments of this disclosure have been described above with reference to the accompanying drawings. However, this disclosure is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this disclosure without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this disclosure.
Claims
1. A method for modeling microstructures, characterized in that, The method, applied to the microstructure of ductile iron, includes: Based on the mineral structure contained in the ductile iron, the target component proportion and target quantity of the mineral structure in the ductile iron sample are obtained; wherein, the mineral structure includes pearlite, ferrite and graphite particles; Based on the property characteristics of the mineral structure, the proportion of the target component, and the quantity of the target component, the target shape and target size of the mineral structure for modeling are determined; Using a preset finite element simulation method, the mineral structure is modeled according to the target quantity, target shape, and target size, generating a target microstructure modeling diagram of the ductile iron under a preset modeling style; The step of obtaining the target component proportion and target quantity of the mineral structure in the ductile iron sample includes: statistically analyzing the component distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample; and determining the first component proportion and first quantity of ferrite, the second component proportion and second quantity of graphite particles, and the third component proportion of pearlite based on the component distribution information of pearlite, ferrite, and graphite particles, respectively. The step of statistically analyzing the compositional distribution information of pearlite, ferrite, and graphite particles in the ductile iron sample includes: statistically analyzing the compositional distribution information of graphite particles using an uncorroded ductile iron sample; and statistically analyzing the compositional distribution information of pearlite and ferrite using a corroded ductile iron sample. The step of determining the target shape and target size of the mineral structure for modeling based on the property characteristics of the mineral structure, the proportion of the target component, and the target quantity includes: Based on the property characteristics of the ferrite and the graphite particles, the first shape of the ferrite and the second shape of the graphite particles are determined. Based on the first component proportion and first quantity of the ferrite and the second component proportion and second quantity of the graphite particles, a first size of the ferrite in the first shape and a second size of the graphite particles in the second shape are determined.
2. The method according to claim 1, characterized in that, The process of modeling the mineral structure using a preset finite element simulation method according to the target quantity, target shape, and target size, and generating a target microstructure modeling diagram of the ductile iron under a preset modeling style, includes: The first number of target ferrite, the second number of target graphite particles, and the pearlite of the third component proportion are modeled using the preset finite element simulation method to generate a target microstructure modeling diagram of the ductile iron under the preset modeling style; the target ferrite is ferrite of the first size and the first shape, and the target graphite particles are graphite particles of the second size and the second shape.