Optimization method and reliability evaluation system for red press molding process of mining integrated wheel

Abstract

In order to solve the problems of parameter optimization blindness and insufficient reliability in the prior art, the optimization method of the integrated wheel red press forming process comprises the following steps: based on a response surface method, obtaining process variable data and carrying out a test; performing binomial fitting through a response surface method according to a test result to obtain a response model corresponding to each response value, performing variance analysis on the test result and the response model, determining a corresponding relationship between the process variable and the response value, drawing a response surface diagram, performing constraint and importance definition on the data value of the process variable and the response value, and obtaining a response model corresponding to each response value; the method comprises the following steps of: establishing a fault tree, solving pre-selected process parameters, and determining the die pressing size of the die according to a simulation result of a first-order forming process based on an upper-end first-order forming process, and the reliability evaluation system comprises the steps of establishing the fault tree, calculating the reliability of the system and the probability importance of a basic event, and calculating the average fault-free time of the process through simulation.

Description

Technical Field

[0001] The present invention specifically relates to an optimization method for a red-pressing forming process of an integrated mining wheel and a reliability evaluation system, belonging to the technical field of mining vehicle wheel manufacturing. Background Art

[0002] Spokes are a crucial component of wheels, often welded together to form a rim. For some heavy, specialized vehicles, conventional welded spokes and rims often cannot withstand complex road conditions and high-intensity loads. Therefore, a new wheel upper end is required, one that is stamped and integrated with the spokes and rim. The shaping of this upper end plays a crucial role in the wheel's performance. Compared to conventional vehicles, the wheels of mining vehicles are larger and thicker to ensure safety and reliability. However, this increased thickness makes it difficult to control the press capacity during forming due to the high strength of the workpiece material, resulting in suboptimal product quality.

[0003] However, the existing non-road mining wheels are not taken seriously by industry insiders. The research on this type of wheels has the following problems:

[0004] 1. Process reliance on trial-and-error methods: There is no standardized parameter optimization solution for the hot stamping process, and simulation technology has not been applied to the integrated upstream process. Parameter optimization lacks a scientific basis, and research on key preparation processes for the integrated upstream relies on trial-and-error methods, resulting in high production costs and low efficiency.

[0005] Second, insufficient forming precision: In existing research and development technologies for this type of wheel, the material of the integrated upper end will change its performance under different temperature conditions. Different material properties also have different rebound characteristics. Under the hot pressing process, there is a large error between the actual workpiece and the ideal workpiece state, which is difficult to predict.

[0006] Third, there is a lack of reliability analysis; existing technologies fail to systematically assess process failure risks. The integrated upper end process is a crucial step in the integrated spoke manufacturing process, known for its low cost, high product performance, and suitability for forming large, thick plate structures. However, this process is affected by numerous factors, including labor, mold temperature, mold wear, and equipment. These factors can easily lead to uneven end faces, wrinkled surfaces, and dimensions not meeting expectations in the finished workpiece.

[0007] 4. Process problem: For the integrated upper forming process, since the forming is divided into two sequences, not only will the temperature drop during the transportation process, but also human errors will occur when placing the workpiece, affecting the forming results. In addition, excessive transportation will be time-consuming and labor-intensive, which will greatly increase the occurrence of safety problems. In addition, the two stamping processes will lead to cumbersome transportation between processes, serious temperature loss of the workpiece, and affecting the forming quality.

[0008] Therefore, there is an urgent need for an optimization method and reliability evaluation system for the red-pressing forming process of integrated mining wheels to solve the above problems, solve the problems of blind parameter optimization and insufficient reliability in the existing process, and achieve improved forming accuracy and reduced production costs. Summary of the Invention

[0009] In order to solve the problems mentioned in the above background technology; the purpose of the present invention is to provide an optimization method and reliability evaluation system for the red-pressing forming process of an integrated wheel for mining. The optimization method of the integrated wheel red-pressing forming process includes obtaining process variable data and conducting experiments based on the response surface method, performing binomial fitting through the response surface method according to the test results to obtain the response model corresponding to each response value, performing variance analysis on the test results and the response model, determining the correspondence between the process variables and the response values ​​and drawing a response surface diagram, constraining and defining the importance of the process variable data values ​​and the response values, and solving the preselected process parameters.

[0010] Preferably, the optimization method of the one-piece wheel red pressing forming process further includes determining the die size of the mold based on the upper end single-sequence forming process and the simulation results of the single-sequence forming process.

[0011] Preferably, the process variables include: processing temperature, first-order inclination angle, and friction coefficient.

[0012] Preferably, the response values ​​include: mold size, flatness, and maximum machining allowance.

[0013] Preferably, the obtaining of process variable data and conducting experiments, performing binomial fitting on the experimental results by response surface methodology and obtaining a response model corresponding to each response value comprises:

[0014] Obtain process parameter thresholds, and select reference values ​​from the thresholds as simulation parameter values. Set mold size, flatness, and maximum machining allowance as response targets, respectively. Use the BBD method to design a three-factor, three-level experimental scheme using Design Expert software, and establish a quadratic polynomial equation:

[0015]

[0016] In the above formula, J A 、 B 、 CRepresents the response surface functions corresponding to the three response targets A, B, and C of the one-piece wheel forming process; α0, α i , α ii , α ij is the regression equation coefficient, x i 、x j is the process parameter variable; k is the number of process parameters.

