Casting solidification simulation optimization method and equipment based on calibration test block

Abstract

The present application discloses a casting solidification simulation optimization method and device based on a calibration test block, the method comprising: constructing a plurality of cross regions of different sizes and shapes to obtain a calibration test block containing a plurality of thermal nodes; adding at least one annular structure at one end of the calibration test block; selecting or constructing two thermal nodes of the same size on the calibration test block, and constructing a cooling device on one thermal node; constructing a riser at the largest thermal node in the calibration test block; casting based on the geometric shape of the constructed calibration test block, and performing defect detection on the physical object of the calibration test block; based on the defect detection results, correcting and optimizing one or more simulation physical parameters. Through the scheme of the present application, the simulation physical parameters can be corrected in a targeted manner to improve the accuracy of the simulation.

Description

Technical Field

[0001] The present application relates to the field of simulation calculation, and in particular to a casting solidification simulation optimization method and equipment based on a calibration test block. Background Art

[0002] Computer simulation solidification simulation technology is an important auxiliary design software in the casting process design process. It can perform virtual manufacturing. The location and size of shrinkage holes and shrinkage defects that may appear on the casting can be predicted without actual production. Casting process personnel can optimize the process based on the simulation results, thereby saving trial production time and development costs for casting production. Computer simulation solidification simulation software is based on the solution of physical equations with parameters that have been digitized for physical phenomena, and outputs the results after visualization with parameter control. Therefore, there are three main factors that affect the accuracy of the simulation results: whether the physical equations are accurate; whether the input parameters accurately reflect the actual situation; and whether the parameters of the visualization processing are accurate. Generally, simulation software comes with a database that provides some physical parameters.

[0003] However, in actual production, various materials and physical phenomena are encountered. The default physical equations of the software and the parameters provided in the database may be quite different from the specific foundry process environment and specific materials. In this way, the results calculated based on the default equations and parameters of the software will be different from the actual results. There will be defects at the locations where the simulation calculation shows defects, or the defects are not calculated at the locations where the defects actually appear, or the size of the defects calculated in the simulation is different from the actual defect size. This will cause quality problems in the process specified based on the simulation calculation results, resulting in quality scrap, or excessive process measures are adopted, increasing costs. Generally, the simulation results can be verified by test blocks of typical shapes, such as cuboids or cubes, but these test blocks have simple shapes, too few heat nodes, and the parameters verified in one experiment are too single, making it impossible to perform multi-parameter adjustment of the simulation process.

[0004] Therefore, there is an urgent need for a technical solution that can specifically correct the simulation physical parameters and improve the accuracy of the simulation. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the embodiment of the present application provides a casting solidification simulation optimization method and device based on a calibration test block. The present application solves the technical problems that the prior art cannot perform multi-parameter calibration of the simulation process.

[0006] The embodiment of the present application provides a casting solidification simulation optimization method based on a calibration test block, including: constructing a plurality of intersection areas of different sizes and shapes to obtain a calibration test block containing a plurality of thermal nodes; adding at least one annular structure at one end of the calibration test block; selecting or constructing two thermal nodes of the same size on the calibration test block, and constructing a cooling device on one thermal node; constructing a riser at the largest thermal node in the calibration test block; casting based on the geometric shape of the constructed calibration test block, and performing defect detection on the actual calibration test block; based on the defect detection results, correcting and optimizing one or more simulation physical parameters.

[0007] In a possible implementation, casting is performed based on the geometric shape of the constructed calibration test block, including: according to the constructed three-dimensional model of the calibration test block, a model is made using three-dimensional printing technology, and then a sand mold is prepared using the model to replicate the geometric details of the test block model, wherein the geometric details include hot spots, annular structures, cooling devices and risers; the same material as the simulation setting is selected, the metal is melted in an induction furnace, and a modifier and an inoculant are prepared to determine the composition and quality of the required molten metal; after pouring the molten metal into a ladle and quickly measuring the temperature to confirm the temperature, the molten metal is poured into the prepared sand mold, and the actual temperature and time parameters of the casting process are recorded; the test block is left to stand to cool naturally at room temperature, and then the sand mold is opened to remove the molding sand remaining on the surface of the test block to obtain the actual calibration test block.

[0008] In a possible implementation, the defect detection of the physical object of the calibration test block includes: performing non-destructive testing on the physical object of the calibration test block, including using industrial CT equipment to perform X-ray scanning on the entire test block, and / or using phased array ultrasonic equipment to detect internal defects of the test block; cutting the test block along a predetermined position to expose the internal structure, and the cutting position includes the center of each hot node, the annular structure, the heat dissipation ribs and the target area around the riser; selecting a sample on the cutting surface to prepare a metallographic specimen; using a metallographic microscope to observe the microstructure of the metallographic specimen, including the defect characteristics of dendrite spacing, shrinkage cavities and shrinkage porosity; measuring the defect size and distribution to calculate the defect rate of each hot node area. Using a Brinell hardness tester to perform hardness testing; recording the defect type, size, location and distribution, as well as the shrinkage compensation distance of the annular structure, and comparing the defect differences between hot nodes with and without cooling devices.

