Method and device for parameterized modeling of overflow groove of die-casting die based on RSM

Abstract

The invention discloses a parametric modeling method and device for an overflow groove of a die-casting die based on RSM. The method comprises the steps that the optimal die opening direction of the die-casting die is determined; creating reference parameters of the overflow groove; a parting surface of the die-casting die is defined through the edge of the target die casting, and the attachment position of the overflow groove on the parting surface is determined according to the edge shape of the die casting; according to a Latin hypercube design sampling method, the casting quality and the mold filling performance under different overflow groove reference parameter combinations are recorded; an RSM response surface model is constructed according to the reference parameters of the overflow groove and the corresponding casting quality and mold filling performance, and an optimal reference parameter combination of the overflow groove is obtained; a trapezoidal fuzzy number, Box-Cox transformation and an individual condition expectation curve are introduced into the RSM response surface model; and determining the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position. According to the method, the efficiency of parameter driving design of the overflow groove is improved, so that the working efficiency of die-casting die designers is improved.

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: 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; 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; Defining a parting surface of the die-casting mold by the edge of the target die-casting, and determining an attachment position of the overflow groove on the parting surface according to an edge shape of the die-casting; The casting quality and filling performance under different overflow tank benchmark parameter combinations were recorded according to the Latin hypercube design sampling method; According to the overflow trough reference parameters and the corresponding casting quality and filling performance, an RSM response surface model is constructed to solve and obtain the optimal overflow trough reference parameter combination; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curve; The overflow groove is determined according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

[0007] In an optional manner, the RSM response surface model is specifically: in, , , , For the trapezoidal fuzzy number The lower limit of the level, , , , For the trapezoidal fuzzy number The upper limit of the level, and is any overflow tank reference parameter, and k is the total number of overflow tank reference parameters.

[0008] In an optional manner, the step of solving for an optimal overflow tank reference parameter combination further includes: The overflow tank reference parameters and the corresponding casting quality and filling performance are used as response variables; If the response variable does not satisfy the normal relationship, it is processed using Box-Cox transformation to reduce the influence of skewness and kurtosis; If there is uncertainty in the response variable, a trapezoidal fuzzy number is introduced into the RSM response surface model to represent the uncertainty of the response; Estimating the relationship between the response variable and the overflow tank benchmark parameter by the RSM response surface model; The expected responses of the overflow tank reference parameters corresponding to different combinations of the response variables are obtained through the individual condition expected curve.

[0009] In an optional manner, the determining the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position further comprises: 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; Marking the position, shape and predetermined attachment position of the parting surface; According to the optimal overflow groove reference parameter combination, the shape and size of the overflow groove are determined to ensure that it is coordinated with the parting surface and the attachment position.

[0010] In an optional manner, obtaining the expected response of the overflow tank reference parameter corresponding to different combinations of the response variables through the individual condition expected curve further includes: Construct individual conditional expectation curve ICE for each overflow tank benchmark parameter; According to the individual condition expectation curve ICE, the expected response values ​​of casting quality and filling performance under different parameter combinations are calculated.

[0011] In an optional manner, the method further includes: The lower bound and the upper bound of the trapezoidal fuzzy number are determined according to the lower membership function inflection point and the upper membership function inflection point, respectively.

[0012] In an optional manner, the formula for the Box-Cox transformation is: in, , is the original response variable, is the transformed response variable, is the Box-Cox parameter, is a preset constant.

[0013] According to another aspect of the present invention, a device for parameterizing modeling of an overflow tank of a die casting mold based on RSM is provided, comprising: 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; A reference parameter creation module is used to 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; an attachment position determination module, used to define a parting surface of the die-casting mold by an edge of the target die-casting part, and determine an attachment position of the overflow groove on the parting surface according to an edge shape of the die-casting part; Data sampling module, used to record casting quality and filling performance under different overflow tank benchmark parameter combinations according to Latin hypercube design sampling method; A parameter solving module is used to construct an RSM response surface model according to the overflow trough reference parameters and the corresponding casting quality and filling performance, and solve to obtain the optimal overflow trough reference parameter combination; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curve; The overflow groove determination module is used to determine the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

[0014] According to another aspect of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; According to another aspect of the present invention, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables the processor to execute operations corresponding to the above-mentioned RSM-based parametric modeling method for overflow grooves of die casting molds.