[0017] Preferably, the method further comprises the following steps: taking the mold size, flatness and maximum machining allowance as response targets, designing a three-factor three-level experimental scheme using the BBD method through the DesignExpert software, and establishing a quadratic polynomial equation.

[0018] The BBD design method was tested and the test results were obtained. According to formula (2.1) and the test results, binomial fitting was performed to obtain the quadratic response surface model of mold size, flatness, maximum machining allowance and process parameters:

[0019]

[0020] In the above formula, J A 、J B 、J C The quadratic response surface models represent the mold size, flatness and maximum machining allowance, respectively.

[0021] The present invention also provides a reliability assessment system for the red-pressing forming process of an integrated mining wheel, which is applied to the optimization method of the above-mentioned red-pressing forming process of an integrated mining wheel. With the existence of defects after forming as the top event, the basic events of the forming process are analyzed and a fault tree is established. The Monte Carlo random sampling method is used to calculate the system reliability and the probability importance of the basic events, and the average failure-free time of the process is calculated through simulation.

[0022] Preferably, the system reliability is:

[0023] The probability importance of basic events is: W N (X i )=N F / N m ;

[0024] In the above formula: m r At a given time t m Total number of failures in the internal system, F' m represents the system failure rate, M represents the number of system simulations, N F Indicates the number of system failures caused by failure of basic components, N m Indicates the total number of system failures.

[0025] Preferably, the Monte Carlo random sampling method is used to calculate the system reliability and the importance of the probability of basic events, further comprising: obtaining corresponding data for the characteristics of the red-pressing process, integrating the data, and generating a reliability curve of the integrated upper-end process, and obtaining a reliability fitting formula based on the simulation data and the reliability curve:

[0026] R(t)=a t (3.1)

[0027] The unreliability fitting formula of the integrated upper end is:

[0028] F(t)=1-R(t)=1-a t (3.2)

[0029] In the above formula, t is time, a is the base of the exponential function, and its value is 1.0434e -6E-04 Compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. Based on the response surface methodology, a response surface model is designed and constructed quickly. Process variable data is obtained and experiments are conducted. Based on the experimental results, binomial fitting is performed through the response surface methodology to obtain the response model corresponding to each response value. Variance analysis is performed on the experimental results and the response model. The correspondence between the process variables and the response values ​​is determined and a response surface diagram is drawn. Constraints and importance definitions are imposed on the process variable data values ​​and the response values. The preselected process parameters are solved and the effects of different parameters on the mold size, flatness and maximum machining allowance in the forming process are intuitively predicted. This expands the application of process simulation in the stamping field and provides an important reference for mold debugging personnel. It has high production efficiency and is conducive to saving resources and costs.

[0031] 2. Through the analysis of the process and reliability of the integrated upper end, a process fault tree was established in terms of process reliability to analyze the reliability and the importance of each failure time. The mean time between failures of the process was calculated through simulation. It was found that the temperature drop caused by transportation is more likely to cause failure. It is recommended to optimize the on-site transportation process to prevent a significant drop in temperature due to excessive transportation time. After optimization, the failure probability was reduced and compared with that before optimization, the mean time between failures of the process system was improved.

[0032] 3. Improve the process of one-step molding of the integrated upper end to save time and manpower, reduce errors and improve safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] For ease of explanation, the present invention is described in detail with reference to the following specific embodiments and accompanying drawings.

[0034] Figure 1 This is a schematic diagram of the integrated upper-end formed spoke stamping scheme;

[0035] Figure 2 Schematic diagram of heating method;

[0036] Figure 3 This is the stamping model diagram of OP2 and OP4;

[0037] Figure 4 This is the model diagram of the workpiece test results;

[0038] Figure 5 This is the secondary stamping result diagram;

[0039] Figure 6 It is a process diagram with the mold closing size as the response value;

[0040] Figure 7 It is a process diagram with flatness as the response value;

[0041] Figure 8 It is a process diagram with the maximum machining allowance as the response value;

[0042] Figure 9 A comparison chart of one-sequence forming and two-sequence forming;

[0043] Figure 10 Schematic diagram of the punch before and after the change;

[0044] Figure 11 This is a schematic diagram of the molding result after the working surface of the punch is changed;

[0045] Figure 12 This is a fault tree diagram of the integrated red press forming process system;

[0046] Figure 13 This is the reliability curve diagram of the integrated hot stamping process system before and after optimization;

[0047] Figure 14 It is a fault tree diagram of a sequential molding process system;

[0048] Figure 15 The reliability curve comparison diagram of the one-sequence molding process system and the two-sequence molding process system;

[0049] Figure 16 This is a flow chart of the optimization method for the red stamping process of integrated mining wheels.

[0050] In the figure, 1-punch surface; 2-die surface; 3-upper end surface of the workpiece; 4-horizontal surface of the workpiece. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clearly apparent, the present invention is described below using specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are merely illustrative and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0052] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, while other details that are not closely related to the present invention are omitted.