[0009] In a possible implementation, based on the defect detection results, one or more simulated physical parameters are corrected and optimized, including: comparing the defect detection results of the physical test block with the simulation predictions; based on the comparative analysis results, one or more simulated physical parameters are corrected and optimized.

[0010] In a possible implementation, the defect detection result of the physical test block is compared with the simulation prediction, including: superimposing the defect distribution map of the physical test block obtained by X-ray and ultrasonic detection with the defect distribution map predicted by simulation, calculating the position fit, wherein the position fit is equal to the percentage of the overlapping area of ​​the actual defect and the predicted defect divided by the total area of ​​the predicted defect; for each thermal node, measuring the actual defect size and comparing it with the simulation prediction value, calculating the size error, wherein the size error is equal to the absolute value of the difference between the actual defect size and the predicted defect size divided by the percentage of the actual defect size; drawing a relationship curve between the thermal node size and the defect rate, and comparing the actual curve with the simulation curve; measuring the annular The actual compensation distance in the structure is compared with the simulation prediction value, and the compensation distance error is calculated, where the compensation distance error is equal to the absolute value of the difference between the actual compensation distance and the predicted compensation distance divided by the percentage of the actual compensation distance; the difference in defect rates of hot nodes with and without cooling devices is compared, and the heat dissipation effect coefficient is calculated, where the heat dissipation effect coefficient is equal to the defect rate without cooling device minus the defect rate with cooling device divided by the defect rate without cooling device; the solidification time of each hot node predicted by simulation is compared with the actual solidification time obtained by metallographic analysis, and the time prediction error is calculated, where the time prediction error is equal to the absolute value of the difference between the actual solidification time and the predicted solidification time divided by the percentage of the actual solidification time.

[0011] In a possible implementation, based on the comparative analysis results, one or more simulation physical parameters are corrected and optimized, including: adjusting the specific heat capacity and latent heat parameters of the material according to the solidification time error to correct the material thermal physical property parameters. When the actual solidification time is longer than the predicted value, the specific heat capacity and latent heat parameters are increased according to the preset ratio; based on the defect position fit, the metal-mold interface heat transfer coefficient is adjusted; when the actual defect position is close to the surface, the interface heat transfer coefficient is increased according to the preset formula to correct the heat transfer effect; according to the defect size error, the critical solid fraction parameter for determining shrinkage defects is corrected; when the actual defect size is smaller than the predicted value, the critical solid fraction threshold is increased by the preset formula; based on the feeding distance error, the material's feeding capacity parameter is adjusted; when the actual feeding distance is shorter than the predicted value, the material's feeding capacity parameter is reduced according to the preset ratio to optimize the feeding effect; according to the heat dissipation effect coefficient, the equivalent thermal conductivity parameter of the heat dissipation rib is adjusted; the equivalent thermal conductivity of the heat dissipation rib is corrected by the preset formula to optimize the heat dissipation effect; based on the results of the thermal node sensitivity analysis, the defect judgment threshold parameter is corrected; using the fitted defect rate curve parameters, the defect judgment standard is adjusted by the preset formula to optimize the defect prediction accuracy; according to the degree of defect detail capture, the mesh density parameter of the key area is adjusted; when small defects are not effectively captured, the mesh density of the area is increased by the preset formula to optimize the simulation accuracy.

[0012] In a possible implementation, a relationship curve between the thermal node size and the defect rate is plotted, and the actual curve is compared with the simulation curve, including: ,in, Indicates the starting threshold, the value range is 0.025-0.07, Indicates the growth rate, the value range is 0.15-0.3, Indicates the saturation value, ranging from 0.7 to 0.99.

[0013] An embodiment of the present application also provides a casting solidification simulation optimization device based on a calibration test block, characterized in that it includes: a processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method described in the above embodiment.

[0014] In a casting solidification simulation optimization method and device based on a calibration test block as provided above, the embodiment of the present application can simultaneously evaluate the prediction ability of the simulation software for multiple casting conditions in one test by designing a calibration test block containing multiple characteristic structures. The characteristic structures such as the cross-region, annular structure, cooling device and riser in the calibration test block simulate the complex geometric shapes and process designs commonly seen in actual castings, which improves the representativeness and comprehensiveness of the calibration process. By comparing the defect detection results of the physical test block with the simulation prediction, the simulation physical parameters can be corrected in a targeted manner to improve the accuracy of the simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A schematic diagram of a flow chart of a casting solidification simulation optimization method based on a calibration test block provided in an embodiment of the present application;

[0017] Figure 2 A schematic diagram of a calibration test block provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0019] Those skilled in the art can understand that the terms "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor represent the necessary logical order between them. It should also be understood that in the embodiments of the present disclosure, "multiple" can refer to two or more, and "at least one" can refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are an "or" relationship. It should also be understood that the description of each embodiment in the present disclosure emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one.

