Parametric Modeling Method and Device for Overflow Groove of Die Casting Mold Based on RSM

Abstract

The present invention discloses a method and device for parametric modeling of overflow grooves of die-casting molds based on RSM. The method includes determining the optimal mold opening direction of the die-casting mold; creating reference parameters for the overflow grooves; defining the parting surface of the die-casting mold through the edges of the target castings, and determining the attachment positions of the overflow grooves on the parting surface according to the edge shapes of the castings; recording the casting quality and filling performance under different combinations of reference parameters of the overflow grooves according to the Latin hypercube design sampling method; constructing an RSM response surface model based on the reference parameters of the overflow grooves and the corresponding casting quality and filling performance to obtain the optimal combination of reference parameters of the overflow grooves; the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curves; determining the overflow grooves according to the optimal combination of reference parameters of the overflow grooves, the parting surface and the attachment positions. The present invention improves the efficiency of parameter-driven design of overflow grooves, thereby improving the work efficiency of die-casting mold designers.

Description

Technical Field

[0001] The present invention relates to the technical field of mold numerical analysis, and in particular to a method and device for parameterized modeling of an overflow groove of a die-casting mold based on RSM, a computing device, and a computer program product. Background Art

[0002] The process of mold design is relatively complex and delicate, requiring designers to design molds that meet the requirements of functionality and aesthetics according to the needs of the product and the use environment. Modeling technology is widely used in the geometric shape design, structural analysis and process planning of parts. At present, mold designers usually use general CAD software for design work, which provides parametric modeling methods based on general modeling functions (such as sketching, stretching, rotation, sweeping, lofting, etc.). However, the implementation process of this modeling method is relatively complicated. During the parametric modeling process, designers need to set a large number of parameters to define the geometric shape and properties of parts. Due to the complexity of parameter settings, designers often need to reset multiple parameters when making local changes to parts, resulting in low work efficiency. In addition, the correlation and constraints between parameters also increase the difficulty of design. This leads to the low efficiency of overflow tank modeling, which has become a technical problem that needs to be solved urgently by technicians in this field.

[0003] The existing or traditional overflow tank modeling can only be modeled based on the experience and skills of the designer due to the complex parameter settings, and lacks optimization means, resulting in the overflow tank design result may not be optimal, affecting the overall performance of the die-casting mold. In addition, in the design of die-casting molds, it is often necessary to consider factors such as product complexity and manufacturing process, resulting in unsatisfactory design results.

[0004] Therefore, how to improve the efficiency of overflow tank modeling design has become a technical problem that needs to be solved urgently by technical personnel in this field, and is also the main problem to be solved by the present invention. Summary of the invention

[0005] In view of the above problems, the present invention is proposed to provide a method and device, computing equipment, and computer program product for parametric modeling of overflow grooves of die casting molds based on RSM to overcome the above-mentioned problem of low overflow groove modeling efficiency.

[0006] According to one aspect of the present invention, a method for parameterized modeling of an overflow groove of a die casting mold based on RSM is provided, comprising:

[0007] Determine the optimal die opening direction of the die casting mold based on the edge shape, wall thickness and strength, needle tip and sharp edge of the target die casting;

[0008] Create overflow tank reference parameters, wherein the overflow tank reference parameters include length, water outlet height, water outlet width, tank bottom slope, water inlet method, depth and angle;

[0009] Define the parting surface of the die casting mold through the edges of the target die casting, and determine the attachment position of the overflow groove on the parting surface according to the edge shape of the die casting;

[0010] Record the casting quality and filling performance under different combinations of overflow groove reference parameters according to the Latin hypercube design sampling method;

[0011] Construct an RSM response surface model based on the overflow groove reference parameters and the corresponding casting quality and filling performance, and solve to obtain the optimal combination of overflow groove reference parameters; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curves;

[0012] Determine the overflow groove according to the optimal combination of overflow groove reference parameters, the parting surface and the attachment position.

[0013] In an alternative manner, the RSM response surface model is specifically:

[0014]

[0015] Wherein, , , , are the lower bound values at the trapezoidal fuzzy number level, , , , are the upper bound values at the trapezoidal fuzzy number level, and are any overflow groove reference parameters, and k is the total number of overflow groove reference parameters.

[0016] In an alternative manner, the solving to obtain the optimal combination of overflow groove reference parameters further includes:

[0017] Take the overflow groove reference parameters and the corresponding casting quality and filling performance as response variables;

[0018] If the response variables do not satisfy the normal relationship, use the Box-Cox transformation to process them to reduce the influence of skewness and kurtosis;

[0019] If there is uncertainty in the response variables, introduce trapezoidal fuzzy numbers into the RSM response surface model to represent the uncertainty of the response;

[0020] Estimate the relationship between the response variables and the overflow groove reference parameters through the RSM response surface model;

[0021] Obtain the expected response of the overflow tank reference parameters corresponding to different combinations of the response variables through the individual conditional expectation curve.