[0053] Specific implementation method 1: This embodiment only provides a preferred implementation method, which is to provide an optimization method for the red pressing forming process of an integrated mining wheel, such as Figure 16 As shown, the optimization method of the red stamping process of the one-piece wheel includes obtaining process variable data and conducting experiments based on the response surface methodology, performing binomial fitting on the test results through the response surface methodology and obtaining a response model corresponding to each response value, performing variance analysis on the test results and the response model, determining the correspondence between the process variables and the response values ​​and constructing a response surface diagram, constraining the process variable data values ​​and the response values ​​and defining their importance, and solving the preselected process parameters.

[0054] Specifically, for the integrated upper end forming process of the one-piece wheel red pressing forming, the present invention provides a specific embodiment, the integrated upper end forming process is as follows Figure 1 As shown, it is divided into two pressing steps. Figure 1 (a) is the workpiece blank diagram, which is a cylinder with a thickness of 25 mm and a height of h1; Figure 1 (b) is the expected size after the first press, which is formed by pressing the punch down with a height of h2, the purpose of which is to bend the upper part of the workpiece to facilitate the forming of the workpiece; Figure 1 (c) is the expected size after the second pressing, which is also the final target size. For the material of this workpiece, Q355D is a low-alloy high-strength steel with excellent comprehensive performance. It has the characteristics of high bearing strength, good toughness at low temperatures, strong impact resistance, and light weight. It is widely used in high-altitude and cold areas such as offshore oil extraction. It is also used in the processing of this integrated upper-end product. The main parameters of Q355D are shown in Table 1:

[0055] Table 1 Main mechanical properties of Q355D steel

[0056]

[0057] For the present invention, a simulation method is introduced. For the simulation model, it can be but not limited to using a designed forming scheme, using SolidWorks software to build a corresponding three-dimensional model, converting the format into STEP format and importing it into Simufact Forming software, and solving and analyzing it into five steps, as shown in Table 2;

[0058] Table 2 Five processes

[0059]

[0060] However, since the material is initially thick, it is difficult to stamp it directly at room temperature. Therefore, the present invention provides a preferred solution, which is to heat it first. Figure 2 The following table shows the actual heating method in the factory and the finite element simulation heating method. The height of the heated workpiece should be the same as that of the heated workpiece. Figure 1 The height of the bending part of the first stamping is consistent. After reaching the preset heating temperature, the workpiece is stamped for the first time. After the stamping is completed, it is cooled once and reaches the second stamping temperature, and then the second stamping is performed. Among them, OP3 is the process of temperature reduction caused by normal transportation. The stamping models of OP2 and OP4 are as follows Figure 3 As shown; finally, after OP5 cooling, the numerical analysis results of the workpiece are obtained, and the workpiece test result model diagram is shown in Figure 4 As shown, Figure 4 (a) Model made for field test, Figure 4 (b) is the model obtained through simulation test. By comparing the results of the field test with the simulation test, it can be judged that the results are consistent. By measuring the forming state of the model, it is found that the inner circle of the workpiece is seriously concave and the forming state is poor, which does not meet the expected goal. Therefore, it is necessary to optimize the design of the process parameters.

[0061] The present invention first provides a process parameter optimization method based on the response surface method. For the process optimization of the integrated wheel red pressing process, Simufact Forming is used to perform CAE analysis. The software forming module is used to set the punch process to terminate when the maximum tonnage reaches 16000kN, and the process variables are analyzed. The process variables of this embodiment include but are not limited to the temperature during the second-order processing, the friction coefficient of the system, and the first-order inclination angle. The Box-Behnken design is used to quickly construct a response surface model, and the influence of different parameters on each response value in the forming process is intuitively predicted, which expands the application of process simulation in the stamping field and provides an important reference for mold debugging personnel. It has high production efficiency. Specifically, for the process parameter optimization method based on the response surface method, the process variables are first determined, and then the response surface is established. The present invention provides a preferred method, Box-Behnken experimental design (Box-Behnken Design (BBD) is a type of response surface design method with the characteristics of simple model and high accuracy. It can establish a nonlinear relationship between response value and factor without a large number of experiments. The range of process parameter values ​​is selected according to process standards, on-site equipment specifications, processing guidelines and designer experience, and the recommended values ​​of process parameters are obtained as simulation parameters. The BBD method is used to design a 3-factor 3-level test plan. The BBD test plan design is completed using Design Expert software, in which multiple groups of factorial experiments are designed. The feasibility and effectiveness of CAE forming analysis are determined by fully combining the practical experience of FAW's main engine plant in stamping product forming. Dynaform is used to perform finite element analysis and carry out BBD experiments, and the reliable data required for response surface method is quickly obtained in a CAE manner. The response surface method uses the quadratic regression equation and combines the least squares method to fit the polynomial to construct an approximate expression to replace the objective function in the design problem. The result can be set as the response value to establish a mathematical relationship with the second-order function of the process parameter. Combined with the mathematical relationship of the second-order function and the analysis results, binomial fitting is performed to obtain a quadratic response surface model of the response value and the process parameters. After the model is established, the response surface analysis is performed, and the variance analysis is performed on the test results and the effectiveness of the model to determine the relationship between each process variable and the response value. The response surface diagram is constructed by converting the matrix through the formula. The surface changes in the response surface diagram can effectively reflect the influence of each process parameter on the response value of the workpiece. According to the above situation, the optimal parameters are selected and tested. The present invention uses the Optimization function of Design Expert, which mainly includes the application of DOE experimental design and optimization algorithm. The experimental data that have been carried out are fitted to minimize the number of experiments to obtain more experimental information, thereby quickly finding the parameters of the influencing factors and optimizing the products and processes.After drawing the response surface based on the experimental design results, the Optimization function is used to constrain the independent variables and dependent variables and define their importance. After solving, the optimal process parameters are obtained and compared with the process simulation results.