[0015] The present invention aims to provide a simple, efficient and accurate overflow groove modeling method by introducing response surface methodology (RSM) and fuzzy mathematics to improve the efficiency and quality of die casting mold design.

[0016] According to the solution provided by the present invention, the optimal die opening direction of the die-casting mold is determined according to the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-casting; the overflow groove reference parameters are created, and the overflow groove reference parameters include length, water outlet height, water outlet width, groove bottom slope, water inlet method, depth and angle; the parting surface of the die-casting mold is defined by the edge of the target die-casting, and the attachment position of the overflow groove on the parting surface is determined according to the edge shape of the die-casting; the casting quality and filling performance under different overflow groove reference parameter combinations are recorded according to the Latin hypercube design sampling method; an RSM response surface model is constructed according to the overflow groove reference parameters and the corresponding casting quality and filling performance, and the optimal overflow groove reference parameter combination is solved; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curves; the overflow groove is determined according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position. The present invention can accurately determine the optimal parameter combination of overflow troughs such as length, water outlet height, and water outlet width in a die-casting mold through parameterized modeling, thereby improving the quality and filling performance of castings. Trapezoidal fuzzy numbers are introduced into the RSM response surface model to effectively handle the uncertainty of response variables, so that the model is closer to the actual production environment. At the same time, the Box-Cox transformation reduces the skewness and peak state effects of the data and improves the accuracy of the model. Through the individual conditional expectation curve, the expected response of the overflow trough reference parameters corresponding to different response variable combinations is more accurately estimated, providing more accurate guidance for actual production. Since the method can accurately predict the casting quality and filling performance under different parameter combinations, it helps production personnel to quickly find the optimal parameter combination, reduce the number of trial and error, improve production efficiency and product quality, is not only suitable for 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.

[0017] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1A schematic flow chart of a method for parameterizing a die casting die overflow tank based on RSM according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a framework of a device for parameterizing a die casting die overflow tank based on RSM according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying 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 in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0020] Figure 1 FIG. 1 is a flow chart of a method for parameterizing the overflow groove of a die casting mold based on RSM according to an embodiment of the present invention. Specifically, Figure 1 As shown, the following steps are included: Step S101, determining the optimal 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.

[0021] In this embodiment, the mold opening direction is optimized to ensure uniform flow of the casting during the filling process, reduce defects such as gas traps and cold shuts, and thus improve the quality and reliability of the product. The optimized mold opening direction improves the manufacturability and production efficiency of the mold, and reduces production costs and scrap rates. As shown in Table 1, direction B can be selected as the optimal mold opening direction because direction B has excellent filling performance, wall thickness uniformity, and pinpoint / sharp edge avoidance, and the mold manufacturability is also good.

[0022] Table 1 In Table 1, filling performance evaluates the flow uniformity and gas trapping of the casting during the filling process. Wall thickness uniformity evaluates the uniformity of the distribution of the wall thickness of the casting. Mold manufacturability evaluates the difficulty and cost of mold manufacturing.

[0023] Step S102, creating overflow trough reference parameters, wherein the overflow trough reference parameters include length, water outlet height, water outlet width, trough bottom slope, water inlet method, depth and angle.