[0022] In an alternative manner, the determining of the overflow tank based on the optimal overflow tank reference parameter combination, the parting surface, and the attachment position further includes:

[0023] Determine the relative position of the overflow tank in the die-casting mold according to the position, shape, and size of the parting surface, and determine the predetermined attachment position of the overflow tank in the die-casting mold;

[0024] Mark the position, shape, and the predetermined attachment position of the parting surface;

[0025] Determine the shape and size of the overflow tank according to the optimal overflow tank reference parameter combination to ensure its coordination with the parting surface and the attachment position.

[0026] In an alternative manner, the obtaining of the expected response of the overflow tank reference parameters corresponding to different combinations of the response variables through the individual conditional expectation curve further includes:

[0027] Construct an individual conditional expectation curve ICE for each overflow tank reference parameter;

[0028] Calculate the expected response values of the casting quality and filling performance under different parameter combinations according to the individual conditional expectation curve ICE.

[0029] In an alternative manner, the method further includes:

[0030] Determine the lower bound and the upper bound of the trapezoidal fuzzy number according to the inflection point of the lower membership function and the inflection point of the upper membership function respectively.

[0031] In an alternative manner, the formula of the Box-Cox transformation is:

[0032]

[0033] where, , is the original response variable, is the transformed response variable, is the parameter of Box-Cox, is a preset constant.

[0034] According to another aspect of the present invention, there is provided a parametric modeling device for an overflow tank of a die-casting mold based on RSM, including:

[0035] Determine the optimal mold opening direction of the die-casting mold according to the edge shape, wall thickness and strength, needle tip, and sharp edge of the target die-casting part;

[0036] A reference parameter creation module for creating reference parameters of an overflow groove, where the reference parameters of the overflow groove include length, water outlet height, water outlet width, bottom slope of the groove, water inlet mode, depth, and angle;

[0037] An attachment position determination module for defining a parting surface of the die-casting mold through an edge of the target die-cast part and determining an attachment position of the overflow groove on the parting surface according to an edge shape of the die-cast part;

[0038] A data sampling module for recording the casting quality and filling performance under different combinations of reference parameters of the overflow groove according to the Latin hypercube design sampling method;

[0039] A parameter solving module for constructing an RSM response surface model based on the reference parameters of the overflow groove and the corresponding casting quality and filling performance, and solving to obtain an optimal combination of reference parameters of the overflow groove; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation, and individual conditional expectation curves;

[0040] An overflow groove determination module for determining the overflow groove according to the optimal combination of reference parameters of the overflow groove, the parting surface, and the attachment position.

[0041] According to another aspect of the present invention, there is provided a computing device, including: a processor, a memory, a communication interface, and a communication bus, where the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0042] According to still another aspect of the present invention, there is provided a computer program product including at least one executable instruction, and the executable instruction causes the processor to execute operations corresponding to the above method for parametric modeling of an overflow groove of a die-casting mold based on RSM.

[0043] By introducing technologies such as the response surface method (RSM) and fuzzy mathematics, the present invention aims to provide a simple, efficient, and accurate modeling method for an overflow groove to improve the efficiency and quality of die-casting mold design.

[0044] According to the solution provided by the present invention, the optimal mold opening direction of the die-casting mold is determined based on the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-cast part; the reference parameters of the overflow groove are created, and the reference parameters of the overflow groove include length, water outlet height, water outlet width, bottom slope of the groove, water inlet mode, depth and angle; the parting surface of the die-casting mold is defined by the edge of the target die-cast part, and the attachment position of the overflow groove on the parting surface is determined according to the edge shape of the die-cast part; the casting quality and filling performance under different combinations of the reference parameters of the overflow groove are recorded according to the Latin hypercube design sampling method; an RSM response surface model is constructed based on the reference parameters of the overflow groove and the corresponding casting quality and filling performance, and the optimal combination of the reference parameters of the overflow groove is obtained by solving; wherein, the trapezoidal fuzzy number, Box-Cox transformation and individual conditional expectation curve are introduced into the RSM response surface model; the overflow groove is determined according to the optimal combination of the reference parameters of the overflow groove, the parting surface and the attachment position. Through parametric modeling, the present invention can accurately determine the optimal parameter combination of the overflow groove in the die-casting mold, such as length, water outlet height, water outlet width, etc., and improve the quality and filling performance of the casting. The trapezoidal fuzzy number is introduced into the RSM response surface model to effectively handle the uncertainty of the response variable and make the model closer to the actual production environment. At the same time, the Box-Cox transformation reduces the influence of data skewness and kurtosis and improves the accuracy of the model. Through the individual conditional expectation curve, the expected response of the reference parameters of the overflow groove corresponding to different combinations of response variables can be estimated more accurately, providing more accurate guidance for actual production. Since this method can accurately predict the casting quality and filling performance under different parameter combinations, it helps production personnel quickly find the optimal parameter combination, reduces the number of trial and errors, improves production efficiency and product quality. It is not only applicable to specific die-casting molds and castings, but also can adapt to different production environments and product requirements by adjusting parameters and models, and has strong adaptability and scalability. And it is easy to implement and integrate into the existing production process without adding too much additional cost and workload.

