Analysis method for forging hammer impact-rebound coefficient and action time

Abstract

This invention discloses a method for analyzing the rebound coefficient and impact time of a forging hammer impact. The invention includes: obtaining the rebound coefficient and impact time based on measured data; analyzing the rebound coefficient and impact time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models; and selecting one of these models as the optimal model based on the measured data. This invention solves the problem of large errors in the impact time when analyzing the peak impact force in related technologies.

Description

Technical Field

[0001] This invention relates to the field of impact force analysis technology, and more particularly to a method for analyzing the rebound coefficient and impact time of a forging hammer. Background Technology

[0002] A forging hammer is a mechanical device used in metal processing, mainly applied to forging, compression, and forming processes. To improve production efficiency and product quality, it is necessary to select the appropriate type of forging hammer based on specific processing requirements and material properties. There are many types of forging hammers. According to their striking characteristics, there are counter-strike hammers and hammers with anvils; according to their processing applications, there are free forging hammers, die forging hammers, and sheet metal stamping hammers; and according to the force acting on the falling part during the downward stroke, there are single-acting hammers and double-acting hammers. In a single-acting hammer, the falling part is in free fall; in a double-acting hammer, the falling part is subjected to gravity as well as compressed air or hydraulic pressure during the downward stroke.

[0003] To accurately analyze the impact force of forging hammers, a quantitative analysis method for the impact characteristics of forging hammers based on pulse functions (CN120256808A) has been proposed. This method employs five pulse functions and two basic parameters (pulse peak and pulse width), combined with on-site impact test data, to establish a quantitative analysis method for the impact force of forging hammers. This provides a scientific and technical means to ensure the quality of forging equipment and control equipment vibration.

[0004] The peak impact force of this method can be expressed as follows: ,in, For the mass ratio, 1 / 20 can be taken.

[0005] The material properties that change when the motion state of a hammer-impacted system changes are defined and represented by the springback coefficient (or recovery coefficient) e (or K):

[0006]

[0007] The springback coefficient represents the ratio of the relative velocity of the hammering system after forging to the relative velocity before forging; here, it can be expressed as an elastic-plastic property of the forging. The e-value decreases with increasing forging temperature, and its value ranges from 0 to 1. It can be used to calculate the velocities of the hammer and anvil.

[0008] The springback coefficient indicates that during the impact process, while kinetic energy is converted into deformation energy or thermal energy, the remaining energy is converted into springback kinetic energy. Understanding the springback coefficient helps optimize forging process parameters, reduce energy waste, and improve forming quality.

[0009] To ensure the accuracy of the calculation of the forging hammer's striking force, it is necessary to have an accurate springback coefficient e and an effective time t0.

[0010] However, there is only some empirical data regarding the impact duration t0 of the forging hammer, which is roughly between 2ms and 20ms. If the designer lacks experience, they cannot accurately calculate the maximum impact force of the forging hammer, which will affect the determination of the vibration isolation frequency and the deviation of the vibration response. Different workpieces will have significantly different impact rebound coefficients and impact durations. If the values ​​are inaccurate, large deviations will occur, with a maximum difference of up to ten times.

[0011] In calculating the impact force of a forging hammer, the rebound coefficient *e* and the impact duration *t0* are two crucial parameters. These two parameters are related. If this relationship is not properly understood, it can lead to calculation errors.

[0012] The rebound coefficient e can be determined based on process conditions. Some recommended values ​​are found in standards and manuals, as shown in the table below.

[0013] Forging hammer form Forging Manual Dynamics Free forging 0.1~0.3 0.25 Die forging 0.5~0.8 0.56

[0014] However, there is only some empirical data on the duration t0 of the hammer strike, which is roughly between 2ms and 20ms.

[0015] The impact rebound coefficient and impact duration vary significantly when calculating the impact force of a forging hammer on different workpieces. Inaccurate values ​​can lead to substantial deviations, sometimes as large as tenfold. Without experimental data or practical experience, the formula for calculating forging hammer impact force cannot be used correctly. Therefore, we have established formulas for calculating the rebound coefficient *e* and impact duration, combining experimental data and the law of conservation of momentum. This ensures more effective and convenient use of the forging hammer impact force calculation formula and guarantees more accurate results.