[0024] As shown in Table 2, determine the appropriate overflow tank length based on the mold size and filling requirements. Set the water outlet height based on the height of the die casting and the filling speed to ensure sufficient liquid outflow. Set the appropriate water outlet width based on the mold design and liquid flow rate. Design the bottom slope of the tank to promote liquid flow and reduce residue. Select direct or indirect water inlet based on the filling requirements of the die casting and the mold design. Determine the appropriate depth based on the height of the die casting and the liquid pressure. Angles include the bottom angle of the tank and the outlet angle to optimize liquid flow and reduce gas traps.

[0025] Table 2 Step S103, defining a parting surface of the die-casting mold by the edge of the target die-casting part, and determining an attachment position of the overflow groove on the parting surface according to an edge shape of the die-casting part.

[0026] The parting surface of the die-casting mold is a contact surface that divides a part of the mold that directly molds the plastic part into several parts in order to remove the molded plastic part from the mold cavity or to meet the molding needs such as placing inserts and exhausting according to the structure of the plastic part. The position of the parting surface should be selected at the maximum outer contour in the demolding direction of the casting, that is, at the maximum 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 casting and the requirements of the filling performance. By defining the parting surface according to the edge shape of the target die casting and determining the attachment position of the overflow groove on the parting surface, the uniform flow of the molten metal in the mold is ensured, and defects such as gas traps and cold shuts are reduced, thereby improving the filling performance.

[0027] Step S104, recording casting quality and filling performance under different overflow trough reference parameter combinations according to the Latin hypercube design sampling method.

[0028] Specifically, set the number of samples N, that is, the number of different parameter combinations you want to test. For each parameter, divide its value range evenly into N intervals. Randomly select a value in each interval of each parameter, but make sure that the N values ​​of each parameter are evenly distributed over the entire value range. Combine the randomly selected values ​​of each parameter to form different parameter combinations (representing different overflow slot designs). Manufacture castings with different overflow slot designs based on the generated parameter combinations and record their quality and filling performance, which can be done through casting experiments or simulation software.

[0029] 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 the same threshold is reached, reducing the experimental cost. By reducing the correlation between input variables, Latin hypercube sampling can improve the accuracy of Monte Carlo simulation, thereby more accurately evaluating the casting quality and filling performance. The casting quality and filling performance data under different overflow tank benchmark parameter combinations are shown in Table 3.

[0030] Table 3 Step S105, constructing an RSM response surface model according to the overflow trough reference parameters and the corresponding casting quality and filling performance, and solving to obtain the optimal overflow trough reference parameter combination; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curve.

[0031] In this embodiment, trapezoidal fuzzy numbers are used to represent the uncertainty in casting quality and filling performance data. Trapezoidal fuzzy numbers are defined by four parameters: lower bound, lower membership, upper membership and upper bound. Box-Cox transformation is used to stabilize the variance and make the data close to 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 a multivariate regression method (such as polynomial regression) (taking the transformed casting quality and filling performance data as the response variable and the overflow tank reference parameter as the independent variable). The individual conditional expectation curve describes the expected value of the response variable under the given independent variable conditions. In the RSM model, the individual conditional expectation curve is used to more clearly describe the influence of different parameter combinations on casting quality and filling performance.

[0032] The casting quality and filling performance data under different overflow trough benchmark 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.

[0033] Table 4 In Table 4, “casting quality (transformed)” and “filling performance (transformed)” 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 ) represents the lower bound, lower membership, upper membership and upper bound.

[0034] In this embodiment, the RSM response surface model is specifically: in, , , , For the trapezoidal fuzzy number The lower limit of the level, , , , For the trapezoidal fuzzy number The upper limit of the level, and is any overflow tank reference parameter, and k is the total number of overflow tank reference parameters.

[0035] Trapezoidal fuzzy numbers allow for the uncertainty of data to be considered in the modeling process. In the casting process, due to the influence of various factors (such as material properties, process conditions, etc.), the casting quality and filling performance 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.) depends on the trapezoidal fuzzy number By adjusting The value of can simulate different levels of uncertainty. Under the constraint of trapezoidal fuzzy numbers, the optimization algorithm is used to solve the optimal overflow tank benchmark parameter combination, which not only considers the optimal value of the parameter, but also the uncertainty of the parameter value.

