Method for constructing machine-die-material-piece precision transfer model of medium plate fine blanking component

Abstract

The invention provides a method for constructing a machine-die-material-part precision transfer model of a medium plate fine blanking component, and belongs to the technical field of fine blanking machining. The method comprises the steps that a compliance matrix and a load vector of a target machine tool are obtained; performing multiplication calculation on the compliance matrix and the load vector to obtain first error information; a second error, a nominal clearance, a cutting edge geometric parameter and a cutting edge abrasion loss of the target mold are obtained; based on the first error information and the second error information, the effective gap and the dislocation amount delta of the target mold are determined; based on the effective gap, the dislocation amount and the material parameter information, the material springback amount of the target board is determined; the yield strength, the elastic modulus, the nominal thickness t and the thickness deviation of the target plate are obtained; according to a constructed material springback value prediction model s, beta0, beta1 and beta2 are model coefficients of the material springback value model and are effective gaps, and delta is a dislocation value; and according to the constructed multi-level precision transfer model, the weight coefficients calibrated for the dimensional deviation, gamma 1, gamma 2 and gamma 3 of the target fine blanking component are effective gaps. The method does not need numerical simulation, is small in calculation amount, can be applied online, and is suitable for multi-dimensional precision prediction and process parameter optimization of the medium-thickness plate fine blanking part.

Description

Technical Field

[0001] This invention relates to the field of precision stamping technology, specifically to a method for constructing a precision transfer model for medium-thick plate precision stamping components, encompassing the machine, die, material, and part. Background Technology

[0002] Precision blanking technology, as an important component of advanced manufacturing, is widely used in industries such as automotive, aerospace, and electronics. Taking precision blanking components of medium-thick plates as an example, these components (thickness range 4-25mm) play a crucial role in heavy equipment and ship structures due to their high structural strength and load-bearing capacity. However, during the precision blanking process of medium-thick plates, the final accuracy of the components is subject to the complex coupling of multiple factors, including machine tools, molds, and materials, increasing the difficulty of precision control.

[0003] In related technologies, there are two main methods for constructing the precision transfer model of medium and heavy plate precision stamping components: one is numerical simulation, such as finite element analysis, which simulates material deformation, stress distribution, and springback behavior during the stamping process by establishing a detailed physical model. Although numerical simulation can provide relatively accurate results, it has limitations such as high computational resource consumption, long analysis cycle, and high professional requirements for operators, making it difficult to meet the needs of rapid optimization and online monitoring in production sites. The other method is a statistical model based on empirical formulas, which derives the precision prediction formula through regression of a large amount of independent experimental data. However, this ignores the interaction of relevant performance data in the machine tool and die, resulting in insufficient accuracy in precision prediction.

[0004] Therefore, there is an urgent need in this field to provide a solution that can improve the speed and accuracy of precision prediction without numerical simulation, so as to support the precision prediction, process parameter optimization and quality control of medium and heavy plate fine stamping components, and meet the needs of modern manufacturing industry for high-efficiency and high-precision processing. Summary of the Invention

[0005] In view of this, it is necessary to provide a method for constructing a precision transfer model for medium and heavy plate fine stamping components, which can solve the technical problems of low speed and accuracy in predicting the precision of fine stamping components in the existing technology.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for constructing a precision transfer model for medium-thick plate precision stamping components, comprising: Based on the first error information of the target machine tool, the second error information of the target mold, and the material parameter information of the target sheet, a multi-level accuracy transfer model is constructed to predict the accuracy of the target fine blanking component. The target fine blanking component is a component that is fine blanked on the target machine tool using the target mold. The accuracy prediction results of the target fine-stamping component are obtained by analyzing the multi-level accuracy transfer model.

[0007] In one possible implementation, the construction of a multi-level accuracy transfer model for predicting the accuracy of the target fine-stamped component, based on the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target sheet metal, includes: The effective clearance and misalignment of the target mold are determined based on the first error information and the second error information. Based on the effective gap, the misalignment amount, and the material parameter information, the material springback amount of the target plate is determined; Based on the material springback, the material parameter information, the effective gap, and the misalignment, the multi-level precision transfer model is constructed.

