Defect detection method and device for casting template, storage medium and electronic equipment

Abstract

The application discloses a casting template defect detection method and device, a storage medium and an electronic device. The method comprises the following steps: performing simulation casting on a casting template to be detected to obtain at least one casting model; determining a defect model meeting a first defect condition from the at least one casting model; determining at least one defect sub-model existing in a non-solidified state from the defect model; calculating a defect coefficient of each defect sub-model according to a respective set of casting state parameters of each defect sub-model; and determining a defect report according to the defect coefficient of each defect sub-model. The application solves the technical problem of high lag of casting defect detection in the related art.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method, apparatus, storage medium, and electronic device for detecting defects in casting templates. Background Technology

[0002] High-pressure casting, as an efficient and precise light alloy forming process, is widely used in the manufacturing of automotive power systems, key components of new energy vehicles, power equipment, and lightweight structural parts for aerospace. In these applications, casting molds are typically used to cast the parts.

[0003] However, in related technologies, defect detection in castings typically involves destructive metallographic analysis or non-destructive testing. Both of these methods can only be implemented after the casting is completed, making them post-production inspections. This means that by the time defects are identified, the casting has already been formed and the production batch may have already been produced in large quantities, making it impossible to predict or identify defects before the casting is finished. In other words, these technologies suffer from a high lag in casting defect detection.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method, apparatus, storage medium, and electronic device for detecting defects in casting templates, in order to at least solve the technical problem of high lag in casting defect detection in related technologies.

[0006] According to one aspect of the embodiments of this application, a defect detection method for a casting template is provided, comprising: simulating casting of the casting template to be detected to obtain at least one casting model, wherein different casting models among the at least one casting model are used to indicate models formed at different times; determining a defect model that satisfies a first defect condition from the at least one casting model; determining at least one defect sub-model containing molten metal in an unsolidified state from the defect model; calculating a defect coefficient for each defect sub-model based on a set of casting state parameters for each defect sub-model; and determining a defect report according to the defect coefficient for each defect sub-model, wherein the set of casting state parameters includes physical parameters of the molten metal within the defect sub-model.

[0007] According to another aspect of the embodiments of this application, a defect detection device for casting templates is also provided, comprising: a simulation unit for simulating casting of the casting template to be detected to obtain at least one casting model, wherein different casting models among the at least one casting model are used to indicate models formed at different times; a first determining unit for determining a defect model that satisfies a first defect condition from the at least one casting model; a second determining unit for determining at least one defect sub-model containing molten metal in an unsolidified state from the defect models; a calculation unit for calculating a defect coefficient for each of the defect sub-models based on a set of casting state parameters for each of the defect sub-models; and a third determining unit for determining a defect report based on the defect coefficient for each of the defect sub-models, wherein the set of casting state parameters includes physical parameters of the molten metal within the defect sub-models.

[0008] As an optional solution, the above-mentioned apparatus further includes: a first determining module, used to sequentially use each of the at least one defect sub-model as a target defect sub-model; a second determining module, used to determine a first defect coefficient of the target defect sub-model based on the casting state parameter set of the target defect sub-model, wherein the first defect coefficient is used to indicate the confidence level of the occurrence of a target defect in the target defect sub-model when the casting template is being cast; the determination of a defect report according to the respective defect coefficient of each of the above-mentioned defect sub-models includes: a third determining module, used to determine that the target defect sub-model has a defect during the casting process when the first defect coefficient is less than or equal to a defect threshold, wherein the defect report includes the target defect sub-model.

[0009] As an optional solution, the above-mentioned device further includes: a first acquisition module, configured to acquire a set of correction coefficients based on the type of molten metal and the process type used in the simulated casting, wherein the type of molten metal and the process type are matched with the metal process correction coefficients in the set of correction coefficients; a fourth determination module, configured to determine solidification characteristic parameters based on the set of casting state parameters of the target defect sub-model, wherein the solidification characteristic parameters include at least one of solidification rate, temperature gradient value, and cooling rate, and each of the solidification characteristic parameters is matched with other correction coefficients in the set of correction coefficients besides the metal process correction coefficients; a first calculation module, configured to perform exponentiation on each of the solidification characteristic parameters and their respective matched correction coefficients to obtain at least one second defect coefficient; and a second calculation module, configured to determine the first defect coefficient by multiplying the metal process correction coefficients by at least one of the second defect coefficients.

[0010] As an optional solution, the above-mentioned device further includes: a fifth determining module, used to determine the solidification rate based on the solidification rates of the molten metal corresponding to the first direction, the second direction, and the third direction within the target defect sub-model, when the solidification characteristic parameters include the solidification rate, wherein the first direction, the second direction, and the third direction are parallel to the coordinate axes of a three-dimensional rectangular coordinate system established with the center of the target defect sub-model as the origin, and the casting state parameter set includes the solidification rate of the molten metal; and a sixth determining module, used to determine the solidification rate based on the solidification rates of the first direction, the second direction, and the third direction within the target defect sub-model, when the solidification characteristic parameters include the temperature gradient value. The temperature gradient value is determined by the corresponding temperature change rate, wherein the casting state parameter set includes the temperature change rate; the seventh determining module is used to determine the cooling rate based on the first temperature, the second temperature, the first time period, and the second time period when the solidification characteristic parameters include the cooling rate, wherein the first temperature is higher than the second temperature, the first time period is used to indicate the time value when the temperature of the molten metal in the target defect sub-model is higher than the first temperature, the second time period is used to indicate the time value when the temperature of the molten metal in the target defect sub-model is higher than the second temperature, and the casting state parameter set includes the first temperature, the second temperature, the first time period, and the second time period.

[0011] As an optional solution, the above-mentioned apparatus further includes: a traversal module, configured to traverse at least one of the above-mentioned casting models according to time, and perform the following operations on the traversed current casting model: segmenting the current casting model to obtain at least one current casting sub-model; comparing the physical parameters of the first casting sub-model and the second casting sub-model, wherein the at least one current casting sub-model includes the first casting sub-model and the second casting sub-model; if the comparison result satisfies the second defect condition, determining the current casting model as the defective model, wherein the first casting sub-model and the second casting sub-model are adjacent, and the first defect condition includes the second defect condition; if the second physical parameter of the third casting sub-model satisfies the third defect condition, determining the current casting model as the defective model, wherein at least one of the above-mentioned current casting sub-models includes the third casting model, and the first defect condition includes the third defect condition.

[0012] As an optional embodiment, the above-mentioned apparatus further includes: a first comparison module for comparing the first solidification rate of the first casting sub-model and the second solidification rate of the second casting sub-model; an eighth determination module for determining that the comparison result satisfies the second defect condition when the comparison result indicates that the difference between the first solidification rate and the second solidification rate is greater than or equal to a first difference; and a ninth determination module for determining that the comparison result satisfies the second defect condition when the comparison result indicates that the ratio between the first solidification rate and the second solidification rate is greater than or equal to a first ratio.

[0013] As an optional solution, the above-mentioned device further includes: a second comparison module, used to compare the first temperature gradient value of the first casting sub-model and the second temperature gradient value of the second casting sub-model; a tenth determination module, used to determine that the comparison result satisfies the second defect condition when the comparison result indicates that the difference between the first temperature gradient value and the second temperature gradient value is greater than or equal to a second difference; an eleventh determination module, used to determine that the comparison result satisfies the second defect condition when the comparison result indicates that the ratio between the first temperature gradient value and the second temperature gradient value is greater than or equal to a second ratio condition; and a twelfth determination module, used to determine that the third temperature gradient value of the third casting sub-model satisfies the third defect condition when the third temperature gradient value of the third casting sub-model is greater than or equal to a gradient threshold.