[0036] For example, for the two overflow trough reference parameters including length and water outlet height. There is uncertainty due to measurement errors and other factors. In order to construct the RSM model and take uncertainty into account, trapezoidal fuzzy numbers are introduced to represent the casting quality and filling performance. Specifically, trapezoidal fuzzy numbers are defined for casting quality and filling performance, respectively, where the lower bound, lower membership, upper membership and upper bound represent the uncertainty range of the data, respectively. Then, the above-mentioned RSM model form is used for modeling. By fitting the model and solving the optimal parameter combination, a set of optimal overflow trough reference parameter values ​​is found, which can maximize the casting quality and filling performance while considering the uncertainty of the data, thereby improving the quality stability of the casting.

[0037] Among them, the trapezoidal fuzzy number The lower bound of is: The trapezoidal fuzzy number The upper bound of is: Among them, a and d are the lower and upper bounds 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.

[0038] In this embodiment, the Box-Cox transformation formula is: in, , is the original response variable, is the transformed response variable, is the Box-Cox parameter, is a preset constant.

[0039] In this embodiment, the function of the individual conditional expectation curve is: in, is the parameter vector.

[0040] Individual Conditional Expectation Curve (ICE) is used to show the relationship between casting quality or filling performance and explanatory variables such as overflow trough benchmark parameters. In fuzzy number or uncertainty models, ICE curves are used to better understand how the response variable changes with the change of a specific variable given the other variables. As shown in Table 5, it contains casting quality (as the response variable) and two explanatory variables: overflow trough length (x1) and water outlet height (x2). To generate an ICE curve, select a specific explanatory variable (such as x1 or x2), then fix the value of the other variable (such as the median or mean of x2), and then plot the relationship between the response variable (casting quality) and the selected explanatory variable. For example, select a fixed x2 value (such as the median or mean 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.

[0041] Table 5 In an optional manner, the step of solving for an optimal overflow tank reference parameter combination further includes: The overflow tank reference parameters and the corresponding casting quality and filling performance are used as response variables; If the response variable does not satisfy the normal relationship, it is processed using Box-Cox transformation to reduce the influence of skewness and kurtosis; If there is uncertainty in the response variable, a trapezoidal fuzzy number is introduced into the RSM response surface model to represent the uncertainty of the response; Estimating the relationship between the response variable and the overflow tank benchmark parameter by the RSM response surface model; The expected response of the overflow tank reference parameter corresponding to different combinations of the response variables is obtained through the individual condition expectation curve. Specifically, an individual condition expectation curve ICE is constructed for each overflow tank reference parameter; based on the individual condition expectation curve ICE, the expected response values ​​of casting quality and filling performance under different parameter combinations are calculated.

[0042] Step S106, determining the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

[0043] In this embodiment, the relative position of the overflow groove in the die casting mold is determined according to the position, shape and size of the parting surface, and the predetermined attachment position of the overflow groove in the die casting mold is determined. The position, shape and predetermined attachment position of the parting surface are marked. According to the optimal overflow groove reference parameter combination, the shape and size of the overflow groove are determined to ensure that it is coordinated with the parting surface and the attachment position.

[0044] Specifically, to ensure the effectiveness of the die-casting mold and the quality of the casting, the relative position of the overflow groove in the die-casting mold is determined according to the position, shape and size of the parting surface. The specific position, shape and predetermined attachment position of the parting surface are marked. The shape and size of the overflow groove are determined using the optimal combination of overflow groove reference parameters. When determining the shape and size of the overflow groove, special attention should be paid to the coordination with the parting surface and the attachment position. The overflow groove should be able to smoothly guide the excess metal liquid to flow out of 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 shrinkage and deformation of the casting to ensure the quality and dimensional accuracy of the final casting. Finally, the overflow groove is designed and manufactured in the die-casting mold according to the determined parameters and position.