[0062] Specific implementation method 2: This embodiment only provides a preferred implementation method. The optimization method of the one-piece wheel red pressing forming process also includes determining the die size of the mold based on the upper end single-sequence forming process and the simulation results of the single-sequence forming process.

[0063] The embodiment provided by the present invention aims at the forming process of the integrated upper end, transitions the two-sequence forming process to one-sequence forming, optimizes and simulates the one-sequence forming mold, and directly places the heated workpiece into the second mold for red pressing, obtaining a comparison chart of the simulation results of the one-sequence forming and the optimal two-sequence forming results, as shown in FIG. Figure 9 As shown, in Figure 9 As can be seen in (a), there are obvious depressions at the corners, which greatly increases the difficulty of the subsequent processing process, and the upper plane fails to ensure the size of the workpiece, so the mold needs to be improved. Through analysis, it is found that the workpiece is affected by the radius of the punch during the forming process, causing the upper end of the workpiece to continue to roll over. By lengthening the size of the small inclined surface of the mold, the workpiece is formed in a similar order to reduce the rolling of the workpiece. Figure 10 It can be seen that the working surface of the punch is modified. Through the previous simulation analysis, it is found that the length of the small bevel will affect the forming state of the workpiece corner. The main changes are the size of the small bevel and the inclination of the large bevel. The height of the small bevel is changed from 13mm to 20mm, and the large bevel is changed from 3° to 5°. The results after the changes are obtained through simulation, as shown in the figure below. Figure 11 As shown, through Figure 11 By comparing with the data in Table 3, it can be seen that when the workpiece is formed in the first sequence, the forming state of the rounded corners is better, the levelness of the upper end surface is improved, and under the same tonnage, the mold closing size is relatively small, indicating that the forming of the workpiece can be guaranteed when the temperature is sufficient. After that, the present invention can also analyze the reliability of the first sequence forming.

[0064] Table 3 Two-sequence molding and one-sequence molding data

[0065]

[0066] Specific implementation method three: This embodiment only provides a preferred implementation method, and the process variables include: processing temperature, first-order inclination angle, and friction coefficient.

[0067] The process of integrated upper end red pressing has to go through four steps. The first step is to heat the steel ring to heat the upper part of the steel ring to increase the fluidity of the metal and facilitate the subsequent stamping process. The second step is to stamp the spokes for the first time. The main function is to bend the process to facilitate the subsequent flattening of the spoke end face. The third step is to stamp the spokes for the second time to stamp the spokes into shape. The last step is cooling. For the entire spoke forming process, the most difficult thing to control is the temperature of the spoke. In the process from the first stamping to the second stamping, due to the time delay when the on-site workers operate, the workpiece will lose a lot of heat during the transportation from the first sequence to the second sequence, causing the workpiece temperature to drop. This leads to differences in the performance of the workpiece material, making the forming state of the workpiece fail to meet the expected target. Therefore, the present invention provides a preferred embodiment based on the actual production situation of a factory. By studying the influence of three process variables, namely the temperature during the second sequence processing, the first sequence inclination angle and the friction coefficient of the system, on the response value, a response surface is constructed.

[0068] Specific implementation method four: This embodiment only provides a preferred implementation method, and the response values ​​include: mold size, flatness, and maximum machining allowance.

[0069] After selecting the experimental design in multiple groups of experiments, combined with the influence of various factors in the experiment, the present invention provides a preferred method to take the product mold size, flatness and maximum machining allowance as the response target, and select the temperature during the second-order processing, the first-order inclination angle and the friction coefficient of the system as the three factors of the multiple-selection test factors, such as Figure 5 As shown in the secondary stamping result diagram, the die closing size is the relative distance between the punch surface 1 and the die surface 2 after forming at the same tonnage; the flatness is measured at the upper end surface 3 of the workpiece; and the maximum machining allowance is the maximum vertical dimension of the horizontal surface 3 of the workpiece. By evaluating the above three response values, it is expected that the forming process parameters that achieve the optimal target size with the minimum equipment tonnage and minimize the subsequent cutting requirements can be obtained.

[0070] Specific embodiment 5: This embodiment only provides a preferred embodiment, obtaining process variable data and conducting experiments, performing binomial fitting on the experimental results through the response surface method and obtaining the response model corresponding to each response value, including:

[0071] Obtain process parameter thresholds, and select reference values ​​from the thresholds as simulation parameter values. Set mold size, flatness, and maximum machining allowance as response targets, respectively. Use the BBD method to design a three-factor, three-level experimental scheme using Design Expert software, and establish a quadratic polynomial equation:

[0072]

[0073] In the above formula, J A、B、C Represents the response surface functions corresponding to the three response targets A, B, and C of the one-piece wheel forming process; α0, α i , α ii , α ij is the regression equation coefficient, x i 、x j is the process parameter variable; k is the number of process parameters.