[0014] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the defect detection method for casting templates as described above.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described defect detection method for casting templates through the computer program.

[0016] In this embodiment, by acquiring the casting template to be tested and performing simulated casting, at least one casting model at different times is obtained, realizing dynamic simulation of the solidification process before the actual manufacturing of the casting is completed; then, by screening the defect models that meet the first defect condition from the casting models, and identifying the defect sub-models with unsolidified molten metal, the defect coefficient is determined by combining the casting state parameter set of each defect sub-model, and a defect report is generated based on the defect coefficient, thereby identifying the defects that may occur when the casting template is casting in advance in the virtual environment, realizing pre-predictive analysis, and solving the technical problem of high lag in casting defect detection in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a schematic diagram of the application environment of an optional defect detection method for casting template according to an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of the flow of an optional defect detection method for casting templates according to an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of an optional defect detection method for casting templates according to an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of another optional defect detection method for casting templates according to an embodiment of this application;

[0022] Figure 5 This is a schematic diagram of another optional defect detection method for casting templates according to an embodiment of this application;

[0023] Figure 6 This is a schematic diagram of another optional defect detection method for casting templates according to an embodiment of this application;

[0024] Figure 7 This is a schematic diagram of an optional defect detection device for casting template according to an embodiment of this application;

[0025] Figure 8 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to one aspect of the embodiments of this application, a method for detecting defects in casting templates is provided. Optionally, as an optional implementation, the above-described method for detecting defects in casting templates can be applied, but is not limited to, to applications such as... Figure 1 The environment shown may include, but is not limited to, user equipment 102 and server 112. User equipment 102 may include, but is not limited to, a display 104, a processor 106 and a memory 108. Server 112 includes a database 114 and a processing engine 116.

[0029] The specific process can be summarized in the following steps:

[0030] Step S102: User equipment 102 acquires the casting template to be inspected;

[0031] Step S104: Send the casting template to be tested to the server 112 via network 110;

[0032] In steps S106-S114, server 112 simulates casting of the casting template to be inspected through processing engine 116 to obtain at least one casting model; from the at least one casting model, a defect model that satisfies the first defect condition is determined; from the defect model, at least one defect sub-model that contains molten metal in an unsolidified state is determined; based on the casting state parameter set of each defect sub-model, the defect coefficient of each defect sub-model is calculated; and a defect report is determined according to the defect coefficient of each defect sub-model.

[0033] In step S116, a defect report for displaying defects in the casting template is sent to user equipment 102 via network 110. User equipment 102 displays the defect report on display 104 via processor 106 and stores the defect report for displaying defects in the casting template in memory 108.

[0034] remove Figure 1 Beyond the examples shown, the terminal devices described above can be terminal devices configured with a target client, including but not limited to at least one of the following: mobile phones (such as Android phones, iOS phones, etc.), laptops, tablets, PDAs, MIDs (Mobile Internet Devices), PADs, desktop computers, smart TVs, etc. The target client can be a video client, instant messaging client, browser client, educational client, etc. The networks described above can include, but are not limited to, wired networks and wireless networks. The wired networks include local area networks (LANs), metropolitan area networks (MANs), and wide area networks (WANs). The wireless networks include Bluetooth, Wi-Fi, and other networks that enable wireless communication. The server described above can be a single server, a server cluster consisting of multiple servers, or a cloud server. The above is merely an example, and no limitations are imposed in this embodiment.

[0035] Alternatively, as an optional implementation, such as Figure 2 As shown, the defect detection method for casting templates can be performed by electronic equipment, such as... Figure 1 The user equipment or server shown includes the following specific steps:

[0036] S202, simulate casting the casting template to be tested to obtain at least one casting model, wherein different casting models in the at least one casting model are used to indicate the model formed at different times;

[0037] S204, determine a defect model that satisfies the first defect condition from at least one casting model;

[0038] S206, Identify at least one defect sub-model from the defect model that indicates the presence of molten metal in an unsolidified state;

[0039] S208, calculate the defect coefficient of each defect sub-model based on the casting state parameter set of each defect sub-model;

[0040] S210, determine the defect report according to the defect coefficient of each defect sub-model, wherein the casting state parameter set includes the physical parameters of the molten metal in the defect sub-model.

[0041] Optionally, in this embodiment, the casting template may refer to, but is not limited to, a mold structure used to form the shape of the casting during the casting process, the internal cavity of which determines the geometry of the final casting, such as a metal cavity structure used to form the body of a wire clamp in aluminum alloy die casting.

[0042] Optionally, in this embodiment, simulated casting may refer to, but is not limited to, reproducing the behavior of molten metal filling and solidification in a computer environment using a physical model, such as deduce the dynamic evolution of molten metal in a template based on temperature field and flow field data.

[0043] Optionally, in this embodiment, the casting model may refer to, but is not limited to, the staged three-dimensional models generated during the simulated casting process. Each model corresponds to the state of the molten metal at a specific time point, such as the three-dimensional models generated when the solidification degree of the molten metal in the template reaches 5%, 35%, and 70%, respectively.

[0044] Optionally, in this embodiment, the first defect condition may refer to, but is not limited to, a pre-set logical standard that can be used to screen potential defect areas, such as the existence of an isolated liquid phase region during the solidification process of the molten metal, or the temperature gradient of the molten metal being higher than a critical value.

[0045] Optionally, in this embodiment, the defect model may refer to, but is not limited to, a subset of casting models that meet the first defect condition, such as the three-dimensional model corresponding to the local unsolidified area that appears at 70% solidification.

[0046] Optionally, in this embodiment, the molten metal in an unsolidified state may refer to, but is not limited to, the metal region that is still in a liquid or semi-solid state during the simulation.

[0047] Optionally, in this embodiment, the defect sub-model may refer to, but is not limited to, local three-dimensional spatial units extracted from the defect model that contain unsolidified molten metal, such as a set of three-dimensional mesh units that are determined to be isolated liquid phases in the thick-walled region of the casting, or a set of three-dimensional mesh units whose temperature gradient value is greater than the temperature gradient threshold.

[0048] Optionally, in this embodiment, the casting state parameter set may refer to, but is not limited to, a multi-dimensional parameter set describing the physical state of the molten metal within the defect sub-model, and may include, but is not limited to, physical parameters such as solidification rate, temperature gradient, cooling rate, liquid phase fraction, and local pressure.

[0049] Optionally, in this embodiment, the defect coefficient may refer to, but is not limited to, a numerical value that can characterize the degree of defect risk, calculated by mathematical formula based on the set of casting state parameters.

[0050] Optionally, in this embodiment, a predictive model based on component machine learning can be used to train a neural network based on historical casting data. The multidimensional physical parameters of the defect sub-model, such as liquid phase fraction distribution, pressure gradient, heat flux density, solidification time window, etc., are input, and the defect risk score is output as the defect coefficient.

[0051] Optionally, in this embodiment, the casting template to be tested is first simulated to obtain at least one casting model, and a dynamic dataset of the entire solidification process of the casting is obtained based on the casting model, providing a state sequence in the time dimension for defect identification.

[0052] Then, from at least one casting model, the defect model that satisfies the first defect condition is determined, thereby screening out local areas with defect formation potential, and thus eliminating risk-free areas to reduce computational redundancy.

[0053] Next, at least one defect sub-model is identified from the defect model that contains molten metal in an unsolidified state, thereby focusing on high-risk areas that have not yet completed solidification and avoiding invalid analysis of areas that have already solidified.