[0020] At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered part of the specification. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] Figure 1 A schematic diagram of a flow chart of a casting solidification simulation optimization method based on a calibration test block provided in an embodiment of the present application. The method aims to optimize the physical parameters in the casting solidification simulation by constructing a specific calibration test block and combining physical experiments and simulation analysis, thereby improving the simulation accuracy and reliability.

[0023] First of all, it is important to understand that casting solidification simulation refers to the use of computers to simulate the solidification behavior of castings during the pouring process, including changes in temperature field, flow field, stress field, etc. This simulation technology plays a key role in casting process design and optimization. It can predict possible defects in castings, such as shrinkage cavities, shrinkage porosity, thermal cracking, etc., thereby guiding process improvements. However, the accuracy of the simulation results depends largely on the accuracy of the physical parameters used in the simulation model. Therefore, this method designs a special calibration test block (such as Figure 2 As shown in Figure 3), these physical parameters are optimized by combining physical tests and simulation analysis.

[0024] like Figure 1 As shown, at step S101, multiple intersection areas of different sizes and shapes are constructed to obtain a calibration test block containing multiple thermal nodes. The "thermal node" here refers to the area in the casting where heat is concentrated and cooling is slow, which is usually the location where defects are easily formed. Constructing intersection areas of different sizes and shapes can simulate various thermal node conditions that may occur in actual castings. These intersection areas may include but are not limited to T-shaped, cross-shaped, Y-shaped and other structures, and their sizes can be designed according to the characteristics of the actual casting. In this way, multiple thermal node conditions can be simulated in one test block to improve the calibration efficiency.

[0025] In one embodiment, the thermal nodes are formed by intersection areas, for example, a rectangular base is designed with dimensions of 300mm x 200mm x 50mm. A 3x3 grid is designed on the top of the base. The side lengths range from 20mm to 60mm, and the heights range from 30mm to 90mm. Connecting plates are added between the cylinders with a thickness of 15mm. These connecting plates will form additional thermal nodes at the intersections. By designing the size of the connecting plates, the size of the thermal nodes formed ranges from 20mm x 30mm to 60mm x90mm, covering the range of thermal node sizes for common castings. With this design, thermal nodes of various sizes and shapes can be simulated in one test block, providing a basis for comprehensive software tuning.

[0026] At step S102, at least one annular structure is added to one end of the calibration test block. The function of the annular structure is to simulate the circular or annular structure that may exist in the actual casting, such as a bearing seat, flange, etc. The presence of the annular structure can affect the flow and solidification process of the molten metal, so incorporating it into the calibration test block design can more comprehensively evaluate the accuracy of the simulation model. The annular structure can be designed as a single ring or multiple rings, and its size and position can be adjusted according to actual needs.

[0027] In one embodiment, a ring structure is designed on one side of the base, with an inner diameter of 200mm, an outer diameter of 250mm, and a height of 60mm. The ring wall thickness is 25mm, forming a uniform heat node. A larger riser is set on one side of the ring, with a diameter of 80mm and a height of 100mm. The ring structure is connected to the base through a 30mm x 30mm beam. This ring structure can be used to observe the material's feeding capacity and feeding distance, and provide a basis for the feeding model of the calibration software.

[0028] At step S103, two hot spots of the same size are selected or constructed on the calibration test block, and a cooling device is constructed on one of the hot spots. The purpose of this step is to compare the effect of the cooling device on the solidification process of the hot spot. The cooling device is a commonly used process measure to improve the solidification of castings, which accelerates the cooling of the hot spot by increasing the heat dissipation area. By setting and not setting a cooling device on hot spots of the same size, the effect of the cooling device can be intuitively observed and compared, thereby verifying whether the simulation model can accurately predict the effect of the cooling device.

[0029] At step S104, a riser is constructed at the maximum thermal node in the calibration test block. The riser is an important process measure in the casting process for compensating for metal shrinkage and preventing shrinkage cavities and shrinkage porosity. Setting a riser at the maximum thermal node can test whether the simulation model can accurately predict the shrinkage compensation effect of the riser. The design of the riser can include an open riser or a closed riser, and its size and shape should be calculated and optimized according to the size and characteristics of the thermal node. In this way, the accuracy of the simulation model in predicting the riser effect can be evaluated.

[0030] In one embodiment, a cylindrical riser is designed on the top of the hot node, with a diameter of 1.2 times the diameter of the hot node, i.e. 72 mm. The riser height is set to 1.5 times the height of the hot node, i.e. 135 mm. A neck transition zone can be set between the riser and the hot node, with a height of 15 mm and a diameter gradually transitioning from 60 mm to 72 mm. Adding this riser can study the effect of the riser on the maximum hot node defect and provide a basis for the riser design model of the tuning software.