[0074] Specifically, in this invention, the above parameter ranges were selected based on process standards, on-site equipment specifications, processing guidelines, and designer experience to obtain recommended process parameter values ​​as simulation parameters. The temperature x1 was selected to be 700-850°C, the first-order inclination angle x2 to be 40-50°, and the friction coefficient x3 to be 0.2-0.35. A three-factor, three-level experimental design was designed using the BBD method, and the process parameter factor levels were selected as shown in Table 4.

[0075] Table 4. Factor level table of the multiple-choice BBD test

[0076]

[0077] The BBD test scheme design was completed using Design Expert software, in which 13 groups of factorial experiments were designed. The feasibility and effectiveness of CAE forming analysis were determined by fully combining the practical experience of a certain company in stamping product forming. Dynaform was used to perform finite element analysis and carry out BBD experiments, and the reliable data required for the response surface method was quickly obtained in a CAE manner. The specific test scheme and test results are shown in Table 5, where A is the die size of the hot stamping process, in mm; B is the flatness of the hot stamping process, in mm; and C is the maximum machining allowance of the hot stamping process, in mm. The response surface method uses a quadratic regression equation and combines the least squares method to fit a polynomial to construct an approximate expression to replace the objective function in the design problem. There is an unknown relationship between the three test results A, B, and C of the hot stamping process and the process parameters. The results can be set as response values ​​to establish a quadratic polynomial equation with the process parameters. Specifically, the quadratic equation in the present invention can be, but is not limited to, k=3.

[0078] Table 5 BBD analysis scheme and test results

[0079]

[0080] Specific embodiment 6: This embodiment only provides a preferred embodiment, wherein the mold size, flatness and maximum machining allowance are respectively used as response targets, and a three-factor three-level test scheme is designed using the BBD method using the Design Expert software. After establishing the quadratic polynomial equation, the following is also included:

[0081] The BBD design method was tested and the test results were obtained. According to formula (2.1) and the test results, binomial fitting was performed to obtain the quadratic response surface model of mold size, flatness, maximum machining allowance and process parameters:

[0082]

[0083] In the above formula, J A 、J B 、J C Representing the quadratic response surface model of the mold size, flatness and maximum machining allowance respectively; according to the response surface model, a quadratic polynomial model with the mold size, flatness and maximum machining allowance as response values, temperature, first-order inclination angle and friction coefficient as independent variables was obtained by fitting, as shown in the above formula. The quadratic polynomial was obtained by fitting, and the relationship between the constant term, linear term, quadratic term, interaction term and response variable was specifically described.

[0084] Specific embodiment seven: This embodiment only provides a preferred embodiment. The present invention conducts specific experiments and performs variance analysis on the test results and the validity of the model to determine the relationship between each factor and the response value. After analyzing the data in Tables 6 and 7 below, the influence relationship of each factor in the model is relatively complex. The results are shown in Table 6, where for the mold size, x1x2, x2x3, and x1x3 are two-factor interaction terms. The P value of the whole model is less than 0.0001, indicating that the corresponding significance is strong. The influence of the first-order terms x1 and x3 in the model is very significant, and the influence of the interaction term is not significant. The impact is significant. and The influence of is not significant; for flatness, the influence of the first-order terms x1, x2, and x3 in the model are all significant, the influence of the interaction term is not significant, and the influence of the quadratic term is not significant; for the maximum machining allowance, the influence of the first-order terms x1 and x3 are very significant, x2 is more significant, the interaction term x1x3 is more significant, and the rest are not significant. The impact is significant. The impact is not significant.

[0085] For R 2 and The closer the result is to 1, the higher the fitting accuracy of the model. The three factors selected are x1, x2, and x3, and the coefficient R 2 The response value comes from these three factors, so the model has a good fitting effect and can meet the prediction ability through the response surface. As shown in Table 6, for the mold size, the model fit (determination coefficient) R 2 The corrected fit is 0.9762. is 0.9456; for flatness, the model fit (coefficient of determination) R 2 The corrected fit is 0.8328. is 0.6177; for the maximum machining allowance, the model fit (determination coefficient) R 2 The corrected fit is 0.9762. It is 0.9456.

[0086] Table 6 Analysis of variance of regression model

[0087]

[0088]

[0089] Table 7 Model accuracy parameters

[0090]

[0091] The response surface diagram constructed by the formula transformation matrix is Figure 6 、 7 The surface changes in 8 can effectively reflect the influence of various process parameters on the mold size, flatness and maximum machining allowance of the workpiece. Figure 6 The mold size is the response value, and the analysis Figure 6 (a) It can be seen that the mold size of the two-order stamping is greatly affected by the temperature and the size of the first-order inclination angle. When the temperature is about 800℃ and the first-order inclination angle is about 46°, the mold size can be minimized. That is, under the same mold size, the process required for workpiece forming is the smallest; Figure 6 (b) and Figure 6 As can be seen in (c), the minimum mold closing size occurs at the position with the minimum friction coefficient; Figure 7 It is a process diagram constructed with flatness as the response value. By fixing one factor respectively, the change relationship between the two variables is fitted. By drawing the process diagram, the parameter matching is made more intuitive. Figure 8 The response surface diagram is constructed by three parameters with the maximum machining allowance as the response value. During the red pressing process, the workpiece is squeezed, causing the end size to become coarser and requires turning processing. Therefore, the smaller the allowance, the lower the cost required for turning.