[0008] In one possible implementation, the second error information includes the nominal clearance of the target mold, the cutting edge geometry parameters, and the cutting edge wear; determining the effective clearance and misalignment of the target mold based on the first error information and the second error information includes: Based on the first error information, the clearance error and translation angle error between the target machine tool and the target mold are determined; The effective clearance is determined based on the sum of the nominal clearance, the cutting edge wear, and the clearance error. The misalignment amount is determined based on the translation and rotation angle error.

[0009] In one possible implementation, the material parameter information includes yield strength. Elastic modulus The determination of the material springback of the target sheet material based on the effective gap, the misalignment, and the material parameter information includes: Based on the effective gap, the misalignment, and the material parameter information, a material rebound prediction model s is constructed according to the following analytical expression: Where β0, β1, and β2 are the model coefficients of the material rebound model. The effective gap is δ, and the misalignment is δ. The model coefficients were determined using the least squares method with ridge regularization. The material rebound amount is obtained based on the model coefficients.

[0010] In one possible implementation, the material parameter information also includes the thickness deviation of the target plate. The construction of the multi-level precision transfer model based on the material springback, the material parameter information, the effective gap, and the misalignment includes: The multi-level precision transfer model is constructed according to the following formula. : ; Let γ1, γ2, and γ3 be the dimensional deviations of the target fine-stamped component, where γ1, γ2, and γ3 are the weighting coefficients. This is the effective gap.

[0011] In one possible implementation, the step of analyzing the multi-level accuracy transfer model to obtain the accuracy prediction result of the target fine-stamping component includes: Reference dimensions constructed based on the target fine blanking The following linear regression formula is analyzed to obtain the accuracy prediction results: ; This is the accuracy prediction result for the target fine-stamped component.

[0012] In one possible implementation, the step of determining the first error information includes: Obtain the compliance matrix and load vector of the target machine tool; The first error information is obtained by multiplying the compliance matrix and the load vector.

[0013] Secondly, the present invention also provides a model construction device for the precision transfer of medium-thick plate fine stamping components from machine to die to material to part, comprising: A construction unit is used to construct a multi-level accuracy transfer model for predicting the accuracy of a target fine-blanking component based on the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target sheet. The target fine-blanking component is a component that is fine-blanked on the target machine tool using the target die. The analysis unit is used to analyze the multi-level accuracy transfer model to obtain the accuracy prediction result of the target fine stamping component.

[0014] Thirdly, the present invention also provides a model construction device for the precision transfer of medium-thick plate fine stamping components from machine to die to material to part, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for constructing a precision transfer model of medium-thick plate fine stamping components machine-drill-material-part as described in any of the above implementations.

[0015] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the method for constructing a precision transfer model of a medium-thick plate fine stamping component machine-mold-material-part as described in any of the above implementations.

[0016] The beneficial effects of this invention are: The present invention provides a method for constructing a machine-die-material-part accuracy transfer model for medium-thick plate fine blanking components. Based on the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target sheet, a multi-level accuracy transfer model is constructed to predict the accuracy of the target fine blanking component. The target fine blanking component is a component formed by fine blanking the target sheet using the target die on the target machine tool. This achieves dynamic coupling and interaction of the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target sheet, ensuring the completeness and reliability of the multi-level accuracy prediction model. The multi-level accuracy transfer model is analyzed to obtain the accuracy prediction result of the target fine blanking component. Since the multi-level accuracy transfer model is based on analytical formulas forming a closed calculation chain, complex numerical simulation calculations are avoided through analytical methods, accelerating the accuracy prediction. Furthermore, the multi-level accuracy transfer model is constructed based on physical mechanisms, giving the model's prediction results clear physical meaning and improving the stability and accuracy of the accuracy prediction for fine blanking components. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of an embodiment of the method for constructing a precision transfer model for medium-thick plate precision stamping components provided by the present invention; Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of S101; Figure 3 For the present invention Figure 2 A schematic diagram of an embodiment of S201; Figure 4 This is a schematic flowchart of another embodiment of the method for constructing a precision transfer model for medium-thick plate precision stamping components based on the present invention; Figure 5A schematic diagram of the structure of the precision transfer model building device for medium and heavy plate fine stamping components provided by the present invention; Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides a method, apparatus, electronic device, and storage medium for constructing a precision transfer model of medium-thick plate fine stamping components from machine to die to material to part, which will be described below.