[0031] At step S105, casting is performed based on the geometric shape of the constructed calibration test block, and the actual object of the calibration test block is detected for defects. After the construction of the calibration test block is completed, the embodiment of the present application proposes casting based on the geometric shape of the constructed calibration test block, and detecting defects in the actual object of the calibration test block. This step is to convert the designed calibration test block into a physical object, and verify the effectiveness of the calibration test block through the actual casting process. During the casting process, it is necessary to reasonably design the casting system according to the geometric shape of the calibration test block, including runners, runners, risers, etc., to ensure smooth filling of the metal liquid and a reasonable solidification order. After the casting is completed, it is necessary to detect defects in the actual object of the calibration test block. Visual inspection, radiographic inspection, ultrasonic detection and other methods can be used to comprehensively evaluate the defects inside the calibration test block, providing a basis for subsequent simulation optimization.

[0032] The pouring process should be carried out strictly in accordance with the designed process parameters, including but not limited to pouring temperature, pouring speed and pouring system design. Defect detection can adopt a variety of non-destructive testing methods, such as X-ray testing, ultrasonic testing or computer tomography (CT). The focus of the inspection should be on the hot spots, cooling device areas and near the risers to fully evaluate the actual solidification of these characteristic structures. Defect detection should record detailed information such as the type, location, size and distribution of defects to provide accurate reference data for subsequent simulation optimization.

[0033] Among them, casting is performed based on the geometric shape of the constructed calibration test block, including: according to the constructed three-dimensional model of the calibration test block, a model is made by using three-dimensional printing technology, and then a sand mold is prepared by using the model to replicate the geometric details of the test block model, wherein the geometric details include thermal nodes, annular structures, heat dissipation ribs and risers; the same material as the simulation setting is selected, the metal is melted by an induction furnace, and a modifier and an inoculant are prepared to determine the composition and quality of the required molten metal; after pouring the molten metal into a ladle and quickly measuring the temperature to confirm the temperature, the molten metal is poured into the prepared sand mold, and the actual temperature and time parameters of the pouring process are recorded; the test block is left to stand to cool naturally at room temperature, and then the sand mold is opened to remove the residual molding sand on the surface of the test block to obtain the actual calibration test block.

[0034] In one implementation scenario, a sand mold is made according to a 3D model. A model can be made using 3D printing technology, and then a sand mold is made using this model. Ensure that all details of the mold, especially multiple hot nodes, annular structures, and heat dissipation ribs, are accurately replicated. Prepare for pouring and select the same material as the simulation, such as carbon steel (ASTM A216 WCB). Use an induction furnace to melt the metal and control the temperature to 1550±10℃. Prepare an appropriate amount of modifier and inoculant to ensure the quality of the metal. During the pouring process, pour the molten metal into the ladle and quickly measure the temperature to ensure the temperature. Complete the pouring within 10 seconds, which is consistent with the simulation settings. Record the actual pouring temperature, time and other parameters. Cooling and cleaning, naturally cool to room temperature, which takes about 24 hours. Carefully open the mold and clean the molding sand on the surface of the test block.

[0035] After obtaining the physical object, the physical object of the calibration test block is subjected to defect detection, including: non-destructive testing of the physical object of the calibration test block, including using industrial CT equipment to perform X-ray scanning on the entire test block, and / or using phased array ultrasonic equipment to detect internal defects of the test block; cutting the test block along a predetermined position to expose the internal structure, and the cutting position includes the center of each hot node, the annular structure, the heat dissipation ribs and the target area around the riser; selecting samples on the cutting surface to prepare metallographic specimens; using a metallographic microscope to observe the microstructure of the metallographic specimen, including the defect characteristics of dendrite spacing, shrinkage cavities and shrinkage porosity; measuring the defect size and distribution to calculate the defect rate of each hot node area. Use a Brinell hardness tester to perform hardness testing; record the defect type, size, location and distribution, as well as the shrinkage compensation distance of the annular structure, and compare the defect differences between hot nodes with and without cooling devices.

[0036] In another implementation scenario, a comprehensive defect inspection is performed on the cast physical test pieces:

[0037] Non-destructive testing:

[0038] X-ray inspection: Use industrial CT equipment (such as YXLON FF35 CT system) to scan the entire test block. Set parameters: voltage 225kV, current 3mA, exposure time 2000ms.

[0039] Ultrasonic testing: Use a phased array ultrasonic device (such as Olympus OmniScan SX) to detect internal defects. Set the frequency to 5MHz and the scanning step to 1mm.

[0040] Cutting sampling:

[0041] Use a wire cutting machine to cut the test block along the predetermined position to expose the internal structure.

[0042] The cutting positions include: the center of each thermal node, the annular structure, the heat dissipation ribs and other key areas.

[0043] Metallographic analysis:

[0044] Samples were selected from the cut surface for grinding, polishing and etching.