[0016] If a mathematical formula is used to link the rebound coefficient e with the duration of the impact, resulting in a unique correspondence, the accuracy of the calculation will be greatly improved.

[0017] Main drawbacks of existing technology:

[0018] (1) Parameter isolation: There is no clear formula for the rebound coefficient e and the impact time t0, which leads to calculation deviation (up to ten times).

[0019] (2) Experience dependence: When there is a lack of theoretical models, designers have difficulty selecting parameters accurately, which affects the vibration isolation frequency and vibration control accuracy.

[0020] There is currently no effective solution to the aforementioned problems in the relevant technologies. Summary of the Invention

[0021] The main objective of this application is to provide an analytical method for the impact rebound coefficient and impact time of a forging hammer, so as to at least solve the technical problem of large error in the impact time when analyzing the peak impact force in related technologies.

[0022] To achieve the above objectives, according to one aspect of this application, a method for analyzing the springback coefficient and impact time of a forging hammer is provided. The method includes: obtaining the springback coefficient and impact time based on measured data; analyzing the springback coefficient and impact time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models; and selecting one of the quadratic polynomial models, exponential function models, and power function models as the optimal model based on the measured data.

[0023] Optionally, the quadratic polynomial can be transformed into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The constant of the second polynomial is used; the exponential function is transformed into... ,in, It is the first constant. The second constant is used; the power function is transformed into... .

[0024] Optionally, regression analysis can be performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

[0025] Optionally, regression analysis can be performed on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; regression analysis is performed on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0026] Optionally, based on the first constant and the second constant, the exponential function model and the power function model can be obtained.

[0027] Optionally, a suitable model can be selected from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0028] According to another aspect of this application, an analysis device for the springback coefficient and impact time of a forging hammer impact is provided. The device includes: an acquisition unit for acquiring the springback coefficient and impact time based on measured data; an analysis unit for analyzing the springback coefficient and impact time using a quadratic polynomial, an exponential function, and a power function to obtain a quadratic polynomial model, an exponential function model, and a power function model; and a selection unit for selecting one of the quadratic polynomial model, the exponential function model, and the power function model as the optimal model based on the measured data.

[0029] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes a method for analyzing the springback coefficient and action time of a forging hammer impact according to any one of the above claims.

[0030] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include an analysis method for the springback coefficient and action time of a forging hammer impact for performing any one of the following:

[0031] This application employs the following steps: obtaining the rebound coefficient and impact time based on measured data; analyzing the rebound coefficient and impact time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models; and selecting one of these models as the optimal model based on the measured data. This solves the problem of large errors in the impact time when analyzing the peak impact force in related technologies, thereby achieving accurate acquisition of the impact time and making the impact force analysis results more accurate. Attached Figure Description

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a flowchart of an analysis method for the springback coefficient and action time of a forging hammer impact, provided according to an embodiment of this application;

[0034] Figure 2 To fit the image;

[0035] Figure 3This is a structural block diagram of an analysis device for the springback coefficient and action time of a forging hammer impact, provided according to an embodiment of this application. Detailed Implementation

[0036] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0038] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0039] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0040] This embodiment provides a method for analyzing the rebound coefficient and action time of a forging hammer impact, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0041] Figure 1 This is a flowchart illustrating a method for analyzing the springback coefficient and impact time of a forging hammer according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0042] Step S101: Obtain the rebound coefficient and action time based on the measured data;

[0043] Specifically, based on the measured data of the forging hammer's impact force and the collection of relevant information, the rebound coefficient can be obtained. With action time Basic data, duration of action It refers to the duration of the attack.

[0044] Step S102: The rebound coefficient and action time are analyzed by quadratic polynomial, exponential function and power function to obtain quadratic polynomial model, exponential function model and power function model.

[0045] Specifically, based on known experimental data, the relationship between the rebound coefficient φ and the impact time... The relationship between the two functions was analyzed using least squares fitting with three mathematical function models: a quadratic polynomial model, an exponential function model, and a power function model. The fitting process was based on linear regression, using logarithmic transformation to convert the nonlinear model into a linear form.

[0046] Step S103: Select the optimal model from the quadratic polynomial model, exponential function model, and power function model based on the measured data.