[0024] The execution subject of the method for constructing the precision transfer model of the heavy plate fine stamping component machine-mold-material-part in this application embodiment can be the medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction device provided in this application embodiment, or a server device, physical host, or user equipment (UE) integrating the medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction device, etc. The medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction device can be implemented in hardware or software. The UE can be a smartphone, tablet computer, laptop computer, handheld computer, desktop computer, or personal digital assistant (PDA) and other terminal devices. This application takes the medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction device as the execution subject for illustration.

[0025] Figure 1 This is a schematic flowchart of an embodiment of the method for constructing a precision transfer model for medium-thick plate precision stamping components based on the present invention, as shown below. Figure 1 As shown, the method for constructing the precision transfer model of medium and heavy plate fine stamping components from machine to die to material to part includes: S101. Based on the first error information of the target machine tool, the second error information of the target mold, and the material parameter information of the target sheet, a multi-level accuracy transfer model is constructed to predict the accuracy of the target fine blanking component. The target fine blanking component is a component that is fine blanked on the target machine tool using the target mold.

[0026] In this embodiment, the fine-stamping component can be a fine-stamping component of a medium-thick plate.

[0027] The multi-level precision transfer model refers to the precision transfer model from machine tool to die to sheet metal to component. It is a multi-level coupled mathematical model that quantifies the transfer of precision from machine tool to fine blanking component. This model integrates the key parameters of machine tool, die, sheet metal and component, and characterizes their interaction during the processing to achieve precision prediction of fine blanking component.

[0028] The precision parameters of the four key components in a fine blanking system—machine tool, die, sheet metal, and component—are defined as follows: Machine tool accuracy refers to the positional accuracy of a machine tool under load, including translational error and rotational error, reflecting the static compliance characteristics and dynamic stiffness characteristics of the machine tool-mold frame system; Mold precision: refers to the geometric precision and wear condition of the mold; Sheet material precision: refers to the dimensional accuracy and material uniformity of the sheet material; Component accuracy: refers to the machining accuracy of the final stamped component, including dimensional accuracy, shape accuracy, positional accuracy, and cross-sectional accuracy.

[0029] The first error information refers to the static and dynamic stiffness characteristics between the target machine tool and the target mold, and the error information generated under machining loads, such as pose error. The second error information refers to the error information of mold structure deformation.

[0030] Specifically, based on the first and second error information, the process state information of the target mold can be determined through coupled calculation. This process state information is then integrated with material parameter information to establish a material response characteristic model, such as a material springback model. Based on this material response characteristic model, a multi-layered accuracy transfer model is constructed, encompassing the machine tool, mold, sheet metal, and component. In this embodiment, the construction process integrates machine tool system errors, mold state errors, and material characteristic parameters through an accuracy transfer path, forming a complete mapping relationship from processing conditions to component accuracy. This achieves dynamic coupling and interaction between the first error information of the target machine tool, the second error information of the target mold, and the material parameter information of the target sheet metal, ensuring the completeness and reliability of the multi-layered accuracy prediction model.

[0031] S102. Analyze the multi-level accuracy transfer model to obtain the accuracy prediction result of the target fine stamping component.

[0032] Among them, the accuracy prediction result can be the dimensional accuracy information of the target fine blanking construction.

[0033] Specifically, the multi-level precision transfer model is analyzed to obtain the precision prediction results of the target fine-stamped component. Since the multi-level precision transfer model is based on a closed calculation chain formed by analytical formulas, it avoids complex numerical simulation calculations and accelerates the precision prediction accuracy. Furthermore, the multi-level precision transfer model is built based on physical mechanisms, which gives the model's prediction results clear physical meaning and improves the stability and accuracy of precision prediction for fine-stamped components.

[0034] In summary, the method for constructing a machine-die-material-part accuracy transfer model for medium-thick plate fine blanking components provided in this embodiment of the invention constructs a multi-level accuracy transfer model for predicting the accuracy of the target fine blanking component based on the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target plate. The target fine blanking component is a component that undergoes fine blanking processing on the target plate using the target die on the target machine tool. This achieves dynamic coupling and interaction of the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target plate, ensuring the completeness and reliability of the multi-level accuracy prediction model. Analyzing the multi-level accuracy transfer model yields the accuracy prediction result of the target fine blanking component. Since the multi-level accuracy transfer model is based on analytical formulas forming a closed calculation chain, the analytical method avoids complex numerical simulation calculations, accelerates the accuracy prediction, and, because the multi-level accuracy transfer model is constructed based on physical mechanisms, the prediction results have clear physical meaning, improving the stability and accuracy of the accuracy prediction for fine blanking components.