[0045] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the embodiments of the present invention. Description of the Drawings

[0046] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0047] Figure 1Shows the schematic flow chart of the parametric modeling method for the overflow groove of a die-casting mold based on RSM according to an embodiment of the present invention;

[0048] Figure 2 Shows the schematic framework diagram of the parametric modeling device for the overflow groove of a die-casting mold based on RSM according to an embodiment of the present invention;

[0049] Figure 3 Shows the schematic structural diagram of a computing device according to an embodiment of the present invention. Detailed implementation manners

[0050] Hereinafter, the exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0051] Figure 1 Shows the schematic flow chart of the parametric modeling method for the overflow groove of a die-casting mold based on RSM according to an embodiment of the present invention. Specifically, as Figure 1 shown, it includes the following steps:

[0052] Step S101, determine the optimal mold opening direction of the die-casting mold according to the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-casting part.

[0053] In this embodiment, by optimizing the mold opening direction, it is ensured that the casting flows uniformly during the filling process, reducing defects such as gas traps and cold shuts, thereby improving the quality and reliability of the product. The optimized mold opening direction improves the manufacturability and production efficiency of the mold, reduces production costs and scrap rates. As shown in Table 1, direction B can be selected as the optimal mold opening direction because it performs well in terms of filling performance, wall thickness uniformity, and avoidance of needle tips / sharp edges, and the mold manufacturability is also good.

[0054] Table 1

[0055]

[0056] In Table 1, the filling performance evaluates the flow uniformity and gas traps during the filling process of the casting. The wall thickness uniformity evaluates the distribution uniformity of the wall thickness of the casting. The mold manufacturability evaluates the difficulty and cost of mold manufacturing.

[0057] Step S102, create overflow groove reference parameters, where the overflow groove reference parameters include length, water outlet height, water outlet width, bottom slope of the groove, water inlet method, depth, and angle.

[0058] As shown in Table 2, determine the appropriate length of the overflow groove according to the mold size and filling requirements. Set the water outlet height based on the height and filling speed of the die-cast part to ensure sufficient liquid outflow. Set the appropriate water outlet width according to the mold design and liquid flow rate. Design the slope of the groove bottom to promote liquid flow and reduce residue. Select the direct water inlet or indirect water inlet method according to the filling requirements of the die-cast part and the mold design. Determine the appropriate depth according to the height of the die-cast part and the liquid pressure. The angles include the groove bottom angle and the water outlet angle, which are used to optimize liquid flow and reduce gas traps.

[0059] Table 2

[0060]

[0061] In step S103, define the parting surface of the die-casting mold through the edges of the target die-cast part, and determine the attachment position of the overflow groove on the parting surface according to the edge shape of the die-cast part.

[0062] The parting surface of the die-casting mold is the contact surface that divides a part of the mold directly forming the plastic part into several parts according to the structure of the plastic part in order to take out the formed plastic part from the mold cavity or to meet the forming requirements such as placing inserts and exhausting air. The position of the parting surface should be selected at the maximum outer contour in the demolding direction of the casting, that is, through the largest cross-section of the casting in this direction. After determining the parting surface, determining the attachment position of the overflow groove on the parting surface depends on the edge shape of the die-cast part and the requirements of the filling performance. By defining the parting surface according to the edge shape of the target die-cast part and determining the attachment position of the overflow groove on the parting surface, it is ensured that the molten metal flows evenly in the mold, reducing defects such as gas traps and cold shuts, thereby improving the filling performance.

[0063] In step S104, record the casting quality and filling performance under different combinations of overflow groove reference parameters according to the Latin hypercube design sampling method.