[0054] Then, based on the casting state parameter set of each defect sub-model, the defect coefficient of each defect sub-model is calculated, thereby quantifying the physical state into a measurable risk indicator and realizing the numerical expression of the degree of defect.

[0055] Finally, based on the defect coefficient of each defect sub-model, a defect report is determined, thereby generating a systematic defect distribution and severity assessment result, providing data support for process optimization.

[0056] It should be noted that by simulating the casting process to generate multi-time-series casting models and screening unsolidified defect sub-models based on the first defect condition, and combining the set of physical parameters to calculate the defect coefficient to output a systematic defect report, non-destructive proactive prediction of shrinkage porosity defects inside castings is achieved, which significantly improves the accuracy of defect identification and reduces the detection cost.

[0057] In addition, in this embodiment, other defects besides shrinkage and porosity can be determined by the set of casting state parameters. For example, when the local cooling rate increases abnormally and the temperature gradient changes abruptly, the hot cracking tendency area can be identified; when the liquid phase fraction distribution is uneven and the flow front stagnates, the risk of porosity or air entrapment can be inferred; when the pressure gradient is lower than the critical value required for molten metal feeding, the risk of shrinkage or surface depression can be determined.

[0058] The embodiments provided in this application obtain at least one casting model at different times by acquiring the casting template to be tested and performing simulated casting, thereby realizing dynamic simulation of the solidification process before the actual manufacturing of the casting is completed. Then, by screening the defect models that meet the first defect condition from the casting models, and identifying the defect sub-models with unsolidified molten metal, the defect coefficients are determined by combining the casting state parameter set of each defect sub-model, and a defect report is generated based on the defect coefficients. Thus, the defects that may occur when the casting template is casting can be identified in advance in the virtual environment, realizing pre-predictive analysis and solving the technical problem of high lag in casting defect detection in related technologies.

[0059] As an optional approach, based on the casting state parameter set of each defect sub-model, the defect coefficient of each defect sub-model is calculated, including:

[0060] S1-1, take each defect sub-model in at least one defect sub-model as the target defect sub-model in turn;

[0061] S1-2, Based on the casting state parameter set of the target defect sub-model, determine the first defect coefficient of the target defect sub-model, wherein the first defect coefficient is used to indicate the confidence level of the occurrence of the target defect in the target defect sub-model when casting is carried out in the casting template;

[0062] Based on the defect coefficient of each defect sub-model, a defect report is determined, including:

[0063] As an alternative approach, if the first defect coefficient is less than or equal to the defect threshold, it is determined that the target defect sub-model has a defect during the casting process, wherein the defect report includes the target defect sub-model.

[0064] Optionally, in this embodiment, the first defect coefficient may, but is not limited to, refer to a single numerical index calculated based on the casting state parameter set of the target defect sub-model, used to quantify the probability of the sub-model experiencing a target defect during the casting process.

[0065] Optionally, in this embodiment, the defect threshold may refer to, but is not limited to, a pre-set critical value used to determine the existence of a defect. For example, the defect threshold may be set to 10, and a defect is determined to exist when the first defect coefficient is less than or equal to this value.

[0066] Optionally, in this embodiment, the target defect may be, but is not limited to, micropores formed by insufficient feeding in the later stage of solidification of molten metal, such as shrinkage cavities.

[0067] Optionally, in this embodiment, each defect sub-model in at least one defect sub-model is first used as the target defect sub-model in turn, thereby ensuring that each potential defect area is independently included in the evaluation process and avoiding risk masking caused by area merging.

[0068] Then, based on the casting state parameter set of the target defect sub-model, the first defect coefficient of the target defect sub-model is determined, thereby transforming the multidimensional physical state into a single risk score and realizing a precise numerical expression of the defect probability.

[0069] Optionally, in this embodiment, if the first defect coefficient is less than or equal to the defect threshold, it is determined that the target defect sub-model has a defect during the casting process, thereby establishing an objective and repeatable defect judgment logic.

[0070] The embodiments provided in this application analyze defect sub-models one by one and calculate the first defect coefficient based on the set of casting state parameters. Combined with a preset defect threshold, the existence of defects is automatically determined, thereby realizing standardized identification and report generation of internal defects in castings, improving the automation level of defect detection, reducing manual intervention, and thus improving the efficiency of defect detection.

[0071] As an optional approach, the first defect coefficient of the target defect sub-model is determined based on the set of casting state parameters of the target defect sub-model, including:

[0072] S2-1, Based on the type of molten metal and the process type used in the simulated casting, obtain a set of correction coefficients, wherein the type of molten metal and the process type are matched with the metal process correction coefficients in the set of correction coefficients;

[0073] S2-2, Based on the casting state parameter set of the target defect sub-model, the solidification characteristic parameters are determined. The solidification characteristic parameters include at least one of solidification rate, temperature gradient value and cooling rate. Each solidification characteristic parameter is matched with other correction coefficients in the correction coefficient set, except for the metal process correction coefficient.

[0074] S2-3, perform exponentiation on each solidification characteristic parameter and its corresponding matching correction coefficient to obtain at least one second defect coefficient;

[0075] S2-4, the product of the metal processing correction factor and at least one second defect factor is determined as the first defect factor.

[0076] Optionally, in this embodiment, the set of correction coefficients may refer to, but is not limited to, a set of adjustable parameters determined by the type of molten metal and the type of process, including metal process correction coefficients and other correction coefficients corresponding to solidification characteristic parameters, such as ZL102 aluminum alloy, ZL104 aluminum alloy, ZL101A aluminum alloy and their respective coefficient sets under different casting processes.

[0077] Optionally, in this embodiment, in addition to the metal process correction coefficient, there are other correction parameters corresponding to the solidification characteristic parameters. The values ​​of these other correction parameters are also determined by the type of molten metal and its casting process.

[0078] To further illustrate, the metal process correction factor for ZL102 aluminum alloy under high pressure casting is 1.2, and the solidification characteristic parameters include solidification rate, temperature gradient value and cooling rate. In this case, the first correction factor corresponding to the solidification rate can be 1.0, the second correction factor corresponding to the temperature gradient value can be 1.5, and the third correction factor corresponding to the cooling rate can be -0.4.

[0079] Furthermore, the metal process correction factor for ZL104 aluminum alloy under centrifugal main process is 1.4, and the solidification characteristic parameters include solidification rate, temperature gradient value and cooling rate. At this time, the first correction factor corresponding to the solidification rate can be 1.2, the second correction factor corresponding to the temperature gradient value can be 1.1, and the third correction factor corresponding to the cooling rate can be -0.5.

[0080] It should be noted that the set of correction coefficients corresponding to different types of molten metal and the casting processes used can be obtained through experimental testing.

[0081] Optionally, in this embodiment, the metal process correction coefficient may refer to, but is not limited to, a comprehensive correction factor in the set of correction coefficients that is directly related to the type of molten metal and the process type. For example, in aluminum alloy high-pressure casting, it may be 1.2.

[0082] Optionally, in this embodiment, the solidification characteristic parameters may refer to physical quantities that can directly reflect the dynamics of the solidification process, extracted from the casting state parameter set of the target defect sub-model. These parameters may include at least one of solidification rate, temperature gradient value, and cooling rate. For example, the solidification characteristic parameters of a sub-model may include temperature gradient value, solidification rate, etc., such as a temperature gradient value of 120 K / mm and a solidification rate of 0.9 K / s.