[0045] As shown in Tables 6 to 8, examples are given of how to determine the position and size of the overflow groove in the die casting mold based on the position and characteristics of the parting surface.

[0046] Table 6: Parting surface and attachment position information Table 7: Optimal overflow tank benchmark parameter combinations Table 8: Overflow tank design parameters According to the information in Tables 6 and 7, the specific overflow groove design parameters are given in Table 8, which take into account the position, shape and size of the parting surface, as well as the optimal overflow groove reference parameter combination to ensure that the overflow groove is coordinated with the parting surface and the attachment position.

[0047] According to the solution provided by the present invention, the optimal die opening direction of the die-casting mold is determined according to the edge shape, wall thickness and strength, and avoidance of needle tips and sharp edges of the target die-casting; overflow groove reference parameters are created, and the overflow groove reference parameters include length, water outlet height, water outlet width, groove bottom slope, water inlet method, depth and angle; the parting surface of the die-casting mold is defined by the edge of the target die-casting, and the attachment position of the overflow groove on the parting surface is determined according to the edge shape of the die-casting; the casting quality and filling performance under different overflow groove reference parameter combinations are recorded according to the Latin hypercube design sampling method; an RSM response surface model is constructed according to the overflow groove reference parameters and the corresponding casting quality and filling performance, and the optimal overflow groove reference parameter combination is solved; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curves; the overflow groove is determined according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position. The present invention can accurately determine the optimal parameter combination of overflow troughs such as length, water outlet height, and water outlet width in a die-casting mold through parametric modeling, thereby improving the quality and filling performance of castings. Trapezoidal fuzzy numbers are introduced into the RSM response surface model to effectively handle the uncertainty of response variables, so that the model is closer to the actual production environment. At the same time, the Box-Cox transformation reduces the skewness and peak state effects of the data and improves the accuracy of the model. Through the individual conditional expectation curve, the expected response of the overflow trough reference parameters corresponding to different response variable combinations is more accurately estimated, providing more accurate guidance for actual production. Since the 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 error, and improves production efficiency and product quality. It is not only suitable for 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. It is easy to implement and integrate into the existing production process without adding too much additional cost and workload.

[0048] Figure 2 The schematic diagram of the structure of the device for parameterized modeling of overflow groove of die casting mold based on RSM in an embodiment of the present invention is shown. The device for parameterized modeling of overflow groove of 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.

[0049] The mold opening direction determination module 210 is used 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; The reference parameter creation module 220 is used to create overflow tank reference parameters, and the overflow tank reference parameters include length, water outlet height, water outlet width, tank bottom slope, water inlet method, depth and angle; The attachment position determination module 230 is used 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; The data sampling module 240 is used to record the casting quality and filling performance under different overflow tank reference parameter combinations according to the Latin hypercube design sampling method; The parameter solving module 250 is used to construct an RSM response surface model according to 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 curve; The overflow groove determination module 260 is used to determine the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

[0050] In an optional manner, the RSM response surface model is specifically: in, , , , For the trapezoidal fuzzy number The lower limit of the level, , , , For the trapezoidal fuzzy number The upper limit of the level, and is any overflow tank reference parameter, and k is the total number of overflow tank reference parameters.

[0051] In an optional manner, the step of solving for an optimal overflow tank reference parameter combination further includes: The overflow tank reference parameters and the corresponding casting quality and filling performance are used as response variables; If the response variable does not satisfy the normal relationship, it is processed using Box-Cox transformation to reduce the influence of skewness and kurtosis; If there is uncertainty in the response variable, a trapezoidal fuzzy number is introduced into the RSM response surface model to represent the uncertainty of the response; Estimating the relationship between the response variable and the overflow tank benchmark parameter by the RSM response surface model; The expected responses of the overflow tank reference parameters corresponding to different combinations of the response variables are obtained through the individual condition expected curve.