[0045] The microstructure was observed using a metallographic microscope (such as Leica DMI5000 M).

[0046] Focus on defect characteristics such as dendrite spacing, shrinkage cavities, and shrinkage porosity.

[0047] Quantitative analysis:

[0048] Use image analysis software (such as Image-Pro Plus) to measure defect size and distribution.

[0049] Calculate the defect rate for each hot spot area: Defect rate = (defect area / total observation area) x 100%

[0050] Hardness test:

[0051] Use a Brinell hardness tester (such as Wilson UH4000) to perform hardness tests on each hot spot area.

[0052] Test parameters: indenter diameter 10mm, load 3000kg, holding time 10s.

[0053] Data Records:

[0054] Detailed records of defect type, size, location and distribution of each hot spot.

[0055] Pay special attention to defects in the largest thermal node (30mm x 30mm) and the smallest thermal node (10mm x 10mm).

[0056] Record the feeding distance of the ring structure, that is, the distance from the outer edge of the ring to the defect-free area.

[0057] Compare the defect differences between thermal nodes with and without heat dissipation ribs.

[0058] Through these detailed inspection steps, we can fully understand the defects in the actual test block, providing an accurate data basis for the subsequent comparative analysis with the simulation results.

[0059] At step S106, based on the defect detection results, one or more simulation physical parameters are corrected and optimized. After obtaining the defect detection results of the actual calibration test block, the embodiment of the present application proposes to correct and optimize one or more simulation physical parameters based on the defect detection results. Specifically, the defect detection results of the actual test block can be compared with the simulation predictions to analyze the differences and causes between the two. Based on the results of the comparative analysis, the physical parameters in the simulation model, such as thermal conductivity, specific heat capacity, latent heat, interface heat transfer coefficient, etc., can be adjusted in a targeted manner to make the simulation results closer to the actual casting process. By repeatedly iteratively optimizing the simulation parameters, the accuracy of the simulation predictions can be continuously improved, providing more reliable guidance for actual casting production.

[0060] Among them, based on the defect detection results, one or more simulation physical parameters are corrected and optimized, including: comparing the defect detection results of the physical test block with the simulation prediction; based on the comparative analysis results, one or more simulation physical parameters are corrected and optimized.

[0061] Specifically, first, the defect detection results of the physical test block are compared with the simulation prediction, including: superimposing the defect distribution map of the physical test block obtained by X-ray and ultrasonic testing with the defect distribution map predicted by simulation, and calculating the position fit, where the position fit is equal to the percentage of the overlapping area of ​​the actual defect and the predicted defect divided by the total area of ​​the predicted defect; for each hot spot, measuring the actual defect size and comparing it with the simulation prediction value, calculating the size error, where the size error is equal to the absolute value of the difference between the actual defect size and the predicted defect size divided by the percentage of the actual defect size; drawing the relationship curve between the hot spot size and the defect rate, and comparing the actual curve with the simulation curve; including:

[0062]

[0063] in, Indicates the starting threshold, the value range is 0.025-0.07, Indicates the growth rate, the value range is 0.15-0.3, It represents the saturation value, and its value range is 0.7-0.99. The actual compensation distance in the annular structure is measured, compared with the simulation prediction value, and the compensation distance error is calculated, where the compensation distance error is equal to the absolute value of the difference between the actual compensation distance and the predicted compensation distance divided by the percentage of the actual compensation distance; the difference in defect rates between the hot nodes with and without cooling devices is compared, and the heat dissipation effect coefficient is calculated, where the heat dissipation effect coefficient is equal to the defect rate without cooling device minus the defect rate with cooling device divided by the defect rate without cooling device; the solidification time of each hot node predicted by simulation is compared with the actual solidification time obtained by metallographic analysis, and the time prediction error is calculated, where the time prediction error is equal to the absolute value of the difference between the actual solidification time and the predicted solidification time divided by the percentage of the actual solidification time.

[0064] In one embodiment, comparing the test results of the physical test block with the simulation prediction in detail includes:

[0065] Defect location comparison: Overlay the X-ray and ultrasonic test results with the defect distribution map predicted by simulation. Calculate the position fit: Position fit = (overlap area of ​​actual defect and predicted defect / total predicted defect area) x100%.

[0066] Defect size comparison: For each thermal node, the actual measured defect size is compared with the simulation prediction value to calculate the size error: Size error = |actual defect size - predicted defect size| / actual defect size x 100%.

[0067] Hot-node sensitivity analysis: Draw the relationship curve between hot-node size and defect rate, and compare the actual curve with the simulation curve. Fitting curve equation: Defect rate = a* exp(b * hot-node size) + c Where a, b, c are fitting parameters, representing the starting threshold, growth rate and saturation value of defect formation, respectively.

[0068] Feed distance analysis: Measure the actual feed distance in the ring structure and compare it with the simulation prediction. Calculate the feed distance error: Feed distance error = |actual feed distance - predicted feed distance| / actual feed distance x 100%.