[0047] Specifically, the optimal fitting model is determined by comparing the goodness of fit (such as the sum of squared residuals, coefficient of determination, etc.) of the three models. and The inverse relationship is a correct macroeconomic trend. We can reinforce this concept from the perspective of "stiffness" and "resistance": high (High elasticity), short (Short pulse width): This corresponds to a "high stiffness" system. The colliding bodies (forgings and hammer / anvil) deform very little, and the material rebounds rapidly like a spring. The force is large, but the duration of action is extremely short. Small impact energy (low e) results in high peak impact force. This typically occurs when the impact energy is insufficient, producing only elastic or minor plastic deformation, or when impacting very hard materials. High plasticity (low e), long... This corresponds to a "low stiffness" or "high damping" system. The forging undergoes significant plastic flow, acting like a "damper" to absorb energy. The force is relatively small, but it is maintained for a long time. (Large). This is the desired state for our forging process, meaning that energy is effectively used for forming. Therefore, the impact time... It can be seen as an indirect measure of the degree of "plasticity" in a process. The longer the length, the more complete the plastic forming. In practical applications, its applicable boundaries and consistency in extreme cases can be verified: when It conforms to perfectly elastic collision, ideally. When This conforms to a perfectly plastic impact. This is physically plausible, but for a forging hammer strike, It cannot be infinitely long; it has an upper limit determined by mass and system stiffness.

[0048] Optionally, the quadratic polynomial can be transformed into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The constant of the second polynomial is used; the exponential function is transformed into... ,in, It is the first constant. The second constant is used; the power function is transformed into... .

[0049] Optionally, regression analysis can be performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

[0050] Specifically, the quadratic polynomial has been transformed into The standard multinomial regression model is ,in: It is the dependent variable (the value to be predicted). It is the independent variable (original feature). These are the coefficients that the model needs to learn. It is the error term. This is achieved by defining a new set of variables. This transforms a nonlinear problem into a linear one. In the formula, The original model instantly transformed into a multiple linear regression model: Now, all solutions for linear regression can be directly applied. Since the quadratic polynomial is... Therefore, the standard model That is , That is , for Subsequent analyses were all described using standard multinomial regression models.

[0051] Analysis using the least squares method:

[0052] The goal is to find a set of coefficients , , ,…, This minimizes the residual sum of squares (RSS).

[0053] Mathematical expression: , where m is the number of samples.

[0054] Matrix solution:

[0055] Represent the problem in matrix form:

[0056] Design matrix X (containing all polynomial characteristics): coefficient vector Target vector The model is The optimal coefficient solution that minimizes the sum of squared residuals is obtained by the following normal equation: .

[0057] Finally, the solution was obtained. This allows us to obtain the quadratic polynomial model corresponding to the measured data.

[0058] Optionally, regression analysis can be performed on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; regression analysis is performed on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0059] Specifically, regarding the exponential function assumption ; The coefficient is: ; For the power function assumption ; The coefficient is: ; Linear regression analysis was used to establish a linear relationship between the rebound coefficient and the impact time, and the regression coefficients (slope) and intercept were determined.

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] Thus, the linear regression equation is obtained: .

[0069] Optionally, based on the first constant and the second constant, the exponential function model and the power function model can be obtained.

[0070] Specifically, by calculating the first and second constants, the final exponential function model is obtained as follows: Power function model These correspond to the exponential function patterns and power function models corresponding to the measured data.

[0071] Optionally, a suitable model can be selected from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0072] Specifically, after obtaining the quadratic polynomial model, exponential function model, and power function model, the graphs corresponding to the functions are plotted according to their characteristics. Then, the graphs of the measured data are plotted, and finally, the model that matches the measured data model is taken as the final analysis model.

[0073] In one embodiment, measured data is obtained:

[0074] t0 e lg(t0) lg[e] 0.01 0.9 -2.000 -0.046 2 0.8 0.301 -0.097 5 0.5 0.699 -0.301 15 0.2 1.176 -0.699 20 0.1 1.301 -1.000 100 0.01 2.000 -2.000

[0075] Regression analysis was performed using three models, and a quadratic polynomial fit was obtained. , , The exponential function was fitted to obtain , Power function fitting yields , .