[0064] Specifically, set the number of samples N, that is, the number of different parameter combinations to be tested. For each parameter, evenly divide its value range into N intervals. Randomly select a value within each interval of each parameter, but ensure that the N values of each parameter are evenly distributed within the entire value range. Combine the randomly selected values of each parameter together to form different parameter combinations (representing different overflow groove designs). Manufacture castings with different overflow groove designs according to the generated parameter combinations, and record their quality and filling performance, which can be completed through casting experiments or simulation software.

[0065] In this embodiment, the Latin hypercube sampling method can ensure uniform coverage of the parameter space, making the test samples representative. Compared with other sampling methods, fewer samples can be used when reaching the same threshold, reducing the experimental cost. By reducing the correlation between input variables, Latin hypercube sampling can improve the accuracy of Monte Carlo simulation, thus more accurately evaluating the casting quality and filling performance. The casting quality and filling performance data under different overflow groove reference parameter combinations are shown in Table 3.

[0066] Table 3

[0067]

[0068] Step S105, construct an RSM response surface model based on the overflow groove reference parameters and the corresponding casting quality and filling performance, and solve to obtain the optimal overflow groove reference parameter combination; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation, and individual conditional expectation curves.

[0069] In this embodiment, trapezoidal fuzzy numbers are used to represent the uncertainty existing in the casting quality and filling performance data. Trapezoidal fuzzy numbers are defined by four parameters: lower bound, lower membership degree, upper membership degree, and upper bound. Box-Cox transformation is used to stabilize the variance and make the data approach a normal distribution. Box-Cox transformation is performed on the casting quality and filling performance data to improve the fitting effect of the model. The RSM response surface model can be constructed using multiple regression methods (such as polynomial regression) (using the transformed casting quality and filling performance data as response variables and the overflow groove reference parameters as independent variables). The individual conditional expectation curve describes the expected value of the response variable under given independent variables. In the RSM model, the influence of different parameter combinations on the casting quality and filling performance is more clearly described through the individual conditional expectation curve.

[0070] The casting quality and filling performance data under different overflow groove reference parameter combinations, as well as the four parameters of the trapezoidal fuzzy number (lower bound, lower membership degree, upper membership degree, and upper bound), are shown in Table 4.

[0071] Table 4

[0072]

[0073] In Table 4, "Casting quality (after transformation)" and "Filling performance (after transformation)" are the data after Box-Cox transformation. "Casting quality trapezoidal fuzzy number" and "Filling performance trapezoidal fuzzy number" represent the trapezoidal fuzzy numbers of the corresponding data, where (W n-d , d l , d r , W n+d ) represent the lower bound, lower membership degree, upper membership degree, and upper bound.

[0074] In this embodiment, the RSM response surface model is specifically as follows:

[0075]

[0076] Among them, , , , are the lower bound values at the level of the trapezoidal fuzzy number , , , , are the upper bound values at the level of the trapezoidal fuzzy number , and are arbitrary overflow tank reference parameters, and k is the total number of overflow tank reference parameters.

[0077] Trapezoidal fuzzy numbers allow considering the uncertainty of data during the modeling process. During the casting process, due to the influence of various factors (such as material properties, process conditions, etc.), the quality and filling performance of castings often fluctuate. By introducing trapezoidal fuzzy numbers, this uncertainty can be quantified, and a more robust model can be constructed. The coefficients in the model (such as , , etc.) depend on the level of the trapezoidal fuzzy number . By adjusting the value of , different levels of uncertainty can be simulated. Under the constraint of trapezoidal fuzzy numbers, an optimization algorithm is used to solve the optimal combination of overflow tank reference parameters, considering not only the optimal values of the parameters but also the uncertainty of the parameter values.

[0078] For example, for two overflow tank reference parameters including length and water outlet height. There is uncertainty due to measurement errors and other factors. To construct an RSM model and consider uncertainty, trapezoidal fuzzy numbers are introduced to represent the quality and filling performance of castings. Specifically, trapezoidal fuzzy numbers are defined for the quality and filling performance of castings respectively, where the lower bound, lower membership degree, upper membership degree, and upper bound represent the uncertainty range of the data. Then, the above RSM model form is used for modeling. By fitting the model and solving the optimal parameter combination, a set of optimal overflow tank reference parameter values are found, which can still maximize the quality and filling performance of castings considering data uncertainty, improving the quality stability of castings.

[0079] Among them, the lower bound of the trapezoidal fuzzy number is:

[0080]

[0081] The upper bound of the trapezoidal fuzzy number is:

[0082]

[0083] Among them, a and d are respectively the lower bound and the upper bound of the trapezoidal fuzzy number, b is the inflection point of the lower membership function, and c is the inflection point of the upper membership function.