[0069] Heat dissipation effect analysis: Compare the difference in defect rates between heat nodes with and without heat dissipation ribs. Calculate the heat dissipation effect coefficient: Heat dissipation effect coefficient = (defect rate without heat dissipation ribs - defect rate with heat dissipation ribs) / defect rate without heat dissipation ribs.

[0070] Solidification time comparison: Compare the solidification time of each hot section predicted by simulation with the actual solidification time obtained by metallographic analysis. Calculate the time prediction error: Time prediction error = |actual solidification time - predicted solidification time| / actual solidification time x 100%.

[0071] Through these detailed comparative analyses, the prediction accuracy of the simulation software can be comprehensively evaluated, providing a clear direction for the next step of parameter adjustment.

[0072] Next, after obtaining the comparison results, based on the comparison analysis results, one or more simulation physical parameters are corrected and optimized, including: adjusting the specific heat capacity and latent heat parameters of the material according to the solidification time error to correct the material thermal physical property parameters. When the actual solidification time is longer than the predicted value, the specific heat capacity and latent heat parameters are increased according to the preset ratio; based on the degree of fit of the defect position, the metal-mold interface heat transfer coefficient is adjusted; when the actual defect position is close to the surface, the interface heat transfer coefficient is increased according to the preset formula to correct the heat transfer effect; according to the defect size error, the critical solid fraction parameter for determining shrinkage defects is corrected; when the actual defect size is smaller than the predicted value, the critical solid fraction threshold is increased by the preset formula. Based on the error of the feeding distance, adjust the material's feeding capacity parameters; when the actual feeding distance is shorter than the predicted value, reduce the material's feeding capacity parameters according to the preset ratio to optimize the feeding effect; according to the heat dissipation effect coefficient, adjust the equivalent thermal conductivity parameters of the cooling device; correct the equivalent thermal conductivity of the cooling device through the preset formula to optimize the heat dissipation effect; based on the results of the thermal node sensitivity analysis, correct the defect judgment threshold parameters; use the fitted defect rate curve parameters to adjust the defect judgment criteria through the preset formula to optimize the defect prediction accuracy; adjust the grid density parameters of the key area according to the degree of defect detail capture; when small defects are not effectively captured, increase the grid density of the area through the preset formula to optimize the simulation accuracy.

[0073] In one embodiment, based on the comparative analysis results, systematically adjusting key parameters of the simulation software includes:

[0074] Adjustment of material thermophysical parameters: According to the solidification time error, adjust the specific heat and latent heat of the material. If the actual solidification time is longer than the predicted value, increase the specific heat and latent heat. Adjustment formula: New specific heat = original specific heat * (1 + time prediction error * 0.5) New latent heat = original latent heat * (1 + time prediction error * 0.5).

[0075] Heat transfer coefficient correction: Based on the defect position fit, adjust the metal-mold interface heat transfer coefficient. If the actual defect position is close to the surface, increase the heat transfer coefficient. Adjustment formula: New heat transfer coefficient = original heat transfer coefficient * (1 + (1 - position fit) * 0.3).

[0076] Critical solid fraction adjustment: According to the defect size error, the critical solid fraction for determining shrinkage cavity is corrected. If the actual defect is smaller than the predicted value, the critical solid fraction is increased. Adjustment formula: New critical solid fraction = original critical solid fraction + size error * 0.01.

[0077] Feeding capacity parameter optimization: Based on the feeding distance error, adjust the material's feeding capacity parameters. If the actual feeding distance is shorter than the predicted value, reduce the feeding capacity. Adjustment formula: New feeding capacity = original feeding capacity * (1 - feeding distance error * 0.2).

[0078] Cooling effect correction: According to the cooling effect coefficient, adjust the equivalent thermal conductivity of the cooling ribs. Adjustment formula: New equivalent thermal conductivity = original equivalent thermal conductivity * (1 + cooling effect coefficient).

[0079] Defect judgment standard adjustment: Based on the results of the thermal node sensitivity analysis, the defect judgment threshold is modified. Use the fitted defect rate curve parameters for adjustment: New defect judgment threshold = -ln((judgment threshold - b) / a) / c, where a, b, c are the fitted defect rate curve parameters.

[0080] Mesh density optimization: adjust the mesh density of key areas according to the degree of defect detail capture. If small defects are not captured, increase the mesh density of the area. New mesh size = original mesh size * (1 - proportion of uncaptured defects * 0.5).

[0081] In addition, the optimized simulation physical parameters can be iteratively verified: re-simulate with the adjusted parameters. Compare the new simulation results with the experimental data and calculate the degree of error improvement. Error improvement rate = (original error - new error) / original error * 100%. If the error improvement rate is less than 10%, return to step 1 and make more detailed parameter adjustments. If the error improvement rate is greater than 10% but less than 30%, make 2-3 small adjustments. If the error improvement rate is greater than 30%, the adjustment is considered successful.