[0035] In some embodiments of the present invention, such as Figure 2 As shown, step S102 includes: S201. Determine the effective clearance and misalignment of the target mold based on the first error information and the second error information; S202. Based on the effective gap, the misalignment amount, and the material parameter information, determine the material springback amount of the target plate. S203. Based on the material springback, the material parameter information, the effective gap, and the misalignment, construct the multi-level precision transfer model.

[0036] Specifically, the first error information reflecting the accuracy of the target machine tool is coupled with the second error information reflecting the state of the target die. Through a preset geometric mapping relationship, the effective clearance that actually takes effect during the punching process and the amount of die misalignment caused by systematic errors are determined. Then, the effective clearance, misalignment, and material parameter information reflecting the characteristics of the target sheet are integrated. Through a material mechanical response model, the amount of material springback caused by the elastic energy accumulated during the punching process after the load is released is predicted. Then, with the amount of material springback as the output variable, the material parameter information, effective clearance, and misalignment and other key process state parameters are integrated to establish a complete mathematical relationship that can describe the transmission of accuracy from the machine tool system, die state, and material characteristics to the final component. This completes the construction of a multi-level accuracy transmission model, realizes the systematic modeling of the error transmission path, and improves the accuracy prediction efficiency and reliability accuracy of the multi-level accuracy transmission model by considering the coupling effect of multiple factors.

[0037] In some embodiments of the present invention, the step of determining the first error information includes: obtaining the compliance matrix and load vector of the target machine tool; multiplying the compliance matrix and the load vector to obtain the first error information.

[0038] Specifically, the static accuracy characteristics and dynamic stiffness characteristics of the machine tool and mold frame can be obtained, and the positional error of the machine tool-mold under load can be established, i.e., the first error information.

[0039] More specifically, the six-degree-of-freedom compliance matrix of the machine tool and die holder can be obtained through experimental calibration. The external load vector can be correlated with the six-degree-of-freedom pose error of the punch-die to form the pose error, the first error information. , where, Δx, Δy, Δz, Δθ x ,Δθ y , Δθ z The physical meanings of each are as follows: translation error of the punch in the X-axis direction in the horizontal plane, translation error of the punch in the Y-axis direction in the horizontal plane, translation error of the punch in the Z-axis direction in the horizontal plane, tilting angle error of the punch around the X-axis, tilting angle error of the punch around the Y-axis, and torsional angle error of the punch around the Z-axis.

[0040]

[0041] Wherein, C is a 6×6 compliance matrix, obtained by installing displacement sensors and inclinometers on the machine tool table, applying simulated punching loads, measuring pose changes under different load levels, and calibrating using the least squares method. The load vector includes the punching force component F. x , F y , F z and bending moment component M x M y M z .

[0042] This first error information characterizes the linear mapping relationship between the load vector and the orientation error, providing machine tool error input for subsequent accuracy transfer analysis.

[0043] In some embodiments of the present invention, the second error information includes the nominal clearance of the target mold, the cutting edge geometry parameters, and the cutting edge wear amount; such as Figure 3 As shown, step S201 includes: S301. Determine the clearance error and translation angle error between the target machine tool and the target mold based on the first error information; S302. Determine the effective clearance based on the sum of the nominal clearance, the cutting edge wear, and the clearance error; S303. Determine the misalignment amount based on the translation and rotation angle error.

[0044] Specifically, the gap error can be calculated using the following formula. :

[0045] Where a, b, c, and d are geometric coefficients.

[0046] The translation and rotation angle error can be calculated using the following formula. :

[0047] Where H is the effective height from the center of the corner to the working surface of the cutting edge.

[0048] nominal gap Cutting edge wear and gap error The sum of the values ​​is determined as the effective gap. ,Right now The calculation formula is as follows:

[0049] The translation angle error is taken as the misalignment amount. ,Right now .

[0050] Understandably, by determining the effective clearance based on the sum of the nominal clearance, the amount of edge wear, and the clearance error, and by determining the misalignment based on the translational angle error, the precise quantification of error transmission is achieved.