[0083] Optionally, in this embodiment, the second defect coefficient may, but is not limited to, refer to the intermediate result obtained by exponentiation of each solidification characteristic parameter and its matching correction coefficient. For example, exponentiation of the temperature gradient value 120 with the correction coefficient 1.5 yields 120¹·5 .

[0084] Optionally, in this embodiment, a set of correction coefficients is first obtained based on the type of molten metal and the process type used in the simulated casting, thereby providing a parameter benchmark that is completely matched with the current casting conditions for subsequent calculations and ensuring model adaptability.

[0085] Then, based on the casting state parameter set of the target defect sub-model, the solidification characteristic parameters are determined, thereby extracting the physical variables that affect the formation of defects.

[0086] Next, each solidification characteristic parameter and its corresponding matching correction coefficient are exponentially operated to obtain at least one second defect coefficient, thereby transforming the nonlinear influence of the physical parameters into multiplicative mathematical components.

[0087] Finally, the product of the metal processing correction coefficient and at least one second defect coefficient is determined as the first defect coefficient, thereby realizing the comprehensive coupling calculation of material processing characteristics and local solidification state, and forming a complete defect risk score.

[0088] The embodiments provided in this application obtain a set of correction coefficients based on the type of molten metal and the process type, and then multiply the correction coefficients by exponentiation of the solidification characteristic parameters and the corresponding correction coefficients, thereby achieving accurate calculation of the defect coefficients and improving the accuracy and generalization ability of defect prediction under different material and process conditions.

[0089] As an optional approach, the solidification characteristic parameters of the target defect sub-model are determined based on the set of casting state parameters of the target defect sub-model, including at least one of the following:

[0090] S3-1, when the solidification characteristic parameters include the solidification rate, the solidification rate is determined according to the solidification rate of the molten metal in the first direction, the second direction and the third direction in the target defect sub-model. The first direction, the second direction and the third direction are parallel to the coordinate axis directions of the three-dimensional rectangular coordinate system established with the center of the target defect sub-model as the origin. The casting state parameter set includes the solidification rate of the molten metal.

[0091] S3-2, when the solidification characteristic parameters include temperature gradient values, the temperature gradient values ​​are determined according to the temperature change rates corresponding to the first direction, the second direction and the third direction in the target defect sub-model. The casting state parameter set includes the temperature change rate.

[0092] S3-3, when the solidification characteristic parameters include the cooling rate, the cooling rate is determined based on the first temperature, the second temperature, the first time period, and the second time period. The first temperature is higher than the second temperature. The first time period is used to indicate the time value when the temperature of the molten metal in the target defect sub-model is higher than the first temperature. The second time period is used to indicate the time value when the temperature of the molten metal in the target defect sub-model is higher than the second temperature. The casting state parameter set includes the first temperature, the second temperature, the first time period, and the second time period.

[0093] Optionally, in this embodiment, the first direction, the second direction, and the third direction may refer to three mutually perpendicular axes in a three-dimensional rectangular coordinate system established with the center of the target defect sub-model as the origin, corresponding to the x, y, and z directions in space, for example, extending along the length, width, and thickness directions respectively in the thick-walled region of the casting.

[0094] Optionally, in this embodiment, the solidification rate of the molten metal may refer to, but is not limited to, the proportion of molten metal changing from liquid to solid state within the target defect sub-model per unit time, calculated independently along each direction. For example, the solidification rate in the x-direction is 0.75s. - ¹, 0.68s in the y direction - ¹, the z-direction is 0.52s - ¹, thus reflecting the difference in the solidification rate of molten metal in different spatial dimensions, and used to accurately calculate the overall solidification rate.

[0095] Optionally, in this embodiment, the temperature change rate may refer to, but is not limited to, the temperature difference per unit distance along each spatial direction within the target defect sub-model. For example, the temperature drops by 120K per millimeter in the x-direction, 95K / mm in the y-direction, and 110K / mm in the z-direction, thereby characterizing the direction and intensity of heat flow and serving as a direct input for calculating the temperature gradient value.

[0096] Optionally, in this embodiment, the first temperature may, but is not limited to, refer to a reference high temperature value higher than the liquidus-solid phase line temperature of the metal, for example, set to a temperature value 2°C higher than the liquidus line, to define the starting point of the cooling process, thereby defining the upper boundary of the cooling rate calculation and ensuring that the time interval corresponds to the phase transition stage.

[0097] Optionally, in this embodiment, the second temperature may, but is not limited to, refer to a reference low temperature value that is lower than the first temperature and higher than the solidus line. The second temperature may also be set as the liquid-solid phase line temperature of the metal to define the termination point of the cooling process, thereby defining the lower boundary of the cooling rate calculation and ensuring that the calculation range is in the liquid-solid coexistence stage.

[0098] Optionally, in this embodiment, the first time period may refer to, but is not limited to, the duration for which the temperature of the molten metal in the target defect sub-model remains above the first temperature, for example, 0.2 seconds, thereby eliminating interference from the overheating stage and ensuring that the cooling rate only reflects the cooling behavior of the phase transition region.

[0099] Optionally, in this embodiment, the second time period may, but is not limited to, refer to the duration during which the molten metal temperature in the target defect sub-model is at a second temperature, for example, 1.5 seconds, thereby providing an effective cooling window for calculating the average cooling rate within that temperature range.

[0100] Optionally, in this embodiment, when the solidification characteristic parameters include the solidification rate, the solidification rate is determined according to the solidification rates of the molten metal in the first direction, the second direction, and the third direction within the target defect sub-model, thereby achieving a three-dimensional spatial average or vector synthesis of the solidification rate and improving the accuracy of capturing anisotropic solidification behavior.

[0101] Optionally, in this embodiment, when the solidification characteristic parameters include temperature gradient values, the temperature gradient values ​​are determined according to the temperature change rates corresponding to the first direction, the second direction, and the third direction within the target defect sub-model, thereby constructing a three-dimensional temperature gradient vector that accurately reflects the spatial distribution of heat flow direction and intensity.

[0102] Optionally, in this embodiment, when the solidification characteristic parameters include the cooling rate, the cooling rate is determined based on the first temperature, the second temperature, the first time period, and the second time period, thereby calculating the true and effective average cooling rate based on the time distribution of the phase change temperature zone, avoiding the interference of temperature fluctuations in the non-phase change stage on the results.

[0103] Through the embodiments provided in this application, the solidification rate, temperature gradient value and cooling rate are independently calculated based on the solidification rate of molten metal, temperature change rate and phase transition temperature zone time parameters in three-dimensional space, thereby realizing the extraction of solidification characteristic parameters in the spatial and temporal dimensions and improving the accuracy of defect coefficient input parameters.

[0104] As an alternative approach, the defect model is determined from at least one casting model, including:

[0105] Iterate through at least one casting model according to time, and perform the following operations on the current casting model encountered:

[0106] S4-1, Divide the current casting model into at least one current casting sub-model;

[0107] S4-2, compare the physical parameters of the first casting sub-model and the second casting sub-model respectively, wherein at least one current casting sub-model includes the first casting model and the second casting model;

[0108] S4-3, if the comparison result meets the second defect condition, determine the current casting model as a defect model, wherein the first casting sub-model and the second casting sub-model are adjacent to each other. The first defect condition includes the second defect condition.

[0109] S4-4, if the second physical parameters of the third casting sub-model satisfy the third defect condition, the current casting model is determined to be a defect model, wherein at least one current casting sub-model includes the third casting sub-model, and the first defect condition includes the third defect condition.