[0084] In this embodiment, the formula of the Box-Cox transformation is:

[0085]

[0086] Among them, , is the original response variable, is the transformed response variable, is the parameter of Box-Cox, is a preset constant.

[0087] In this embodiment, the function of the individual conditional expectation curve is:

[0088]

[0089] Among them, is the parameter vector.

[0090] The Individual Conditional Expectation Curve (ICE) is used for the relationship between the casting quality or filling performance and the explanatory variables such as the overflow tank reference parameters. In the fuzzy number or uncertainty model, the ICE curve is used to better understand how the response variable changes with the change of a specific variable under the condition of given other variables. As shown in Table 5, it includes the casting quality (as the response variable) and two explanatory variables: the overflow tank length (x1) and the water outlet height (x2). To generate the ICE curve, select a specific explanatory variable (such as x1 or x2), then fix the values of other variables (such as the median or average value of x2), and then plot the relationship between the response variable (casting quality) and the selected explanatory variable. For example, select a fixed value of x2 (such as the median or average value of x2). Filter out all rows with this fixed x2 value. Plot the relationship between x1 (horizontal axis) and y (vertical axis) in the filtered data.

[0091] Table 5

[0092]

[0093] In an alternative way, the solution to obtain the optimal combination of overflow tank reference parameters further includes:

[0094] Taking the overflow tank reference parameters and the corresponding casting quality and filling performance as the response variables;

[0095] If the response variable does not satisfy the normal relationship, use the Box-Cox transformation to process it to reduce the influence of skewness and kurtosis;

[0096] If there is uncertainty in the response variable, introduce trapezoidal fuzzy numbers into the RSM response surface model to represent the uncertainty of the response;

[0097] Estimate the relationship between the response variable and the overflow groove reference parameters through the RSM response surface model;

[0098] Obtain the expected response of the overflow groove reference parameters corresponding to different combinations of the response variable through the individual conditional expectation curve. Specifically, construct an individual conditional expectation curve ICE for each overflow groove reference parameter; according to the individual conditional expectation curve ICE, calculate the expected response values of the casting quality and filling performance under different parameter combinations.

[0099] Step S106, determine the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

[0100] In this embodiment, determine the relative position of the overflow groove in the die-casting mold according to the position, shape and size of the parting surface, and determine the predetermined attachment position of the overflow groove on the die-casting mold. Mark the position, shape and predetermined attachment position of the parting surface. According to the optimal overflow groove reference parameter combination, determine the shape and size of the overflow groove to ensure its coordination with the parting surface and the attachment position.

[0101] Specifically, to ensure the effectiveness of the die-casting mold and the quality of the casting, determine the relative position of the overflow groove in the die-casting mold according to the position, shape and size of the parting surface. Mark the specific position, shape and predetermined attachment position of the parting surface. Use the optimal overflow groove reference parameter combination to determine the shape and size of the overflow groove. When determining the shape and size of the overflow groove, special attention should be paid to its coordination with the parting surface and the attachment position. The overflow groove should be able to smoothly guide the excess molten metal to flow out from the parting surface while avoiding interference with other parts of the mold. In addition, its position and size also need to consider factors such as the shrinkage and deformation of the casting to ensure the quality and dimensional accuracy of the final casting. Finally, design and manufacture the overflow groove in the die-casting mold according to the determined parameters and positions.

[0102] As shown in Tables 6 to 8, illustrate how to determine the position and size of the overflow groove in the die-casting mold according to the position and characteristics of the parting surface.

[0103] Table 6: Parting surface and attachment position information

[0104]

[0105] Table 7: Optimal combination of reference parameters for overflow channels

[0106]

[0107] Table 8: Design parameters of overflow channels

[0108]

[0109] Based on the information in Table 6 and Table 7, the specific design parameters of the overflow channels are given in Table 8. These parameters consider the position, shape, and size of the parting surface, as well as the optimal combination of reference parameters for the overflow channels, to ensure the coordination of the overflow channels with the parting surface and the attachment position.