[0051] In some embodiments of the present invention, step S202 includes: constructing a material rebound prediction model s according to the following analytical expression based on the effective gap, the misalignment amount, and the material parameter information: Where β0, β1, and β2 are the model coefficients of the material rebound model. The effective gap is determined by the least squares method with ridge regularization, where δ is the misalignment. The material rebound is obtained based on the model coefficients.

[0052] Specifically, the model coefficients are determined using the least squares method with ridge regularization. These coefficients are then substituted into the material springback prediction model to obtain the material springback s. Understandably, determining the model coefficients using the least squares method with ridge regularization enables the material springback prediction model to stably determine its parameters based on limited production data. This effectively overcomes the dependence of traditional analytical models on ideal conditions and significantly enhances the model's applicability and robustness in actual production environments.

[0053] In one specific implementation, the rebound model coefficients are based on field data. Least squares identification was performed, and the model was used for process parameter optimization and component accuracy prediction. Model parameters were obtained through ridge regression least squares identification to ensure stability under limited experimental data. The objective function is:

[0054] in The parameter vector to be identified, To obtain the optimal estimate of the identified parameters. The characteristic matrix of the model error, This represents the actual error. This is the ridge coefficient, used to suppress overfitting.

[0055] In some embodiments of the invention, the material parameter information also includes the thickness deviation of the target plate. Step S202 includes: constructing the multi-level precision transfer model according to the following formula. : ; Let γ1, γ2, and γ3 be the dimensional deviations of the target fine-stamped component, where γ1, γ2, and γ3 are the weighting coefficients. This is the effective gap.

[0056] Specifically, the multi-level accuracy transfer model D = γ1ceff + γ2δ + γ3Δt - s integrates machine tool error, mold error, material geometric error (Δt), and material mechanical response (s) into a linear and analytical framework. This structure not only ensures computational efficiency but also makes the impact of each error source on the final accuracy quantifiable and traceable, providing a clear direction for process optimization.

[0057] In some embodiments of the present invention, step S102 includes: constructing a reference dimension based on the target fine blanking. The following linear regression formula is analyzed to obtain the accuracy prediction results: ; This is the accuracy prediction result for the target fine-stamped component.

[0058] Among them, the accuracy prediction result is the predicted size of the fine-stamped component.

[0059] Specifically, This achieves high efficiency and decoupling in the prediction process.

[0060] In one specific implementation, based on the actual fine stamping process parameters and material properties of medium and heavy plates, AH36 marine steel plate is selected as the example material. This material is widely used in ships and marine structural components and has good strength and toughness.

[0061] A precision transfer model was constructed and predicted for 10 mm thick AH36 steel plate. AH36 steel plate is a high-strength marine structural steel suitable for medium-thick plate fine stamping processes (thickness range 4-25 mm). Its material properties include yield strength. elastic modulus Thickness deviation (Typical rolling tolerances). A precision hydraulic press is selected as the machine tool, with a rated punching load of... nominal clearance of the mold frame Edge wear .

[0062] First, the accuracy characteristics of the machine tool and die set are obtained to establish a description of the pose error of the machine tool-die set system under load. A six-degree-of-freedom compliance matrix is ​​obtained through laboratory testing and calibration. This matrix maps the external load vector (including punching force, bending moment, etc.) to the pose error of the punch-die system. This includes installing displacement sensors and inclinometers on the machine tool table, applying simulated punching loads, and measuring pose changes. The pose error under typical loads is assumed to be: translational error. (x direction) (y-direction), rotational error (around the x-axis) (Around the y-axis). These values ​​conform to the error range of precision machine tools, and the compliance matrix form is a linear mapping relationship, ensuring the accuracy of pose error prediction.

[0063] Subsequently, the yield strength, thickness deviation, and elastoplastic parameters of the medium-thick plate material were obtained. In addition to the above... and Based on machine tool errors, mold errors, and material parameters, a multi-level precision transfer relationship is constructed between machine, mold, material, and part. First, positional errors are coupled with cutting edge clearance and cutting edge wear to form the effective clearance and cutting edge deformation. Second, material elastic-plastic parameters and thickness fluctuations are introduced to calculate springback. The predicted results of the component's dimensional deviation, flatness, and perpendicularity are obtained through analytical model calculation. The model parameters were identified using least-squares analysis based on field data, and the model was then used for process parameter optimization and component accuracy prediction. Ten sets of field data were collected, including actual measured dimensional deviations, flatness, and perpendicularity. The feature matrix X includes... Ridge regression (ridge coefficient) was used. To suppress overfitting under limited data, the objective function is solved to obtain the updated coefficients. The mean squared error of the identified model is <0.005 mm. This embodiment verifies the computational efficiency of the model, with a single prediction taking less than 1 second, making it suitable for online monitoring systems.