[0110] Optionally, in this embodiment, at least one casting model is first traversed according to time, and a segmentation operation is performed on the current casting model to obtain at least one current casting sub-model, thereby decomposing the overall model into local units that can be analyzed independently, and realizing spatial refinement of defect identification.

[0111] Then, by comparing the physical parameters of the first and second casting sub-models, it is possible to detect whether there are abrupt changes in physical state between adjacent areas, thereby identifying the cause of defects caused by differences in wall thickness or interruption of cooling path.

[0112] Optionally, in this embodiment, if the comparison result meets the second defect condition, the current casting model is determined to be a defect model, and its existence is determined by spatial relationship logic.

[0113] Optionally, in this embodiment, if the second physical parameters of the third casting sub-model satisfy the third defect condition, the current casting model is determined to be a defect model, thereby achieving independent defect determination without neighborhood dependence.

[0114] The embodiments provided in this application identify defects by traversing the casting model through time series and using a dual mechanism of comparing parameters of adjacent sub-models and judging independent parameters of single sub-models. This achieves coverage of casting defects with different spatial distributions and physical mechanisms, improving the completeness and accuracy of defect model screening.

[0115] As an optional approach, the physical parameters of the first and second casting sub-models are compared, including:

[0116] S5-1, compare the first solidification rate of the first casting sub-model and the second solidification rate of the second casting sub-model.

[0117] S5-2, if the comparison result indicates that the difference between the first solidification rate and the second solidification rate is greater than or equal to the first difference, it is determined that the comparison result satisfies the second defect condition.

[0118] S5-3, if the comparison result indicates that the ratio between the first solidification rate and the second solidification rate is greater than or equal to the first ratio, the comparison result is determined to satisfy the second defect condition.

[0119] Optionally, in this embodiment, the first solidification rate may refer to, but is not limited to, the rate at which the molten metal in the first casting sub-mold transforms from a liquid to a solid state, expressed in units of solidification rate per second, such as 0.85 s. - ¹, reflects the rate of solidification in the local area, thus serving as a quantitative basis for the difference in solidification behavior between adjacent sub-models, and is used to identify solidification imbalances caused by abrupt changes in wall thickness or heat flow blockage.

[0120] Optionally, in this embodiment, the second solidification rate may refer to, but is not limited to, the solidification rate of the molten metal within the second casting mold, for example, 0.25 s. - ¹, compared with the first solidification rate, is used to determine whether there is a local stagnant solidification zone, thus providing a comparison benchmark and revealing whether the feeding channel is cut off due to rapid solidification of the neighboring area.

[0121] Optionally, in this embodiment, the first difference may refer to, but is not limited to, a preset solidification rate difference threshold, such as 0.6s. - ¹, used to determine whether the difference in solidification rate between two sub-models reaches the defect risk level.

[0122] Optionally, in this embodiment, the first ratio may, but is not limited to, a pre-set solidification rate ratio threshold, such as 3.0, used to determine whether the relative relationship of solidification rates between the two sub-models exceeds the safe range.

[0123] Optionally, in this embodiment, the first solidification rate of the first casting sub-model and the second solidification rate of the second casting sub-model are compared first to obtain the quantitative difference in solidification behavior between adjacent regions, providing basic data for subsequent condition judgment.

[0124] If the comparison result indicates that the difference between the first solidification rate and the second solidification rate is greater than or equal to the first difference, the comparison result is determined to meet the second defect condition, thereby identifying the risk of feeding interruption that may be caused by local solidification abrupt change through the absolute difference threshold.

[0125] If the comparison results indicate that the ratio between the first solidification rate and the second solidification rate is greater than or equal to the first ratio, the comparison results are determined to meet the second defect condition, thereby identifying solidification imbalance problems that may be caused by differences in structural thickness through relative proportional relationships.

[0126] The embodiments provided in this application compare adjacent sub-models by simultaneously using both the absolute difference and the relative ratio of solidification rates, thereby achieving dual coverage of different types of solidification imbalance mechanisms and improving the sensitivity and robustness of defect identification to structural abrupt changes and abnormal heat flow distribution.

[0127] As an optional approach, the physical parameters of the first and second casting sub-models are compared, including:

[0128] S6-1, compare the first temperature gradient value of the first casting sub-model with the second temperature gradient value of the second casting sub-model;

[0129] S6-2, if the comparison result indicates that the difference between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second difference, determine that the comparison result satisfies the second defect condition.

[0130] S6-3, if the comparison result indicates that the ratio between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second ratio condition, the comparison result is determined to satisfy the second defect condition.

[0131] Before determining that the current casting model is a defective model, the following is included:

[0132] If the third temperature gradient value of the third casting sub-model is greater than or equal to the gradient threshold, it is determined that the third temperature gradient value of the third casting sub-model satisfies the third defect condition.

[0133] Optionally, in this embodiment, the first temperature gradient value may, but is not limited to, refer to the rate of temperature change per unit distance along the spatial direction within the first casting sub-model, such as 120K / mm, reflecting the heat flow intensity and direction in that region, thereby quantifying the local solidification driving force and used to identify abnormal solidification trends caused by heat concentration or uneven heat dissipation.

[0134] Optionally, in this embodiment, the second temperature gradient value may, but is not limited to, refer to the temperature gradient value at the corresponding position within the second casting sub-model, such as 35K / mm, as a comparison benchmark for the first temperature gradient value, thereby revealing abrupt changes in the heat flow distribution in adjacent areas and assisting in determining whether there is a risk of interruption of the feeding channel.

[0135] Optionally, in this embodiment, the second difference may refer to, but is not limited to, a set absolute temperature gradient difference threshold, such as 80K / mm, to determine whether the difference in heat flow intensity between the two sub-models reaches the defect triggering level, thereby establishing a rigid criterion based on abrupt difference and eliminating interference from the gradual thermal field.

[0136] Optionally, in this embodiment, the second ratio may, but is not limited to, a set temperature gradient relative ratio threshold, such as 2.5, to determine whether the multiple relationship of heat flow intensity between the two sub-models exceeds the safe range, thereby identifying the imbalance of heat flow dominance caused by the large difference in structural thickness and enhancing the adaptability to asymmetric castings.

[0137] Optionally, in this embodiment, the third temperature gradient value may, but is not limited to, refer to the temperature gradient value inside the third casting sub-model, such as 150K / mm. It is independent of neighborhood comparison and is determined solely based on its own value, thereby capturing local extreme thermal gradient regions, such as thick-walled cores or isolated liquid phase regions, and directly associating them with the risk of shrinkage porosity.

[0138] Optionally, in this embodiment, the gradient threshold may refer to, but is not limited to, the upper limit of the temperature gradient determination of the single model, such as 140K / mm, for triggering independent defect determination.

[0139] Optionally, in this embodiment, the first temperature gradient value of the first casting sub-model and the second temperature gradient value of the second casting sub-model are compared first to obtain the quantitative difference in heat flow intensity between adjacent regions, providing core input data for subsequent condition judgment.

[0140] Then, if the comparison result indicates that the difference between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second difference, it is determined that the comparison result meets the second defect condition, thereby identifying heat flow interruption type defects caused by structural abrupt changes through the absolute difference threshold.

[0141] Then, if the comparison result indicates that the ratio between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second ratio, it is determined that the comparison result meets the second defect condition, thereby identifying the heat flow dominance imbalance type defect caused by wall thickness difference through the relative ratio threshold.

[0142] Optionally, in this embodiment, if the third temperature gradient value of the third casting sub-model is greater than or equal to the gradient threshold, the current casting model is determined to be a defect model, thereby realizing independent defect identification in the local extreme thermal gradient region and making up for the isolated high-risk area that cannot be covered by the neighborhood comparison.