[0110] According to the solution provided by the present invention, the optimal die opening direction of the die-casting mold is determined based on the edge shape, wall thickness and strength, avoidance of needle tips and sharp edges of the target die-cast part; reference parameters for the overflow channels are created, and the reference parameters for the overflow channels include length, water outlet height, water outlet width, bottom slope of the channel, water inlet method, depth, and angle; the parting surface of the die-casting mold is defined by the edge of the target die-cast part, and the attachment position of the overflow channel on the parting surface is determined according to the edge shape of the die-cast part; the casting quality and filling performance under different combinations of reference parameters for the overflow channels are recorded according to the Latin hypercube design sampling method; an RSM response surface model is constructed based on the reference parameters for the overflow channels and the corresponding casting quality and filling performance, and the optimal combination of reference parameters for the overflow channels is obtained by solving; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation, and individual conditional expectation curves; the overflow channel is determined according to the optimal combination of reference parameters for the overflow channels, the parting surface, and the attachment position. By parametric modeling, the present invention can accurately determine the optimal parameter combination of the overflow channels in the die-casting mold, such as length, water outlet height, water outlet width, etc., improving the quality and filling performance of the casting. The introduction of trapezoidal fuzzy numbers in the RSM response surface model effectively handles the uncertainty of the response variables, making the model closer to the actual production environment. At the same time, the Box-Cox transformation reduces the influence of data skewness and kurtosis, improving the accuracy of the model. Through the individual conditional expectation curve, the expected response of the reference parameters for the overflow channels corresponding to different combinations of response variables can be estimated more accurately, providing more precise guidance for actual production. Since this method can accurately predict the casting quality and filling performance under different parameter combinations, it helps production personnel quickly find the optimal parameter combination, reducing the number of trial and errors and improving production efficiency and product quality. It is not only applicable to specific die-casting molds and castings, but also can adapt to different production environments and product requirements by adjusting parameters and models, with strong adaptability and scalability. It is easy to implement and integrate into the existing production process without adding too much additional cost and workload.

[0111] Figure 2The structural schematic diagram of the device for parametric modeling of the overflow groove of a die-casting mold based on RSM according to an embodiment of the present invention is shown. The device for parametric modeling of the overflow groove of a die-casting mold based on RSM includes: a mold opening direction determination module 210, a reference parameter creation module 220, an attachment position determination module 230, a data sampling module 240, a parameter solution module 250, and an overflow groove determination module 260.

[0112] The mold opening direction determination module 210 is configured to determine the optimal mold opening direction of the die-casting mold according to the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-casting part;

[0113] The reference parameter creation module 220 is configured to create reference parameters of the overflow groove, and the reference parameters of the overflow groove include length, water outlet height, water outlet width, bottom slope of the groove, water inlet mode, depth, and angle;

[0114] The attachment position determination module 230 is configured to define the parting surface of the die-casting mold through the edge of the target die-casting part, and determine the attachment position of the overflow groove on the parting surface according to the edge shape of the die-casting part;

[0115] The data sampling module 240 is configured to record the casting quality and filling performance under different combinations of reference parameters of the overflow groove according to the Latin hypercube design sampling method;

[0116] The parameter solution module 250 is configured to construct an RSM response surface model according to the reference parameters of the overflow groove and the corresponding casting quality and filling performance, and solve to obtain the optimal combination of reference parameters of the overflow groove; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation, and individual conditional expectation curves;

[0117] The overflow groove determination module 260 is configured to determine the overflow groove according to the optimal combination of reference parameters of the overflow groove, the parting surface, and the attachment position.

[0118] In an optional manner, the RSM response surface model is specifically:

[0119]

[0120] Wherein, 、 、 、 are the lower bound values at the level of the trapezoidal fuzzy number, 、 、 、 are the upper bound values at the level of the trapezoidal fuzzy number, and is the reference parameter of any overflow groove, and k is the total number of reference parameters of the overflow groove.

[0121] In an alternative approach, the solution to obtain the optimal combination of overflow groove reference parameters further includes:

[0122] Taking the overflow groove reference parameters and the corresponding casting quality and filling performance as response variables;

[0123] If the response variables do not satisfy the normal relationship, use the Box-Cox transformation to process them to reduce the influence of skewness and kurtosis;

[0124] If there is uncertainty in the response variables, introduce trapezoidal fuzzy numbers into the RSM response surface model to represent the uncertainty of the response;

[0125] Estimate the relationship between the response variables and the overflow groove reference parameters through the RSM response surface model;

[0126] Obtain the expected response of the overflow groove reference parameters corresponding to different combinations of the response variables through the individual conditional expectation curve.

[0127] In an alternative approach, the determination of the overflow groove according to the optimal combination of overflow groove reference parameters, the parting surface, and the attachment position further includes:

[0128] Determine the relative position of the overflow groove in the die-casting mold according to the position, shape, and size of the parting surface, and determine the predetermined attachment position of the overflow groove in the die-casting mold;

[0129] Mark the position, shape, and predetermined attachment position of the parting surface;

[0130] Determine the shape and size of the overflow groove according to the optimal combination of overflow groove reference parameters to ensure its coordination with the parting surface and the attachment position.