[0092] After this, optimal parameters were selected and tested. Existing experimental data was fitted to minimize the number of trials and obtain more experimental information, allowing for the rapid identification of influencing factors and optimization of products and processes. After plotting the response surface based on the experimental design results, the Optimization function was used to constrain the independent and dependent variables and define their importance. The optimal process parameters were obtained after solving the problem, as shown in Table 8. The importance rating was 5 for very important and 3 for important. Further simulation analysis was performed to compare the results with the Design Expert data.

[0093] Table 8 Parameter constraint data

[0094]

[0095] Table 9 Fitting data and experimental data

[0096]

[0097]

[0098] Comparing the data in Table 9, the optimal parameters obtained through response surface analysis are very close to those obtained in process simulation, with the maximum allowance value controlled within 9-10 mm. Larger machining allowances not only lead to material waste and increased costs, but also reduce machining efficiency, deteriorate workpiece surface quality, and decrease machining accuracy. Therefore, it is necessary to control the machining allowance within a smaller range. A die size of 31 mm also meets the requirements. Since the tonnage setting in the simulation is the same, a smaller die size at the same tonnage indicates more complete contact between the workpiece forming surface and the mold during machining, resulting in better molding results. In other words, when achieving the same die size, a smaller tonnage is required, thus saving resources and costs. Flatness also reaches 0.032, and lower flatness is also beneficial for subsequent machining.

[0099] Specific embodiment eight: This embodiment only provides a preferred embodiment. The present invention also provides a reliability evaluation system for the red-pressing forming process of an integrated mining wheel, which is applied to the optimization method of the red-pressing forming process of the integrated mining wheel. The defects after forming are taken as the top event, the basic events of the forming process are analyzed and a fault tree is established. The Monte Carlo random sampling method is used to calculate the system reliability and the probability importance of the basic events and the average failure-free time of the process is calculated through simulation.

[0100] Specifically, the reliability analysis of the process system is carried out to find out the failure causes and weak links of the process system and improve the reliability of the integrated spoke forming quality. The red pressing process of the integrated upper end has two stamping processes. There is a heating process before the first stamping. After electromagnetic heating, the first stamping is carried out through the transportation process, and then the second mold stamping is carried out through the transportation process. First, the process system fault tree is established. According to the fault tree establishment determination principle, "defects after the integrated upper end is formed" is selected as the top event of the system fault analysis. The integrated process system can ultimately be composed of multiple intermediate events. The present invention only provides a preferred implementation method, which can be but not limited to seven intermediate events including unqualified final size, wrinkled workpiece surface, unqualified mold quality, human operation error, rough mold surface, insufficient heating temperature and cracking. By analyzing the logical relationship between each process link and the system, each underlying event is associated with a logic gate to obtain the basic events of the fault tree, as shown in Table 10. The fault tree of the integrated red pressing forming process system is as follows Figure 12 As shown in the figure, this fault tree has a total of 8 logic gates and 9 basic events. After establishing the fault tree, the present invention performs qualitative analysis and quantitative calculations on the process system fault tree, calculates the system reliability and the probability importance of basic events, and calculates the mean time between failures of the process through simulation.

[0101] Table 10 Basic events of fault tree

[0102]

[0103] Specific implementation method 9: This embodiment only provides a preferred implementation method, and the system reliability is:

[0104] The probability importance of basic events is: W N (X i )=N F / N m ;

[0105] In the above formula: m r At a given time t m Total number of failures in the internal system, F m ' represents the system failure rate, M represents the number of system simulations, N F Indicates the number of system failures caused by failure of basic components, N m Indicates the total number of system failures.

[0106] Specifically, the present invention performs qualitative analysis and quantitative calculations on the process system fault tree. First, the minimum cut set of the fault tree is calculated using the ascending method: (X1, X5, X7), (X2, X5, X7), (X3, X5), (X6), (X8), (X9). The importance of each basic event in the fault tree is determined by the order of the minimum cut set, as shown in Table 11.

[0107] Table 11 gives the order of importance of basic events

[0108]

[0109]

[0110] A reliability simulation model is established based on the process system fault tree, and the simulation system variables are defined. The Monte Carlo random sampling method is used to sample each basic event to obtain a simple sample of the basic event failure time and sort it from small to large. The interval statistics method is used to perform distribution statistics and calculation of the number of system failures, and the system's cumulative failure rate and the probability importance of the basic events are obtained. The simulation operation results are analyzed, the weak links of the system are identified, and corresponding improvement measures are taken.

[0111] 1) System reliability Where: m r At a given time t m Total number of failures in the internal system; F' m represents the system failure rate; M represents the number of system simulations.

[0112] 2) Basic event probability importance W N (X i )=N F / N m Where: N F Indicates the number of system failures caused by failure of basic components; N m Indicates the total number of system failures.