[0064] In one specific implementation, such as Figure 5 The diagram shown is a flowchart of another embodiment of the method for constructing a precision transfer model for medium-thick plate precision stamping components based on the present invention, specifically as follows: S401. Obtain the compliance matrix and load vector of the target machine tool; multiply the compliance matrix and load vector to obtain the first error information, and obtain the second error of the target mold; S402. Determine the effective clearance and misalignment δ of the target mold based on the first error information and the second error information; S403. Based on the effective gap, misalignment amount and material parameter information, determine the material springback amount of the target sheet; S404, Obtain the yield strength of the target plate. Elastic modulus Nominal thickness t and thickness deviation ;according to A material springback prediction model s is constructed, where β0, β1, and β2 are the model coefficients of the material springback model. The effective gap is δ, and the misalignment is δ. S405, according to Constructing a multi-level precision transfer model , The weighting coefficients γ1, γ2, and γ3 are used to determine the dimensional deviations of the target fine-stamped component. For effective clearance, this method requires no numerical simulation, has low computational load, and can be applied online, making it suitable for multi-dimensional accuracy prediction and process parameter optimization of medium-thick plate precision blanking parts. To better implement the machine-die-material-part accuracy transfer model construction method for medium-thick plate precision blanking components in this embodiment of the invention, based on the machine-die-material-part accuracy transfer model construction method for medium-thick plate precision blanking components, the corresponding method is as follows: Figure 5 As shown, this embodiment of the invention also provides a precision transfer model construction device for medium and heavy plate fine stamping components (machine-die-material-part). The medium and heavy plate fine stamping component machine-die-material-part precision transfer model construction device 500 includes: The construction unit 501 is used to construct a multi-level accuracy transfer model for predicting the accuracy of a target fine blanking component based on the first error information of the target machine tool, the second error information of the target mold, and the material parameter information of the target sheet. The target fine blanking component is a component that is fine blanked on the target machine tool using the target mold. The analysis unit 502 is used to analyze the multi-level accuracy transfer model to obtain the accuracy prediction result of the target fine stamping component.

[0065] The medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction device 500 provided in the above embodiments can realize the technical solutions described in the above embodiments of the medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction method. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction method, which will not be repeated here.

[0066] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602, and a display 606. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0067] In some embodiments, processor 601 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 602 or process data, such as the method for constructing a precision transfer model of medium and heavy plate fine stamping components machine-mold-material-part in this invention.

[0068] In some embodiments, processor 601 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 601 may be local or remote. In some embodiments, processor 601 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, intranet, multi-cloud, etc., or any combination thereof.

[0069] In some embodiments, memory 602 may be an internal storage unit of electronic device 600, such as a hard disk or memory of electronic device 600. In other embodiments, memory 602 may also be an external storage device of electronic device 600, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 600.

[0070] Furthermore, the memory 602 may include both internal storage units of the electronic device 600 and external storage devices. The memory 602 is used to store application software and various types of data installed on the electronic device 600.

[0071] In some embodiments, display 606 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 606 is used to display information from electronic device 600 and to display a visual user interface. Components 601-606 of electronic device 600 communicate with each other via a system bus.

[0072] In one embodiment, when the processor 601 executes the model construction program for the precision transfer of medium-thick plate fine stamping components from the memory 602, the following steps can be implemented: Based on the first error information of the target machine tool, the second error information of the target mold, and the material parameter information of the target sheet, a multi-level accuracy transfer model is constructed to predict the accuracy of the target fine blanking component. The target fine blanking component is a component that is fine blanked on the target machine tool using the target mold. The accuracy prediction results of the target fine-stamping component are obtained by analyzing the multi-level accuracy transfer model.

[0073] It should be understood that when the processor 601 executes the medium-thick plate fine stamping component machine-mold-material-part precision transfer model construction program in the memory 602, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0074] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 600 mentioned. Electronic device 600 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 600 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0075] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions in the method for constructing a precision transfer model of medium and heavy plate fine stamping components from machine to die to material to part provided in the above-described method embodiments.