[0131] In an alternative approach, the obtaining of the expected response of the overflow groove reference parameters corresponding to different combinations of the response variables through the individual conditional expectation curve further includes:

[0132] Construct an individual conditional expectation curve ICE for each overflow groove reference parameter;

[0133] Calculate the expected response values of the casting quality and filling performance under different parameter combinations according to the individual conditional expectation curve ICE.

[0134] In an alternative approach, the method further includes:

[0135] Determine the lower bound and upper bound of the trapezoidal fuzzy number according to the inflection point of the lower membership function and the inflection point of the upper membership function respectively.

[0136] In an alternative manner, the formula of the Box-Cox transformation is:

[0137]

[0138] Where, , is the original response variable, is the transformed response variable, is the parameter of Box-Cox, is a preset constant.

[0139] Figure 3 FIG. shows a schematic structural diagram of an embodiment of the computing device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0140] As Figure 3 shown, the computing device may include: a processor 302, a communication interface 304, a memory 306, and a communication bus 308.

[0141] Wherein: the processor 302, the communication interface 304, and the memory 306 communicate with each other through the communication bus 308. The communication interface 304 is used to communicate with network elements of other devices such as clients or other servers. The processor 302 is used to execute the program 310, and specifically can execute the relevant steps in the above-mentioned embodiment of the method for parametric modeling of the overflow groove of the die-casting mold based on RSM.

[0142] Specifically, the program 310 may include program code, and the program code includes computer operation instructions.

[0143] The processor 302 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the computing device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0144] The memory 306 is used to store the program 310. The memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0145] According to the solution provided by the present invention, the optimal mold opening direction of the die-casting mold is determined based on the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-cast part; the reference parameters of the overflow groove are created, and the reference parameters of the overflow groove include length, water outlet height, water outlet width, groove bottom slope, water inlet mode, depth and angle; the parting surface of the die-casting mold is defined by the edge of the target die-cast part, and the attachment position of the overflow groove on the parting surface is determined according to the edge shape of the die-cast part; the casting quality and filling performance under different combinations of overflow groove reference parameters are recorded according to the Latin hypercube design sampling method; an RSM response surface model is constructed based on the overflow groove reference parameters and the corresponding casting quality and filling performance, and the optimal combination of overflow groove reference parameters is obtained by solving; wherein, the trapezoidal fuzzy number, Box-Cox transformation and individual conditional expectation curve are introduced into the RSM response surface model; the overflow groove is determined according to the optimal combination of overflow groove reference parameters, the parting surface and the attachment position.

[0146] Through parametric modeling, the present invention can accurately determine the optimal parameter combination of the overflow groove in the die-casting mold, such as length, water outlet height, water outlet width, etc., and improve the quality and filling performance of the casting. The trapezoidal fuzzy number is introduced into the RSM response surface model to effectively handle the uncertainty of the response variable and make the model closer to the actual production environment. At the same time, the Box-Cox transformation reduces the influence of data skewness and kurtosis and improves the accuracy of the model. Through the individual conditional expectation curve, the expected response of the overflow groove reference parameters corresponding to different combinations of response variables can be estimated more accurately, providing more accurate guidance for actual production. Since this method can accurately predict the casting quality and filling performance under different parameter combinations, it helps production personnel quickly find the optimal parameter combination, reduces the number of trial and errors, improves production efficiency and product quality. It is not only applicable to specific die-casting molds and castings, but also can adapt to different production environments and product requirements by adjusting parameters and models. It has strong adaptability and scalability, is easy to implement and integrate into the existing production process, and does not increase too much additional cost and workload.

[0147] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be adopted to combine all the features disclosed in this specification (including the accompanying claims, abstract and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise explicitly stated, each feature disclosed in this specification (including the accompanying claims, abstract and drawings) can be replaced by an alternative feature that provides the same, equivalent or similar purpose. In addition, those skilled in the art can understand that although some of the embodiments herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of the present invention and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination. The present invention can be implemented by means of hardware including several different elements and by means of a properly programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same hardware item. The steps in the above embodiments, unless otherwise specified, should not be construed as a limitation on the execution order.