[0143] The embodiments provided in this application combine a dual comparison mechanism of the difference and ratio of temperature gradients of adjacent sub-models, as well as an independent threshold determination of the temperature gradient of a single sub-model, thereby realizing multi-dimensional and multi-level defect identification of abnormal heat flow distribution, improving the completeness of defect model screening and adaptability to complex casting structures.

[0144] To better understand the above-mentioned defect detection method for casting templates, the method will now be further explained in conjunction with another embodiment.

[0145] As an alternative approach, this example provides a method for predicting shrinkage porosity and void defects in aluminum alloy castings used as the body of wire clamp products.

[0146] Optionally, in this embodiment, as follows: Figure 3 As shown, it includes the following steps:

[0147] S302, Determine the established process;

[0148] S304, perform finite element modeling;

[0149] S306, Perform post-processing result analysis;

[0150] S308, solidification results analysis;

[0151] S310, perform temperature gradient result analysis;

[0152] S312, analyzed using the modified Niyama criterion;

[0153] S314 determines the location of defects by the coexistence of three features.

[0154] S7-1, The die-casting process is determined based on actual production. In this embodiment, the material type of the molten metal is ZL102 aluminum alloy;

[0155] The chemical composition of the material is shown in Table 1:

[0156]

[0157] Table 1

[0158] The die-casting process parameters are shown in Table 2:

[0159]

[0160] Table 2

[0161] S7-2, Finite Element Modeling.

[0162] 3D Model: Import the product's 3D model into the software, such as... Figure 4 As shown in (a), this is a schematic diagram of the 3D model; Mesh generation: The mesh generation module divides the mesh;

[0163] Interface heat transfer coefficient: The interface heat transfer coefficient between ZL102 molten metal and mold is set to 1500 W / m2·K, and the interface heat transfer coefficient between molds is set to 800 W / m2·K.

[0164] S7-3, Multi-feature Joint Judgment Method:

[0165] In die casting production, when the final solidified area of ​​the casting lacks sufficient molten metal for feeding, shrinkage cavities and porosity defects occur, typically appearing in thicker wall areas. In this embodiment, the thicker wall area is as follows... Figure 4 As shown in (b), this is the front view of the wall thickness region, as... Figure 4 As shown in (c), this is the side view of the wall thickness region, as... Figure 4 As shown in (d), this is the back side of the wall thickness region.

[0166] (1) Analysis of solidification results:

[0167] The solidification result is as follows Figure 5 As shown, Figure 5 (a) in the diagram is a schematic of the result of 5% solidification. Figure 5 (b) in the diagram is a schematic of the result after 35% solidification. Figure 5 (c) in the diagram illustrates the result when 70% solidification has occurred. In the initial stage of solidification, the casting solidifies sequentially from the farthest point to the nearest. When 70% solidification is achieved, isolated liquid phase regions form because the solidification rate of the thicker wall areas is slower than the surrounding areas. Although these thicker wall areas continue to solidify and shrink, the surrounding molten metal has already solidified and cannot compensate for the shrinkage in these areas. Therefore, shrinkage porosity defects are prone to occur in these locations.

[0168] (2) Analysis of temperature gradient results:

[0169] Temperature gradient results are as follows Figure 6 As shown. In this example, the wall thickness region was cut three times for temperature gradient analysis. Figure 6 (a) Figure 6 (b) and Figure 6 (c) in the diagram represents a schematic representation of the temperature gradient plots for different slices. Figure 6 In the slices, the temperature gradient on the outer wall is always greater than that on the interior, so the outer wall solidifies first. Since no slag pocket is added to the thicker areas of this product, when the molten metal on the outer wall solidifies first, the thicker areas cannot be adequately fed back, resulting in shrinkage porosity defects.

[0170] (3) The revised Niyama criterion:

[0171] The Niyama criterion is used in the foundry industry to predict shrinkage cavities and porosity defects. However, the standard Niyama criterion is not well applied to aluminum alloy castings. Therefore, this example modifies the Niyama criterion based on the RGL function, and its formula is expressed as follows:

[0172] (1);

[0173] (2);

[0174] (3);

[0175] (4);

[0176] in, This represents the threshold for the occurrence of shrinkage porosity and cavitation defects; a, b, c, and d are constants whose values ​​are adjusted according to different materials and processes; R is the solidification rate; G is the temperature gradient; L is the cooling rate; and T is the temperature of the molten metal. It is a temperature 2°C higher than the liquidus temperature of molten metal; It is the temperature of the liquidus-solid phase line of a metal; Is in Time; Is in The time.

[0177] It should be noted that, It means that, in In terms of direction, how much does the temperature change per unit distance moved? It means that, in In terms of direction, how much does the temperature change per unit distance moved? It means that, in In terms of direction, how much does the temperature change per unit distance traveled?

[0178] also, It means that, in In the x-direction, how much does the temperature change in the x-direction per unit time? It means that, in In the y-direction, how much does the temperature change in the y-direction per unit time? It means that, in In the z-direction, how much does the temperature change in the z-direction per unit time?

[0179] It should be noted that, for this embodiment, The corresponding thresholds are set to 10, a to 1.2, b to 1.0, c to 1.5, and d to -0.5. When A value less than 10 indicates a risk of shrinkage porosity or cavities in the area. In this example, the thick-walled area was cut six times for result analysis.

[0180] The embodiments provided in this application achieve the following technical effects: Improved defect prediction accuracy: By using a multi-feature joint judgment method, false positives and false negatives are avoided, and the defect location is accurately located; Significantly reduced cost: No need to damage the product or purchase expensive testing equipment; Wide applicability: Adaptable to various materials (such as aluminum alloys, magnesium alloys, etc.), geometries, and process parameters, meeting the manufacturing needs of various castings; Good development prospects: Accurate prediction of shrinkage porosity and shrinkage defects provides a solid foundation for subsequent process optimization and has broad development prospects; Strong technical versatility: The multi-feature joint judgment method is versatile and applicable to other casting processes (such as gravity casting, low-pressure casting, investment casting, etc.).

[0181] It is understood that in the specific embodiments of this application, data such as user information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0182] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0183] According to another aspect of the embodiments of this application, a defect detection device for casting templates for implementing the above-described defect detection method for casting templates is also provided. For example... Figure 7 As shown, the device includes:

[0184] Simulation unit 702 is used to simulate casting of the casting template to be tested to obtain at least one casting model, wherein different casting models in the at least one casting model are used to indicate the model formed at different times.

[0185] The first determining unit 704 is used to determine a defect model that satisfies the first defect condition from at least one casting model;

[0186] The second determining unit 706 is used to determine from the defect model at least one defect sub-model that contains molten metal in an unsolidified state.

[0187] The calculation unit 708 is used to calculate the defect coefficient of each defect sub-model based on the casting state parameter set of each defect sub-model.

[0188] The third determining unit 710 is used to determine the defect report according to the defect coefficient of each defect sub-model, wherein the casting state parameter set includes the physical parameters of the molten metal in the defect sub-model.

[0189] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0190] As an optional solution, the apparatus further includes: a first determining module, used to sequentially use each of the at least one defect sub-models as a target defect sub-model; a second determining module, used to determine a first defect coefficient of the target defect sub-model based on the casting state parameter set of the target defect sub-model, wherein the first defect coefficient is used to indicate the confidence level of the occurrence of a target defect in the target defect sub-model during casting in the casting template; and to determine a defect report according to the respective defect coefficient of each defect sub-model, including: a third determining module, used to determine that the target defect sub-model has a defect during the casting process if the first defect coefficient is less than or equal to a defect threshold, wherein the defect report includes the target defect sub-model.