[0076] Thus, the quadratic polynomial model is obtained. Exponential function model Power function model Images were drawn based on three models, with reference to... Figure 2 The impact time is the duration of impact, or the duration of impact. According to the image results, it can be seen that the power function image best matches the measured data. Therefore, the power function model will be used as the analysis model when calculating the relationship between the rebound coefficient and the impact duration under this condition.

[0077] This invention establishes a mathematical relationship between the two, enabling the coordinated determination of parameters and significantly improving calculation accuracy. It is applicable to various forging hammer types, including free forging and die forging, and can dynamically adjust parameters according to process conditions, exhibiting high versatility. Through explicit mathematical formulas, designers can quickly determine key parameters without relying on experience or repeated experiments, lowering the barrier to entry.

[0078] This application also provides an analysis device for the springback coefficient and impact time of a forging hammer. It should be noted that this analysis device can be used to execute the analysis method for the springback coefficient and impact time of a forging hammer provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0079] The following describes an analysis device for the springback coefficient and impact time of a forging hammer provided in an embodiment of this application.

[0080] Figure 3 This is a structural block diagram of an analysis device for the springback coefficient and impact time of a forging hammer according to an embodiment of this application. Figure 3 As shown, the device includes: an acquisition unit 301, used to acquire the rebound coefficient and action time based on measured data; an analysis unit 302, used to analyze the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models; and a selection unit 303, used to select one of the quadratic polynomial models, exponential function models, and power function models as the optimal model based on measured data.

[0081] In an optional embodiment, the analysis unit 302 includes: a transformation subunit for transforming the quadratic polynomial into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The second polynomial constant; the first transformation subunit, used to transform the exponential function into... ,in, It is the first constant. The second constant; the second transformation subunit, used to transform the power function into .

[0082] In an optional embodiment, the analysis unit 302 includes: a first analysis subunit, used to perform regression analysis on the quadratic polynomial to obtain polynomial constants and obtain a quadratic polynomial model.

[0083] In an optional embodiment, the analysis unit 302 includes: a second analysis subunit, used to perform regression analysis on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; the third analysis subunit is used to perform regression analysis on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0084] In an optional embodiment, the analysis unit 302 includes: an acquisition subunit, used to acquire an exponential function model and a power function model based on a first constant and a second constant.

[0085] In an optional embodiment, the first analysis subunit, the second analysis subunit, and the third analysis subunit include: a selection module, used to select a suitable model from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0086] The aforementioned analysis device for the springback coefficient and impact time of a forging hammer includes a processor and a memory. The acquisition unit 301 and others are stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0087] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and C technical problems can be solved by adjusting kernel parameters.

[0088] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0089] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for analyzing the springback coefficient and action time of a forging hammer impact.

[0090] Specifically, an analytical method for the relationship between the springback coefficient and the impact time of a forging hammer includes:

[0091] Step S101: Obtain the rebound coefficient and action time based on the measured data;

[0092] Specifically, based on the measured data of the forging hammer's impact force and the collection of relevant information, the rebound coefficient can be obtained. With action time Basic data, duration of action It refers to the duration of the attack.

[0093] Step S102: The rebound coefficient and action time are analyzed by quadratic polynomial, exponential function and power function to obtain quadratic polynomial model, exponential function model and power function model.

[0094] Specifically, based on known experimental data, the relationship between the rebound coefficient φ and the impact time... The relationship between the two functions was analyzed using least squares fitting with three mathematical function models: a quadratic polynomial model, an exponential function model, and a power function model. The fitting process was based on linear regression, using logarithmic transformation to convert the nonlinear model into a linear form.

[0095] Step S103: Select the optimal model from the quadratic polynomial model, exponential function model, and power function model based on the measured data.