[0113] In view of the characteristics of the red pressing process, the present invention provides a specific embodiment. Through systematic data collection and multi-faceted research, combined with industry data and feedback from on-site workers, the failure probability and probability importance of the bottom event in Table 12 are obtained through data integration, as shown in Table 12. Through simulation calculation, it is assumed that the bottom event follows an exponential distribution, in which the sampling number M = 3000 times is selected, and the system failure-free time T max =5000h, the time interval is 48h.

[0114] Table 12 Failure probability and probability importance of bottom events

[0115]

[0116]

[0117] Specific implementation method ten: This embodiment only provides a preferred implementation method. The Monte Carlo random sampling method is used to calculate the system reliability and the probability importance of basic events, and further includes: obtaining corresponding data for the characteristics of the red pressing process, integrating the data, and generating a reliability curve of the integrated upper end process. The reliability fitting formula obtained based on the simulation data and the reliability curve is:

[0118] R(t)=a t (3.1)

[0119] The unreliability fitting formula of the integrated upper end is:

[0120] F(t)=1-R(t)=1-a t (3.2)

[0121] In the above formula, t is time, a is the base of the exponential function, and its value is 1.0434e -6E-04 .

[0122] Specifically, based on the data obtained from simulation, combined with Figure 13 The reliability curves of the integrated red press forming process system before and after optimization are fitted to obtain the fitting formula (3.1) of the reliability of the integrated upper end process. Specifically, the fitting coefficient R 2 =0.9986, indicating that the regression model is consistent with the results. Therefore, the unreliability F(t) of the upper end of the integration can be expressed by formula (3.2).

[0123] Through simulation analysis, the mean time to failure (MTTF) of the process system is obtained as 1471.77h. Based on the fault tree and the failure probability of each event, the probability of occurrence of the top event is calculated to be 0.0004, which indicates that the reliability of the red pressing process is relatively high. As can be seen from Table 12, excluding the causes of cracking of the workpiece itself, the probability of temperature drop caused by transportation, human error in operation, and the rough surface of the first-order mold are relatively important, which have a greater impact on the reliability of the process system and need to be improved accordingly. After improving the transportation process, the failure probability can be reduced to 3× / 10 -5 ·h -1 , and then simulate, according to the simulation results, the average time to failure MTTF of the process system is obtained = 1664.14h, such as Figure 13As shown, the solid line trend represents the system reliability R(t) curve, and the dotted line trend represents the system unreliability F(t) curve. By analyzing the process and reliability of the integrated upper end, a process fault tree is established in terms of process reliability to analyze the reliability and the importance of each failure time, and the mean time between failures of the process is calculated through simulation. In a specific embodiment, it is found that the temperature drop caused by transportation is more likely to cause failure. It is recommended to optimize the on-site transportation process to prevent a significant drop in temperature due to excessive transportation time. The failure probability is reduced after optimization and compared with that before optimization, thereby improving the mean time between failures of the process system.

[0124] Specific implementation method ten: This embodiment only provides a preferred implementation method, and applies the reliability evaluation system of the red-pressing forming process of the integrated mining wheel mentioned in the present invention to the upper-end one-sequence forming process mentioned in the present invention to provide a specific embodiment to analyze the reliability of the one-sequence forming. Compared with the two-sequence forming process, the one-sequence forming process is relatively simple, which only includes a heating process, a transportation process, and a stamping process. First, the fault tree of the one-sequence forming process system is established. According to the fault tree establishment determination principle, "defects after the integrated upper end forming" is also selected as the top event of the system fault analysis. The integrated process system consists of three intermediate events: unqualified final size and wrinkling of the workpiece surface. By analyzing the logical relationship between each process link and the system, each underlying event is linked with a logic gate to obtain the basic events of the fault tree, as shown in Table 13. The fault tree of the one-sequence forming process system is as follows. Figure 14 As shown in Figure 2. This fault tree has 4 logic gates and 6 basic events.

[0125] Table 13 Basic events of fault tree

[0126]

[0127] Qualitative analysis of the fault tree for this integrated upper molding process system reveals that the minimum cut set of the fault tree is (X1, X4, X5), (X2, X4, X5), (X3, X4, X5), and (X6). The importance of each basic event in the fault tree is determined by the order of the minimum cut set, as shown in Table 14.

[0128] Table 14 Basic event importance order

[0129]

[0130]

[0131] A reliability simulation model is established based on the process system fault tree, and the simulation system variables are defined. The Monte Carlo random sampling method is used to sample each basic event to obtain a simple sample of the basic event failure time and sort it from small to large. The interval statistics method is used to perform distribution statistics and calculation of the number of system failures, and the system's cumulative failure rate and the probability importance of the basic events are obtained. The simulation operation results are analyzed, the weak links of the system are identified, and corresponding improvement measures are taken.

[0132] 1) System reliability Where: m r At a given time t m Total number of failures in the internal system; F' m represents the system failure rate; M represents the number of system simulations.

[0133] 2) Basic event probability importance W N (X i )=N F / N m Where: N F Indicates the number of system failures caused by failure of basic components; N m Indicates the total number of system failures.