[0076] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0077] The foregoing has provided a detailed description of the method, apparatus, equipment, and medium for constructing a precision transfer model for medium and heavy plate fine stamping components, including the machine-mold-material-part model. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing a precision transfer model for medium-thick plate precision stamping components, characterized in that, include: Based on the first error information of the target machine tool, the second error information of the target mold, and the material parameter information of the target sheet, a multi-level accuracy transfer model is constructed to predict the accuracy of the target fine blanking component. The target fine blanking component is a component that is fine blanked on the target machine tool using the target mold. The accuracy prediction results of the target fine-stamping component are obtained by analyzing the multi-level accuracy transfer model.

2. The method for constructing a precision transfer model for medium-thick plate precision stamping components according to claim 1, characterized in that, The multi-level accuracy transfer model for predicting the accuracy of target fine-stamped components is constructed based on the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target sheet metal. This model includes: The effective clearance and misalignment of the target mold are determined based on the first error information and the second error information. Based on the effective gap, the misalignment amount, and the material parameter information, the material springback amount of the target plate is determined; Based on the material springback, the material parameter information, the effective gap, and the misalignment, the multi-level precision transfer model is constructed.

3. The method for constructing a precision transfer model for medium-thick plate precision stamping components according to claim 2, characterized in that, The second error information includes the nominal clearance, cutting edge geometry parameters, and cutting edge wear of the target mold; determining the effective clearance and misalignment of the target mold based on the first and second error information includes: Based on the first error information, the clearance error and translation angle error between the target machine tool and the target mold are determined; The effective clearance is determined based on the sum of the nominal clearance, the cutting edge wear, and the clearance error. The misalignment amount is determined based on the translation and rotation angle error.

4. The method for constructing a precision transfer model for medium-thick plate precision stamping components according to claim 2, characterized in that, The material parameter information includes yield strength. Elastic modulus and nominal thickness t; Determining the material springback of the target sheet material based on the effective gap, the misalignment, and the material parameter information includes: Based on the effective gap, the misalignment, and the material parameter information, a material rebound prediction model s is constructed according to the following analytical expression: Where β0, β1, and β2 are the model coefficients of the material rebound model. The effective gap is δ, and the misalignment is δ. The model coefficients were determined using the least squares method with ridge regularization. The material rebound amount is obtained based on the model coefficients.

5. The method for constructing a precision transfer model for medium-thick plate precision stamping components according to claim 2, characterized in that, The material parameter information also includes the thickness deviation of the target plate. ; The construction of the multi-level precision transfer model based on the material springback, material parameter information, effective gap, and misalignment includes: The multi-level precision transfer model is constructed according to the following formula. : ； Let γ1, γ2, and γ3 be the dimensional deviations of the target fine-stamped component, where γ1, γ2, and γ3 are the weighting coefficients. This is the effective gap.

6. The method for constructing a precision transfer model for medium-thick plate precision stamping components according to claim 5, characterized in that, The step of analyzing the multi-level accuracy transfer model to obtain the accuracy prediction result of the target fine-stamping component includes: Reference dimensions constructed based on the target fine blanking The following linear regression formula is analyzed to obtain the accuracy prediction results: ； This is the accuracy prediction result for the target fine-stamped component.

7. The method for constructing a precision transfer model for medium-thick plate precision stamping components according to claim 3, characterized in that, The step of determining the first error information includes: Obtain the compliance matrix and load vector of the target machine tool; The first error information is obtained by multiplying the compliance matrix and the load vector.

8. A model construction device for transferring the precision of medium-thick plate precision stamping components from machine to die to material to part, characterized in that, include: A construction unit is used to construct a multi-level accuracy transfer model for predicting the accuracy of a target fine-blanking component based on the first error information of the target machine tool, the second error information of the target die, and the material parameter information of the target sheet. The target fine-blanking component is a component that is fine-blanked on the target machine tool using the target die. The analysis unit is used to analyze the multi-level accuracy transfer model to obtain the accuracy prediction result of the target fine stamping component.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for constructing a precision transfer model of medium-thick plate fine stamping components according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the method for constructing a precision transfer model of medium-thick plate fine stamping components according to any one of claims 1 to 7.

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