[0191] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0192] As an optional solution, the device further includes: a first acquisition module, used to acquire a set of correction coefficients based on the type of molten metal and the process type used in the simulated casting, wherein the type of molten metal and the process type are matched with the metal process correction coefficients in the set of correction coefficients; a fourth determination module, used to determine solidification characteristic parameters based on the set of casting state parameters of the target defect sub-model, wherein the solidification characteristic parameters include at least one of solidification rate, temperature gradient value, and cooling rate, and each solidification characteristic parameter is matched with other correction coefficients in the set of correction coefficients besides the metal process correction coefficients; a first calculation module, used to perform exponentiation on each solidification characteristic parameter and its respective matched correction coefficient to obtain at least one second defect coefficient; and a second calculation module, used to determine the first defect coefficient by multiplying the metal process correction coefficient by the at least one second defect coefficient.

[0193] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0194] As an optional solution, the device further includes: a fifth determining module, used to determine the solidification rate based on the solidification rates of the molten metal in the first direction, second direction, and third direction within the target defect sub-model, when the solidification feature parameters include solidification rate, wherein the first direction, second direction, and third direction are parallel to the coordinate axes of a three-dimensional rectangular coordinate system established with the center of the target defect sub-model as the origin, and the casting state parameter set includes the solidification rate of the molten metal; a sixth determining module, used to determine the temperature gradient value based on the temperature change rate in the first direction, second direction, and third direction within the target defect sub-model, when the solidification feature parameters include temperature gradient value, wherein the casting state parameter set includes the temperature change rate; and a seventh determining module, used to determine the cooling rate based on a first temperature, a second temperature, a first time period, and a second time period, when the solidification feature parameters include cooling rate, wherein the first temperature is higher than the second temperature, the first time period indicates the time value during which the temperature of the molten metal in the target defect sub-model is higher than the first temperature, and the second time period indicates the time value during which the temperature of the molten metal in the target defect sub-model is higher than the second temperature, and the casting state parameter set includes the first temperature, the second temperature, the first time period, and the second time period.

[0195] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0196] As an optional solution, the apparatus further includes: a traversal module, configured to traverse at least one casting model according to time, and perform the following operations on the traversed current casting model: segmenting the current casting model to obtain at least one current casting sub-model; comparing the physical parameters of the first casting sub-model and the second casting sub-model, wherein the at least one current casting sub-model includes the first casting sub-model and the second casting sub-model; determining the current casting model as a defective model if the comparison result satisfies a second defect condition, wherein the first casting sub-model and the second casting sub-model are adjacent and the first defect condition includes the second defect condition; determining the current casting model as a defective model if the second physical parameters of the third casting sub-model satisfy a third defect condition, wherein the at least one current casting sub-model includes the third casting model and the first defect condition includes the third defect condition.

[0197] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0198] As an optional solution, the apparatus further includes: a first comparison module for comparing a first solidification rate of a first casting sub-model and a second solidification rate of a second casting sub-model; an eighth determination module for determining that the comparison result satisfies a second defect condition when the comparison result indicates that the difference between the first solidification rate and the second solidification rate is greater than or equal to a first difference; and a ninth determination module for determining that the comparison result satisfies a second defect condition when the comparison result indicates that the ratio between the first solidification rate and the second solidification rate is greater than or equal to a first ratio.

[0199] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0200] As an optional solution, the apparatus further includes: a second comparison module for comparing the first temperature gradient value of the first casting sub-model and the second temperature gradient value of the second casting sub-model; a tenth determination module for determining that the comparison result satisfies the second defect condition when the comparison result indicates that the difference between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second difference; an eleventh determination module for determining that the comparison result satisfies the second defect condition when the comparison result indicates that the ratio between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second ratio condition; and a twelfth determination module for determining that the third temperature gradient value of the third casting sub-model satisfies the third defect condition when the third temperature gradient value of the third casting sub-model is greater than or equal to the gradient threshold.

[0201] For specific implementation examples, please refer to the example shown in the above-described defect detection method for casting templates, which will not be repeated here.

[0202] According to another aspect of the embodiments of this application, an electronic device for implementing the above-described defect detection method for casting templates is also provided. This electronic device can, but is not limited to, […]. Figure 1 The user equipment 102 or server 112 shown in the figure, in this embodiment, is taken as an example of an electronic device, namely user equipment 102. Further, as shown in the figure... Figure 8 As shown, the electronic device includes a memory 802 and a processor 804. The memory 802 stores a computer program, and the processor 804 is configured to execute the steps in any of the above method embodiments via the computer program.

[0203] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.

[0204] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0205] S1, simulate casting of the casting template to be tested to obtain at least one casting model, wherein different casting models in the at least one casting model are used to indicate the model formed at different times;

[0206] S2, determine the defect model that satisfies the first defect condition from at least one casting model;

[0207] S3, Identify at least one defect sub-model from the defect model that indicates the presence of molten metal in an unsolidified state;

[0208] S4. Calculate the defect coefficient of each defect sub-model based on the set of casting state parameters for each defect sub-model.

[0209] S5. Determine the defect report according to the defect coefficient of each defect sub-model. The casting state parameter set includes the physical parameters of the molten metal in the defect sub-model.

[0210] Alternatively, as those skilled in the art will understand, Figure 8 The structure shown is for illustrative purposes only. Figure 8 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 8 The more or fewer components shown (such as network interfaces, etc.), or having the same Figure 8 The different configurations shown.

[0211] The memory 802 can be used to store software programs and modules, such as the program instructions / modules of the casting template defect detection method and apparatus in this embodiment. The processor 804 executes various functional applications and data processing by running the software programs and modules stored in the memory 802, thereby realizing the aforementioned casting template defect detection method. The memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 802 may further include memory remotely located relative to the processor 804, and these remote memories can be connected to electronic devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, the memory 802 may be used, but is not limited to, to store information such as casting templates, at least one casting model, and defect sub-models. As an example, such as... Figure 8 As shown, the memory 802 may include, but is not limited to, the simulation unit 702, the first determining unit 704, the second determining unit 706, the calculation unit 708, and the third determining unit 710 in the defect detection device for the casting template. Furthermore, it may include, but is not limited to, other module units in the defect detection device for the casting template, which will not be described in detail in this example.

[0212] Optionally, the transmission device 806 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 806 includes a Network Interface Controller (NIC), which can be connected to other network devices and a router via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 806 is a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0213] In addition, the aforementioned electronic device also includes: a display 808 for displaying information such as the casting template, at least one casting model, and defect sub-model; and a connection bus 810 for connecting the various module components in the aforementioned electronic device.

[0214] In other embodiments, the aforementioned user equipment or server can be a node in a distributed system, wherein the distributed system can be a blockchain system, which is a distributed system formed by connecting multiple nodes through network communication. The nodes can form a peer-to-peer network, and any form of computing device, such as a server, user equipment, or other electronic device, can become a node in the blockchain system by joining this peer-to-peer network.

[0215] According to one aspect of this application, a computer program product is provided, comprising a computer program / instructions containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions provided in embodiments of this application.

[0216] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0217] It should be noted that the computer system of the electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0218] A computer system includes a Central Processing Unit (CPU), which performs various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) or loaded from RAM. ROM also stores various programs and data required for system operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output interfaces (I / O interfaces) are also connected to the bus.