[0082] You can also perform parameter sensitivity analysis: perform sensitivity analysis on each adjusted parameter to determine its impact on the result. Calculate the sensitivity coefficient: S = (ΔY / Y) / (ΔX / X). Where Y is the output result (such as defect rate) and X is the input parameter.

[0083] You can also create a parameter adjustment database: record the parameters, adjustment range and corresponding effects of each adjustment, and establish the relationship between parameters and materials and process conditions.

[0084] Through this systematic tuning process, the simulation software’s predictive accuracy can be gradually improved to better suit the specific casting process and material. This process may require multiple iterations until a satisfactory level of accuracy is achieved.

[0085] This method can evaluate the prediction ability of simulation software for multiple casting situations in one test by designing a calibration test block containing multiple characteristic structures. The characteristic structures such as the cross-region, annular structure, cooling device and riser in the calibration test block simulate the complex geometric shapes and process designs commonly seen in actual castings, which improves the representativeness and comprehensiveness of the calibration process. By comparing the defect detection results of the physical test block with the simulation prediction, the simulation physical parameters can be corrected in a targeted manner to improve the accuracy of the simulation.

[0086] The method can be applied to various casting processes, including but not limited to gravity casting, pressure casting, centrifugal casting, etc. The material of the calibration test block can be selected according to actual production needs, such as cast iron, cast steel, aluminum alloy, magnesium alloy, etc. The simulation software can be commonly used casting simulation software on the market, such as ProCAST, MAGMASOFT, Flow-3D Cast, etc.

[0087] It should be noted that the design of the calibration test block should take into account the actual production conditions and equipment capabilities. The size of the test block should be moderate, which can reflect the influence of various characteristic structures, but not too large to increase the test cost. The design of the characteristic structure should cover various situations that may be encountered in actual production as much as possible to improve the applicability of the calibration results. When performing defect detection, appropriate detection methods and equipment should be selected to ensure the accuracy and reliability of the detection results.

[0088] Figure 2 A schematic diagram of a calibration block provided for an embodiment of the present application. It should be understood that the calibration block shown in the figure is exemplary and not restrictive. This means that the structure involved is not limited to a specific form or design, but is presented as an example. In other words, the structure shown in the figure can be regarded as a way of expression to clearly describe related concepts and relationships, and does not exclude other forms of calibration blocks. Therefore, when interpreting the structure in the picture, it should be understood that the model is flexible and diverse, and its purpose is to provide an exemplary description rather than a restrictive provision on a specific form. Figure 2 As shown in the figure, a plurality of hot nodes of different sizes are included, cooling devices and risers are arranged on the hot nodes, and a ring structure is arranged at one end of the test block.

[0089] Through a specially designed multi-hot spot scheme that can reflect the actual castings with different sizes of hot spots, on the basis of one experiment, it is possible to display the different defect risks and defect levels of different hot spots in castings of specific process environments and specific materials. Based on this, by comparing the calculation results with the actual experimental results of different hot spots, the physical equations, input parameters, output parameters and other physical quantities of the software are adjusted. The adjusted software can more accurately reflect the solidification characteristics and defect judgment criteria of the process environment and the specific material, so as to more accurately guide the solidification simulation and casting process. The adjustment test block can be widely used in the adjustment of simulated solidification characteristic parameters of cast steel, cast iron, and non-ferrous materials and the parameter optimization process of virtual manufacturing.

[0090] Furthermore, an embodiment of the present application also provides a casting solidification simulation optimization device based on a calibration test block, comprising: a processor, a memory, and a system bus; the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute any of the above methods.

[0091] Furthermore, an embodiment of the present application also provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute any of the above methods.

[0092] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., including several instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0093] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0094] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0095] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A casting solidification simulation optimization method based on a calibration test block, characterized in that: include: Constructing a plurality of intersection regions of different sizes and shapes to obtain a calibration test block containing a plurality of thermal nodes; Add at least one ring structure to one end of the calibration block; Select or construct two hot nodes of the same size on the calibration test block, and construct a cooling device on one hot node; Construct a riser at the largest heat node in the calibration test block; Casting is performed based on the geometric shape of the constructed calibration test block, and defect detection is performed on the actual calibration test block; Based on the defect detection results, one or more simulation physical parameters are corrected and optimized, including: comparing the defect detection results of the physical test block with the simulation prediction; based on the comparison and analysis results, one or more simulation physical parameters are corrected and optimized; Among them, the defect detection results of the physical test block are compared with the simulation prediction, including: The defect distribution map of the physical test block obtained by X-ray and ultrasonic testing is superimposed with the defect distribution map predicted by simulation, and the position matching degree is calculated, where the position matching degree is equal to the percentage of the overlapping area of ​​the actual defect and the predicted defect divided by the total area of ​​the predicted defect; For each hot spot, the actual defect size is measured and compared with the simulation prediction value, and the size error is calculated, where the size error is equal to the absolute value of the difference between the actual defect size and the predicted defect size divided by the percentage of the actual defect size; Draw the relationship curve between the hot spot size and the defect rate, and compare the actual curve with the simulation curve; The actual feeding distance in the ring structure is measured, compared with the simulation prediction value, and the feeding distance error is calculated, wherein the feeding distance error is equal to the absolute value of the difference between the actual feeding distance and the predicted feeding distance divided by the percentage of the actual feeding distance; Compare the difference in defect rates between the heat nodes with and without cooling devices, and calculate the heat dissipation effect coefficient, where the heat dissipation effect coefficient is equal to the defect rate without cooling device minus the defect rate with cooling device divided by the defect rate without cooling device; The solidification time of each hot node predicted by simulation is compared with the actual solidification time obtained by metallographic analysis, and the time prediction error is calculated, where the time prediction error is equal to the absolute value of the difference between the actual solidification time and the predicted solidification time divided by the percentage of the actual solidification time.