[0096] Specifically, the optimal fitting model is determined by comparing the goodness of fit (such as the sum of squared residuals, coefficient of determination, etc.) of the three models. and The inverse relationship is a correct macroeconomic trend. We can reinforce this concept from the perspective of "stiffness" and "resistance": high (High elasticity), short (Short pulse width): This corresponds to a "high stiffness" system. The colliding bodies (forgings and hammer / anvil) deform very little, and the material rebounds rapidly like a spring. The force is large, but the duration of action is extremely short. Small impact energy (low e) results in high peak impact force. This typically occurs when the impact energy is insufficient, producing only elastic or minor plastic deformation, or when impacting very hard materials. High plasticity (low e), long... This corresponds to a "low stiffness" or "high damping" system. The forging undergoes significant plastic flow, acting like a "damper" to absorb energy. The force is relatively small, but it is maintained for a long time. (Large). This is the desired state for our forging process, meaning that energy is effectively used for forming. Therefore, the impact time... It can be seen as an indirect measure of the degree of "plasticity" in a process. The longer the length, the more complete the plastic forming. In practical applications, its applicable boundaries and consistency in extreme cases can be verified: when It conforms to perfectly elastic collision, ideally. When This conforms to a perfectly plastic impact. This is physically plausible, but for a forging hammer strike, It cannot be infinitely long; it has an upper limit determined by mass and system stiffness.

[0097] Optionally, the quadratic polynomial can be transformed into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The constant of the second polynomial is used; the exponential function is transformed into... ,in, It is the first constant. The second constant is used; the power function is transformed into... .

[0098] Optionally, regression analysis can be performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

[0099] Optionally, regression analysis can be performed on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; regression analysis is performed on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0100] Optionally, based on the first constant and the second constant, the exponential function model and the power function model can be obtained.

[0101] Optionally, a suitable model can be selected from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0102] This invention provides a processor for running a program, wherein the program executes the method for analyzing the springback coefficient and action time of a forging hammer impact.

[0103] Specifically, an analytical method for the relationship between the springback coefficient and the impact time of a forging hammer includes:

[0104] Step S101: Obtain the rebound coefficient and action time based on the measured data;

[0105] Specifically, based on the measured data of the forging hammer's impact force and the collection of relevant information, the rebound coefficient can be obtained. With action time Basic data, duration of action It refers to the duration of the attack.

[0106] Step S102: The rebound coefficient and action time are analyzed by quadratic polynomial, exponential function and power function to obtain quadratic polynomial model, exponential function model and power function model.

[0107] Specifically, based on known experimental data, the relationship between the rebound coefficient φ and the impact time... The relationship between the two functions was analyzed using least squares fitting with three mathematical function models: a quadratic polynomial model, an exponential function model, and a power function model. The fitting process was based on linear regression, using logarithmic transformation to convert the nonlinear model into a linear form.

[0108] Step S103: Select the optimal model from the quadratic polynomial model, exponential function model, and power function model based on the measured data.

[0109] Specifically, the optimal fitting model is determined by comparing the goodness of fit (such as the sum of squared residuals, coefficient of determination, etc.) of the three models. and The inverse relationship is a correct macroeconomic trend. We can reinforce this concept from the perspective of "stiffness" and "resistance": high (High elasticity), short (Short pulse width): This corresponds to a "high stiffness" system. The colliding bodies (forgings and hammer / anvil) deform very little, and the material rebounds rapidly like a spring. The force is large, but the duration of action is extremely short. Small impact energy (low e) results in high peak impact force. This typically occurs when the impact energy is insufficient, producing only elastic or minor plastic deformation, or when impacting very hard materials. High plasticity (low e), long... This corresponds to a "low stiffness" or "high damping" system. The forging undergoes significant plastic flow, acting like a "damper" to absorb energy. The force is relatively small, but it is maintained for a long time. (Large). This is the desired state for our forging process, meaning that energy is effectively used for forming. Therefore, the impact time... It can be seen as an indirect measure of the degree of "plasticity" in a process. The longer the length, the more complete the plastic forming. In practical applications, its applicable boundaries and consistency in extreme cases can be verified: when It conforms to perfectly elastic collision, ideally. When This conforms to a perfectly plastic impact. This is physically plausible, but for a forging hammer strike, It cannot be infinitely long; it has an upper limit determined by mass and system stiffness.

[0110] Optionally, the quadratic polynomial can be transformed into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The constant of the second polynomial is used; the exponential function is transformed into... ,in, It is the first constant. The second constant is used; the power function is transformed into... .

[0111] Optionally, regression analysis can be performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

[0112] Optionally, regression analysis can be performed on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; regression analysis is performed on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0113] Optionally, based on the first constant and the second constant, the exponential function model and the power function model can be obtained.