Claims

1. A parametric modeling method for the overflow tank of a die-casting mold based on RSM, characterized in that, Including: Determine the optimal mold opening direction of the die-casting mold according to the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-casting part; Create reference parameters for the overflow groove, where the reference parameters for the overflow groove include length, water outlet height, water outlet width, bottom slope of the groove, water inlet mode, depth, and angle; Define the parting surface of the die-casting mold through the edge of the target die-casting part, and determine the attachment position of the overflow groove on the parting surface according to the edge shape of the die-casting part; Record the casting quality and filling performance under different combinations of overflow groove reference parameters according to the Latin hypercube design sampling method; Construct an RSM response surface model based on the overflow groove reference parameters and the corresponding casting quality and filling performance, and solve to obtain the optimal combination of overflow groove reference parameters; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation, and individual conditional expectation curves; Determine the overflow groove according to the optimal combination of overflow groove reference parameters, the parting surface, and the attachment position; The RSM response surface model is specifically: Among them, , , , are the lower bound values at the trapezoidal fuzzy number level, , , , are the upper bound values at the trapezoidal fuzzy number level, and are arbitrary overflow tank reference parameters, and k is the total number of overflow tank reference parameters.

2. The parametric modeling method for the overflow groove of a die-casting mold based on RSM according to claim 1, wherein The solution to obtain the optimal combination of overflow groove reference parameters further includes: Use the overflow groove reference parameters and the corresponding casting quality and filling performance as response variables; If the response variables do not satisfy the normal relationship, use the Box-Cox transformation to process them to reduce the influence of skewness and kurtosis; If there is uncertainty in the response variables, introduce trapezoidal fuzzy numbers into the RSM response surface model to represent the uncertainty of the response; Estimate the relationship between the response variables and the overflow groove reference parameters through the RSM response surface model; Obtain the expected response of the overflow groove reference parameters corresponding to different combinations of the response variables through the individual conditional expectation curve.

3. The parametric modeling method for the overflow groove of a die-casting mold based on RSM according to claim 1, wherein, The determination of the overflow groove according to the optimal combination of overflow groove reference parameters, the parting surface, and the attachment position further includes: Determine the relative position of the overflow groove in the die-casting mold according to the position, shape, and size of the parting surface, and determine the predetermined attachment position of the overflow groove on the die-casting mold; Mark the position, shape, and predetermined attachment position of the parting surface; Determine the shape and size of the overflow groove according to the optimal combination of overflow groove reference parameters to ensure its coordination with the parting surface and the attachment position.

4. The parametric modeling method for the overflow groove of a die-casting mold based on RSM according to claim 2, wherein, The obtaining of the expected response of the overflow groove reference parameters corresponding to different combinations of the response variables through the individual conditional expectation curve further includes: Construct an individual conditional expectation curve ICE for each overflow groove reference parameter; Calculate the expected response values of the casting quality and filling performance under different parameter combinations according to the individual conditional expectation curve ICE.

5. The parametric modeling method for the overflow groove of a die-casting mold based on RSM according to claim 1, characterized in that, The method further includes: Determine the lower bound and upper bound of the trapezoidal fuzzy number according to the inflection point of the lower membership function and the inflection point of the upper membership function respectively.

6. The parametric modeling method for the overflow groove of a die-casting mold based on RSM according to claim 1, characterized in that, The formula for the Box-Cox transformation is: Among them, , is the original response variable, is the transformed response variable, is the parameter of Box-Cox, is a preset constant.

7. A device for parametric modeling of overflow grooves of die-casting molds based on RSM, characterized in that, Implement the method for parametric modeling of the overflow groove of the die-casting mold based on RSM as described in any one of claims 1-6, including: A mold opening direction determination module for determining the optimal mold opening direction of the die-casting mold according to the edge shape, wall thickness and strength, needle tip and avoidance of sharp edges of the target die-casting part; A reference parameter creation module for creating reference parameters of the overflow groove, where the reference parameters of the overflow groove include length, water outlet height, water outlet width, bottom slope of the groove, water inlet mode, depth, and angle; An attachment position determination module for defining the parting surface of the die-casting mold through the edges of the target die-cast part, and determining the attachment position of the overflow groove on the parting surface according to the edge shape of the die-cast part; A data sampling module for recording the casting quality and filling performance under different combinations of reference parameters of the overflow groove according to the Latin hypercube design sampling method; A parameter solving module for constructing an RSM response surface model based on the reference parameters of the overflow groove and the corresponding casting quality and filling performance, and solving to obtain the optimal combination of reference parameters of the overflow groove; wherein, the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation, and individual conditional expectation curves; An overflow groove determination module for determining the overflow groove according to the optimal combination of reference parameters of the overflow groove, the parting surface, and the attachment position; 8. A computing device, characterized in that, Comprising: A processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete mutual communication through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to the RSM-based parametric modeling method for the overflow groove of the die-casting mold according to any one of claims 1-6; 9. A computer program product, characterized in that, Comprising at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to the RSM-based parametric modeling method for the overflow groove of the die-casting mold according to any one of claims 1-6.

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