2. The casting solidification simulation optimization method according to claim 1, characterized in that: in, Casting based on the geometry of the constructed calibration block, including: According to the constructed three-dimensional model of the calibration test block, a model is produced by using a three-dimensional printing technology, and then a sand mold is prepared by using the model to replicate the geometric details of the test block model, wherein the geometric details include a hot spot, an annular structure, a cooling device and a riser; Select the same material as the simulation setting, use an induction furnace to melt the metal, prepare modifiers and inoculants, and determine the composition and quality of the required molten metal; After pouring the molten metal into the ladle and quickly measuring the temperature to confirm it, pour the molten metal into the prepared sand mold and record the actual temperature and time parameters of the pouring process; The test block is left to stand and allowed to cool naturally at room temperature, and then the sand mold is opened to remove the residual molding sand on the surface of the test block to obtain the actual calibration test block.

3. The casting solidification simulation optimization method according to claim 1, characterized in that: in, Conduct defect detection on the actual calibration test block, including: Performing non-destructive testing on the physical object of the calibration test block, including performing X-ray scanning on the entire test block using industrial CT equipment, and / or detecting internal defects of the test block using phased array ultrasonic equipment; Cutting the test block along a predetermined position to expose the internal structure, wherein the cutting position includes the center of each hot node, the annular structure, the cooling device and the target area around the riser; Select samples on the cut surface and prepare metallographic specimens; Observing the microstructure of the metallographic sample using a metallographic microscope, including defect characteristics of dendrite spacing, shrinkage cavities and shrinkage porosity; Measure defect size and distribution to calculate defect rate in each thermal section area, Hardness test using Brinell hardness tester; The defect type, size, location and distribution, as well as the feeding distance of the ring structure, were recorded, and the defect differences between the hot section with and without cooling device were compared.

4. The casting solidification simulation optimization method according to claim 1, characterized in that: in, Based on the comparative analysis results, one or more simulation physical parameters are modified and optimized, including: According to the solidification time error, the specific heat capacity and latent heat parameters of the material are adjusted to correct the material thermal physical property parameters. When the actual solidification time is longer than the predicted value, the specific heat capacity and latent heat parameters are increased according to the preset ratio; Adjust the metal-mold interface heat transfer coefficient based on the defect position fit; when the actual defect position is close to the surface, increase the interface heat transfer coefficient according to the preset formula to correct the heat transfer effect; According to the error of defect size, the critical solid fraction parameter for determining shrinkage defects is corrected; when the actual defect size is smaller than the predicted value, the critical solid fraction threshold is increased through the preset formula. Adjust the material’s feeding capacity parameters based on the feeding distance error; when the actual feeding distance is shorter than the predicted value, reduce the material’s feeding capacity parameters according to the preset ratio to optimize the feeding effect; According to the heat dissipation effect coefficient, adjust the equivalent thermal conductivity parameters of the heat dissipation ribs; modify the equivalent thermal conductivity of the cooling unit through a preset formula to optimize the heat dissipation effect; Based on the results of the thermal node sensitivity analysis, the defect judgment threshold parameters are modified; using the fitted defect rate curve parameters, the defect judgment criteria are adjusted through the preset formula to optimize the defect prediction accuracy; The mesh density parameters of key areas are adjusted according to the degree of defect detail capture; when small defects are not effectively captured, the mesh density of the area is increased through a preset formula to optimize the simulation accuracy.

5. The casting solidification simulation optimization method according to claim 1, characterized in that: in, Draw the relationship curve between the hot spot size and the defect rate, and compare the actual curve with the simulation curve, including: ； in, Indicates the starting threshold, the value range is 0.025-0.07, Indicates the growth rate, the value range is 0.15-0.3, Indicates the saturation value, ranging from 0.7 to 0.

99.

6. A casting solidification simulation optimization device based on a calibration test block, characterized in that: include: A processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 5.

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