[0134] According to the characteristics of the hot stamping process, through systematic data collection and multi-faceted research, combined with industry data and feedback from on-site workers, the failure probability and probability importance of the bottom event were obtained through data integration, as shown in Table 15. Through simulation calculation, it is assumed that the bottom event follows an exponential distribution, in which the sampling number M = 3000 times is selected, and the system failure-free time T max =5000h, the time interval is 48h. Compare the reliability curve of the process system of the integrated upper end single-sequence molding with the reliability curve of the process system of the two-sequence molding. Figure 15 shown

[0135] Table 15 Failure probability and probability importance of bottom events

[0136]

[0137]

[0138] Based on the fault tree and the failure probability of each event, the probability of occurrence of the top event was calculated to be 0.000172. This indicates that the reliability of the hot stamping process is high and lower than that of the two-sequence forming process. Table 15 shows that, excluding the causes of workpiece cracking, the probability of temperature drop caused by transportation and the rough mold surface are relatively significant, significantly impacting the reliability of the process system and requiring corresponding improvements. Simulation results were used to calculate the mean time between failures (MTBF) of the process system. Comparing this with the MTBF of the two-sequence forming process, it can be seen that the MTBF of the single-sequence forming process is significantly greater than that of the two-sequence forming process. Furthermore, at the same time point, the reliability of the single-sequence forming process is higher than that of the two-sequence forming process.

[0139] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the hot stamping process of a mining integrated wheel, characterized in that: The optimization method of the one-piece wheel red stamping forming process includes obtaining process variable data and conducting experiments based on the response surface methodology, performing binomial fitting through the response surface methodology based on the test results to obtain a response model corresponding to each response value, performing variance analysis on the test results and the response model, determining the correspondence between the process variables and the response values ​​and drawing a response surface diagram, constraining and defining the importance of the process variable data values ​​and the response values, and solving the preselected process parameters.

2. The method for optimizing the red stamping process of the integrated mining wheel according to claim 1, characterized in that: The optimization method of the one-piece wheel red pressing forming process also includes determining the pressing die size of the mold based on the upper end single-sequence forming process and the simulation results of the single-sequence forming process.

3. The method for optimizing the hot stamping process of the integrated mining wheel according to claim 2, characterized in that: The process variables include: processing temperature, first-order inclination angle, and friction coefficient.

4. The method for optimizing the hot stamping process of the integrated mining wheel according to claim 3, characterized in that: The response values ​​include: mold size, flatness, and maximum machining allowance.

5. The method for optimizing the hot stamping process of the integrated mining wheel according to claim 4, characterized in that: The process variable data is obtained and tested, and binomial fitting is performed on the test results using the response surface method to obtain a response model corresponding to each response value, including: Obtain process parameter thresholds, and select reference values ​​from the thresholds as simulation parameter values. Set mold size, flatness, and maximum machining allowance as response targets, respectively. Use the BBD method to design a three-factor, three-level experimental scheme using Design Expert software, and establish a quadratic polynomial equation: In the above formula, J A、B、C Represents the response surface functions corresponding to the three response targets A, B, and C of the one-piece wheel forming process; α0, α i , α ii , α ij is the regression equation coefficient, x i 、x j is the process parameter variable; k is the number of process parameters.

6. The method for optimizing the hot stamping process of the integrated mining wheel according to claim 5, characterized in that: The above-mentioned experiment scheme with three factors and three levels is designed by using the BBD method with the mold size, flatness and maximum machining allowance as the response targets respectively through the Design Expert software. After the quadratic polynomial equation is established, the following is also included: The BBD design method was tested and the test results were obtained. According to formula (2.1) and the test results, binomial fitting was performed to obtain the quadratic response surface model of mold size, flatness, maximum machining allowance and process parameters: In the above formula, J A 、J B 、J C The quadratic response surface models represent the mold size, flatness and maximum machining allowance, respectively.

7. A reliability evaluation system for the hot stamping process of a one-piece mining wheel, applied to the optimization method of the hot stamping process of the one-piece mining wheel, characterized in that: Taking defects after molding as the top event, the basic events of the molding process are analyzed and a fault tree is established. The Monte Carlo random sampling method is used to calculate the system reliability and the probability importance of basic events, and the mean time between failures of the process is calculated through simulation.

8. The reliability evaluation system for the red stamping process of the integrated mining wheel according to claim 7 is characterized in that: The system reliability is: The probability importance of basic events is: W N (X i )=N F / N m ; In the above formula: m r At a given time t m Total number of failures in the internal system, F′ m represents the system failure rate, M represents the number of system simulations, N F Indicates the number of system failures caused by failure of basic components, N m Indicates the total number of system failures.

9. The reliability evaluation system for the red stamping process of the integrated mining wheel according to claim 8, characterized in that: The Monte Carlo random sampling method is used to calculate the system reliability and the importance of the probability of basic events, and further includes: obtaining corresponding data for the characteristics of the red-pressing process, integrating the data, and generating a reliability curve of the integrated upper-end process. The reliability fitting formula obtained based on the simulation data and the reliability curve is: R(t)=a t (3.1) The unreliability fitting formula of the integrated upper end is: F(t)=1-R(t)=1-a t (3.2) In the above formula, t is time, a is the base of the exponential function, and its value is 1.0434e -6E-04 .

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