[0219] The following components are connected to the input / output interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard drives; and communication sections including network interface cards such as LAN cards and modems. The communication section performs communication processing via a network such as the Internet. Drives are also connected to the input / output interface as needed. Removable media, such as disks, optical discs, magneto-optical discs, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0220] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions defined in the system of this application.

[0221] According to one aspect of this application, a computer-readable storage medium is provided, wherein a processor of a computer device reads computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0222] Optionally, in this embodiment, the computer-readable storage medium described above may be configured to store a computer program for performing the following steps:

[0223] S1, simulate casting of the casting template to be tested to obtain at least one casting model, wherein different casting models in the at least one casting model are used to indicate the model formed at different times;

[0224] S2, determine the defect model that satisfies the first defect condition from at least one casting model;

[0225] S3, Identify at least one defect sub-model from the defect model that indicates the presence of molten metal in an unsolidified state;

[0226] S4. Calculate the defect coefficient of each defect sub-model based on the set of casting state parameters for each defect sub-model.

[0227] S5. Determine the defect report according to the defect coefficient of each defect sub-model. The casting state parameter set includes the physical parameters of the molten metal in the defect sub-model.

[0228] Optionally, in embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0229] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware of an electronic device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0230] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0231] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0232] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0233] In the several embodiments provided in this application, it should be understood that the disclosed user equipment can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0234] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0235] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0236] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for detecting defects in casting templates, characterized in that, include: Simulated casting is performed on the casting template to be tested to obtain at least one casting model, wherein different casting models in the at least one casting model are used to indicate the model formed at different times; Determine the defect model that satisfies the first defect condition from the at least one casting model; From the defect model, at least one defect sub-model is determined to indicate the presence of molten metal in an unsolidified state; Based on the casting state parameter set of each defect sub-model, the defect coefficient of each defect sub-model is calculated. A defect report is determined based on the defect coefficient of each defect sub-model, wherein the casting state parameter set includes the physical parameters of the molten metal within the defect sub-model.

2. The method according to claim 1, characterized in that, The step of calculating the defect coefficient for each defect sub-model based on its respective set of casting state parameters includes: Each of the at least one defect sub-models is sequentially used as the target defect sub-model; Based on the casting state parameter set of the target defect sub-model, a first defect coefficient of the target defect sub-model is determined, wherein the first defect coefficient is used to indicate the confidence level of the occurrence of a target defect in the target defect sub-model when the casting template is being cast; The process of determining the defect report based on the defect coefficient of each defect sub-model includes: If the first defect coefficient is less than or equal to the defect threshold, it is determined that the target defect sub-model has a defect during the casting process, wherein the defect report includes the target defect sub-model.

3. The method according to claim 2, characterized in that, The step of determining the first defect coefficient of the target defect sub-model based on the set of casting state parameters of the target defect sub-model includes: Based on the type of molten metal and the process type used in the simulated casting, a set of correction coefficients is obtained, wherein the type of molten metal and the process type are matched with the metal process correction coefficients in the set of correction coefficients; Based on the casting state parameter set of the target defect sub-model, solidification characteristic parameters are determined, wherein the solidification characteristic parameters include at least one of solidification rate, temperature gradient value and cooling rate, and each solidification characteristic parameter is matched with other correction coefficients included in the correction coefficient set, excluding the metal process correction coefficient. Each of the solidification characteristic parameters and its corresponding matching correction coefficient are exponentially operated on to obtain at least one second defect coefficient; The first defect coefficient is determined by multiplying the metal processing correction coefficient by at least one second defect coefficient.

4. The method according to claim 3, characterized in that, The step of determining the solidification characteristic parameters of the target defect sub-model based on the set of casting state parameters of the target defect sub-model includes at least one of the following: When the solidification feature parameters include the solidification rate, the solidification rate is determined according to the solidification rates of the molten metal corresponding to the first direction, the second direction, and the third direction within the target defect sub-model. The first direction, the second direction, and the third direction are parallel to the coordinate axes of a three-dimensional rectangular coordinate system established with the center of the target defect sub-model as the origin. The casting state parameter set includes the solidification rate of the molten metal. When the solidification characteristic parameters include the temperature gradient value, the temperature gradient value is determined according to the temperature change rate corresponding to the first direction, the second direction and the third direction in the target defect sub-model, wherein the casting state parameter set includes the temperature change rate. When the solidification characteristic parameters include the cooling rate, the cooling rate is determined based on a first temperature, a second temperature, a first time period, and a second time period, wherein the first temperature is higher than the second temperature, the first time period is used to indicate the time value during which the temperature of the molten metal in the target defect sub-model is higher than the first temperature, and the second time period is used to indicate the time value during which the temperature of the molten metal in the target defect sub-model is higher than the second temperature, and the casting state parameter set includes the first temperature, the second temperature, the first time period, and the second time period.

5. The method according to claim 1, characterized in that, Determining the defect model from at least one of the casting models includes: Iterate through at least one of the casting models according to time, and perform the following operations on the currently traversed casting model: The current casting model is divided to obtain at least one current casting sub-model; Compare the physical parameters of the first casting sub-model and the second casting sub-model, wherein at least one current casting sub-model includes the first casting model and the second casting model; If the comparison result meets the second defect condition, the current casting model is determined to be the defect model, wherein the first casting sub-model and the second casting model are adjacent, and the first defect condition includes the second defect condition; If the second physical parameters of the third casting sub-model satisfy the third defect condition, the current casting model is determined to be the defect model, wherein at least one of the current casting sub-models includes the third casting model, and the first defect condition includes the third defect condition.

6. The method according to claim 5, characterized in that, The comparison of the physical parameters of the first casting sub-model and the second casting sub-model includes: Compare the first solidification rate of the first casting sub-model with the second solidification rate of the second casting model; If the comparison result indicates that the difference between the first solidification rate and the second solidification rate is greater than or equal to the first difference, it is determined that the comparison result satisfies the second defect condition. If the comparison result indicates that the ratio between the first solidification rate and the second solidification rate is greater than or equal to the first ratio, the comparison result is determined to satisfy the second defect condition.

7. The method according to claim 5, characterized in that, Compare the physical parameters of the first and second casting sub-models, including: Compare the first temperature gradient value of the first casting sub-model with the second temperature gradient value of the second casting model; If the comparison result indicates that the difference between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second difference, it is determined that the comparison result satisfies the second defect condition. If the comparison result indicates that the ratio between the first temperature gradient value and the second temperature gradient value is greater than or equal to the second ratio condition, it is determined that the comparison result satisfies the second defect condition. Before determining the current casting model as the defect model, the process includes: If the third temperature gradient value of the third casting sub-model is greater than or equal to the gradient threshold, it is determined that the third temperature gradient value of the third casting sub-model satisfies the third defect condition.

8. A defect prediction device for casting templates, characterized in that, include: A simulation unit is used to simulate casting of the casting template to be tested to obtain at least one casting model, wherein different casting models in the at least one casting model are used to indicate the model formed at different times; The first determining unit is used to determine, from the at least one casting model, a defect model that satisfies the first defect condition; The second determining unit is used to determine from the defect model at least one defect sub-model that contains molten metal in an unsolidified state; The calculation unit is used to calculate the defect coefficient of each defect sub-model based on the casting state parameter set of each defect sub-model. The third determining unit is used to determine a defect report according to the defect coefficient of each defect sub-model, wherein the casting state parameter set includes the physical parameters of the molten metal in the defect sub-model.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program is executed by an electronic device to perform the method according to any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 7 through the computer program.

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