[0052] In an optional manner, the determining the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position further comprises: 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; Marking the position, shape and predetermined attachment position of the parting surface; According to the optimal overflow groove reference parameter combination, the shape and size of the overflow groove are determined to ensure that it is coordinated with the parting surface and the attachment position.

[0053] In an optional manner, obtaining the expected response of the overflow tank reference parameter corresponding to different combinations of the response variables through the individual condition expected curve further includes: Construct individual conditional expectation curve ICE for each overflow tank benchmark parameter; According to the individual condition expectation curve ICE, the expected response values ​​of casting quality and filling performance under different parameter combinations are calculated.

[0054] In an optional manner, the method further includes: The lower bound and the upper bound of the trapezoidal fuzzy number are determined according to the lower membership function inflection point and the upper membership function inflection point, respectively.

[0055] In an optional manner, the formula for the Box-Cox transformation is: in, , is the original response variable, is the transformed response variable, is the Box-Cox parameter, is a preset constant.

[0056] Figure 3 The schematic diagram of the structure of the computing device embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0057] like Figure 3 As shown, the computing device may include: a processor (processor) 302 , a communication interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

[0058] The processor 302, the communication interface 304, and the memory 306 communicate with each other via the communication bus 308. The communication interface 304 is used to communicate with other devices such as a client or other server network elements. The processor 302 is used to execute the program 310, which can specifically execute the relevant steps in the above-mentioned embodiment of the RSM-based parameterized modeling method for the overflow tank of the die casting mold.

[0059] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0060] The processor 302 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0061] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0062] According to the solution provided by the present invention, the optimal die opening direction of the die-casting mold is determined according to the edge shape, wall thickness and strength, needle tip and sharp edge of the target die-casting; the overflow groove reference parameters are created, and the overflow groove reference parameters include length, water outlet height, water outlet width, groove bottom slope, water inlet method, depth and angle; the parting surface of the die-casting mold is defined by the edge of the target die-casting, and the attachment position of the overflow groove on the parting surface is determined according to the edge shape of the die-casting; the casting quality and filling performance under different overflow groove reference parameter combinations are recorded according to the Latin hypercube design sampling method; an RSM response surface model is constructed according to the overflow groove reference parameters and the corresponding casting quality and filling performance, and the optimal overflow groove reference parameter combination is solved; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curves; the overflow groove is determined according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

[0063] The present invention can accurately determine the optimal parameter combination of overflow troughs such as length, water outlet height, and water outlet width in a die-casting mold through parametric modeling, thereby improving the quality and filling performance of castings. Trapezoidal fuzzy numbers are introduced into the RSM response surface model to effectively handle the uncertainty of response variables, so that the model is closer to the actual production environment. At the same time, the Box-Cox transformation reduces the skewness and peak state effects of the data and improves the accuracy of the model. Through the individual conditional expectation curve, the expected response of the overflow trough reference parameters corresponding to different response variable combinations is more accurately estimated, providing more accurate guidance for actual production. Since the 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 error, improves production efficiency and product quality, is not only suitable for specific die-casting molds and castings, but also can adapt to different production environments and product requirements by adjusting parameters and models, has strong adaptability and scalability, is easy to implement and integrate into the existing production process, and does not increase excessive additional costs and workload.

[0064] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose. In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and to form different embodiments. For example, in the following claims, any one of the claimed embodiments may be used in any combination. The present invention may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The steps in the above embodiments should not be understood as limiting the execution order unless otherwise specified.