[0114] Optionally, a suitable model can be selected from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0115] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: obtaining the rebound coefficient and action time based on measured data; analyzing the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models; and selecting one of the quadratic polynomial models, exponential function models, and power function models as the optimal model based on the measured data. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0116] Optionally, the quadratic polynomial can be transformed into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The constant of the second polynomial is used; the exponential function is transformed into... ,in, It is the first constant. The second constant is used; the power function is transformed into... .

[0117] Optionally, regression analysis can be performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

[0118] Optionally, regression analysis can be performed on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; regression analysis is performed on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0119] Optionally, based on the first constant and the second constant, the exponential function model and the power function model can be obtained.

[0120] Optionally, a suitable model can be selected from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0121] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: obtaining the rebound coefficient and action time based on measured data; analyzing the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models; and selecting one of the quadratic polynomial models, exponential function models, and power function models as the optimal model based on measured data.

[0122] Optionally, the quadratic polynomial can be transformed into... ,in, The rebound coefficient is... For the duration of action, are polynomial constants. The constant coefficients, Let be the initial polynomial constants. The constant of the first polynomial, The constant of the second polynomial is used; the exponential function is transformed into... ,in, It is the first constant. The second constant is used; the power function is transformed into... .

[0123] Optionally, regression analysis can be performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

[0124] Optionally, regression analysis can be performed on the exponential function to obtain... and Then, the values ​​of the first and second constants are obtained; regression analysis is performed on the power function to obtain... and Then, the values ​​of the first constant and the second constant are obtained.

[0125] Optionally, based on the first constant and the second constant, the exponential function model and the power function model can be obtained.

[0126] Optionally, a suitable model can be selected from quadratic polynomial models, exponential function models, and power function models through image fitting as the analysis model for the rebound coefficient and the action time.

[0127] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0128] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0133] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0134] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0135] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0136] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of analyzing a forging hammer's coefficient of restitution and time of action, characterized by, include: The rebound coefficient and action time are obtained based on the measured data; By analyzing the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions, quadratic polynomial models, exponential function models, and power function models are obtained. Based on the measured data, one of the quadratic polynomial model, the exponential function model, and the power function model is selected as the optimal model.

2. The method of claim 1, wherein, By analyzing the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions, quadratic polynomial models, exponential function models, and power function models are obtained, including: converting the quadratic polynomial into wherein, is the coefficient of restitution, is the time of action, is a polynomial constant, is a constant coefficient, is an initial polynomial constant, is a first polynomial constant, is a second polynomial constant; transforming the exponential function into wherein, is a first constant, is a second constant; transforming the power function into .

3. The method of claim 2, wherein, By analyzing the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions, quadratic polynomial models, exponential function models, and power function models are obtained, including: Regression analysis is performed on the quadratic polynomial to obtain the polynomial constants and thus the quadratic polynomial model.

4. The method of claim 2, wherein, By analyzing the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions, quadratic polynomial models, exponential function models, and power function models are obtained, including: Regression analysis is performed on the exponential function to obtain and and further to obtain the values of the first constant and the second constant. Regression analysis is performed on the power function to obtain and , and the values of the first constant and the second constant are obtained.

5. The method according to claim 4, wherein the rebound coefficient and action time are analyzed using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models, including: Based on the first constant and the second constant, the exponential function model and the power function model are obtained.

6. The method according to claim 3 or 5, characterized in that, include: By using image fitting, a suitable model is selected from the quadratic polynomial model, the exponential function model, and the power function model as the analysis model for the rebound coefficient and the action time.

7. An analytical device for the springback coefficient and impact time of a forging hammer, characterized in that, include: The acquisition unit is used to obtain the rebound coefficient and action time based on measured data; The analysis unit is used to analyze the rebound coefficient and action time using quadratic polynomials, exponential functions, and power functions to obtain quadratic polynomial models, exponential function models, and power function models. The selection unit is used to select one of the quadratic polynomial model, the exponential function model, and the power function model as the optimal model based on the measured data.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for analyzing the springback coefficient and action time of a forging hammer impact as described in any one of claims 1 to 6.

9. An electronic device, comprising: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for performing an analysis of the springback coefficient and action time of a forging hammer according to any one of claims 1 to 6.

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