Claims

1. A parametric modeling method for overflow groove of die casting mold based on RSM, characterized in that: include: 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; 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; Defining a parting surface of the die-casting mold by the edge of the target die-casting, and determining an attachment position of the overflow groove on the parting surface according to an edge shape of the die-casting; The casting quality and filling performance under different overflow tank benchmark parameter combinations were recorded according to the Latin hypercube design sampling method; According to the overflow trough reference parameters and the corresponding casting quality and filling performance, an RSM response surface model is constructed to solve and obtain the optimal overflow trough reference parameter combination; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curve; The overflow groove is determined according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

2. The RSM-based parametric modeling method for overflow groove of a die casting mold according to claim 1 is characterized in that: The RSM response surface model is specifically: in, , , , For the trapezoidal fuzzy number The lower limit of the level, , , , For the trapezoidal fuzzy number The upper limit of the level, and is any overflow tank reference parameter, and k is the total number of overflow tank reference parameters.

3. The RSM-based parametric modeling method for overflow groove of die casting mold according to claim 1 is characterized in that: The method of solving for the optimal overflow tank reference parameter combination further includes: The overflow tank reference parameters and the corresponding casting quality and filling performance are used as response variables; If the response variable does not satisfy the normal relationship, it is processed using Box-Cox transformation to reduce the influence of skewness and kurtosis; If there is uncertainty in the response variable, a trapezoidal fuzzy number is introduced into the RSM response surface model to represent the uncertainty of the response; Estimating the relationship between the response variable and the overflow tank benchmark parameter by the RSM response surface model; The expected responses of the overflow tank reference parameters corresponding to different combinations of the response variables are obtained through the individual condition expected curve.

4. The RSM-based parametric modeling method for overflow groove of a die casting mold according to claim 1 is characterized in that: The step of determining the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position further comprises: 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; Marking the position, shape and predetermined attachment position of the parting surface; According to the optimal overflow groove reference parameter combination, the shape and size of the overflow groove are determined to ensure that it is coordinated with the parting surface and the attachment position.

5. The RSM-based parametric modeling method for overflow groove of die casting mold according to claim 3 is characterized in that: The method of obtaining the expected response of the overflow tank reference parameter corresponding to different combinations of the response variables through the individual condition expected curve further includes: Construct individual conditional expectation curves ICE for each overflow tank benchmark parameter; According to the individual condition expectation curve ICE, the expected response values ​​of casting quality and filling performance under different parameter combinations are calculated.

6. The RSM-based parametric modeling method for overflow groove of a die casting mold according to claim 2 is characterized in that: The method further comprises: The lower bound and the upper bound of the trapezoidal fuzzy number are determined according to the inflection point of the lower membership function and the inflection point of the upper membership function, respectively.

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

8. A parametric modeling device for overflow groove of die casting mold based on RSM, characterized in that: The RSM-based parametric modeling method for the overflow groove of a die casting mold according to any one of claims 1 to 7 comprises: The mold opening direction determination module is used to determine the optimal mold opening direction of the die-casting mold according to the edge shape, wall thickness and strength of the target die-casting part, and the avoidance of needle points and sharp edges; A reference parameter creation module is used to 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; an attachment position determination module, used to define a parting surface of the die-casting mold by an edge of the target die-casting part, and determine an attachment position of the overflow groove on the parting surface according to an edge shape of the die-casting part; Data sampling module, used to record casting quality and filling performance under different overflow tank benchmark parameter combinations according to Latin hypercube design sampling method; A parameter solving module is used to construct an RSM response surface model according to the overflow trough reference parameters and the corresponding casting quality and filling performance, and solve to obtain the optimal overflow trough reference parameter combination; wherein the RSM response surface model introduces trapezoidal fuzzy numbers, Box-Cox transformation and individual conditional expectation curve; The overflow groove determination module is used to determine the overflow groove according to the optimal overflow groove reference parameter combination, the parting surface and the attachment position.

9. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the RSM-based parametric modeling method for overflow grooves of die-casting molds according to any one of claims 1-7.

10. A computer program product, characterized in that It includes at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the RSM-based parametric modeling method for the overflow groove of the die-casting mold as described in any one of claims 1-7.

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