Method for improving and optimizing forge piece manufacturing process
By using Deform-3D simulation models and experimental verification, combined with mold wear analysis, forging parameters were accurately selected and corrected, solving the problems of long trial-and-error cycles and mold wear in existing forging manufacturing, and improving the forging qualification rate and production stability.
Patent Information
- Application Number
- CN202512042647.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-05-19
AI Technical Summary
The optimization of existing forging manufacturing processes relies on trial and error based on experience, which results in long trial and error cycles, high costs, insufficient adaptability of simulation models, and insufficient consideration of the impact of mold wear, leading to a decrease in the pass rate of forgings and unstable production.
By constructing a model using Deform-3D rigid-plastic finite element simulation, the risk of defects in forging parameters is assessed. Combined with experimental operation and die wear analysis, a quantitative correction model is established to accurately screen and correct forging parameters.
It enables precise assessment of forging quality and improves production stability, reduces trial production costs, and enhances the forging manufacturing qualification rate and the scientific and intuitive nature of production.
Smart Images
Figure CN122065574A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of process improvement technology, specifically a method for improving and optimizing forging manufacturing processes. Background Technology
[0002] Forgings, as core basic components in the machinery manufacturing field, are widely used in high-end equipment fields such as aerospace, rail transportation, and construction machinery. Their forming quality directly determines the reliability and service life of the equipment. With the continuous improvement of the precision and performance requirements of high-end equipment for forgings, how to improve the forming quality of forgings, reduce the defect rate, and increase the production qualification rate through process optimization has become a key technical problem that the forging manufacturing industry urgently needs to solve.
[0003] Current forging manufacturing process optimization largely relies on the production experience of technical personnel for parameter trial and error. This not only results in long trial and error cycles and high production costs, but also makes it difficult to accurately control the comprehensive impact of different forging parameters on forging quality. Although some process optimization methods have introduced finite element simulation technology, the compatibility of geometric features, material properties, and boundary conditions during the simulation model construction process is insufficient, and a unified quantitative assessment index for defect risk has not been established. This leads to a significant deviation between simulation results and actual production, making it difficult to effectively guide the selection of actual process parameters. At the same time, existing technologies do not fully consider the impact of mold wear on the compatibility of process parameters during use. When mold wear occurs, it is impossible to compensate for the quality risks caused by wear through precise parameter correction, which in turn leads to a significant decrease in the forging pass rate, seriously affecting production stability and economic benefits.
[0004] Therefore, the present invention provides a method for improving and optimizing the forging manufacturing process. Summary of the Invention
[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.
[0006] The technical solution adopted by this invention to solve its technical problem is: Static data of the forging under the current working condition is collected. Based on the rigid-plastic finite element algorithm of Deform-3D, a rigid-plastic finite element simulation model is constructed to simulate the influence of different forging parameters on the forming quality of the forging and to evaluate the defect risk of the forging in the simulated forging process under the forging parameters. If the defect risk is low, extract the defect risk coefficient corresponding to the low-risk parameters to determine the optimal simulation parameters; The forgings were tested based on the optimal simulation parameters. The test run period was preset. The actual pass rate of the forgings during the test run period was quantitatively analyzed to determine whether the pass rate of the forgings was abnormal. If the pass rate is abnormal, analyze the actual mold wear, combine the mold wear amount with the surface roughness increment, establish a quantitative correction model, correct the mold friction coefficient, determine the actual optimal parameters, and improve the pass rate of forging manufacturing. As a further aspect of the present invention: the process of constructing the rigid-plastic finite element simulation model is as follows: Geometric Model: Import CAD models of the forging blank, target forging, and new mold; Material Model: For the forging, a rigid-plastic material model is selected, and the flow stress constitutive curve obtained from the hot tensile test is imported; for the die, a rigid material model is selected, and parameters such as elastic modulus and thermal conductivity are set. Boundary conditions: Set the initial temperature of the forging and the preheating temperature of the die; set the die pressing speed and friction coefficient, and select a shear friction model suitable for high-temperature forging; Meshing and Solving: Tetrahedral elements are used for the forgings and rigid meshes are used for the dies. A rigid-plastic finite element solver is selected, and the convergence accuracy and iteration steps are set. A rigid-plastic finite element simulation model is constructed based on the geometric model, material model, boundary conditions, mesh, and solution.
[0007] As a further aspect of the present invention: the process for evaluating the defect risk of the forging during the simulated forging process under forging parameters is as follows: Set the parameters to be optimized, including forging temperature, die pressing speed, and friction coefficient. In Deform-3D, enable batch processing mode, and automatically modify the forging temperature, pressing speed, and friction coefficient by writing a parameterized script file. Run the simulation in sequence and output the result file. The results file includes the cumulative equivalent strain of the elements, the peak equivalent stress of the elements, the coordinates of the element nodes, and the final volume of the forging. By analyzing the data in the results file, the strain gradient exceeding ratio, stress exceeding ratio, and unfilled volume ratio are determined. The defect risk coefficient is obtained by multiplying the strain gradient exceeding ratio, stress exceeding ratio, and unfilled volume ratio. If the defect risk coefficient is greater than or equal to the defect risk coefficient threshold, it indicates that the defect risk of the forging is high during the simulated forging process under the forging parameters, and the corresponding forging parameters are recorded as high-risk parameters; otherwise, it indicates that the defect risk of the forging is low during the simulated forging process under the forging parameters, and the corresponding forging parameters are recorded as low-risk parameters.
[0008] As a further aspect of the present invention: the process for determining the strain gradient exceedance ratio is as follows: Based on any given element, traverse all adjacent elements of the current element, and calculate the strain gradient between the element and its adjacent elements by combining the element node coordinates. The element strain gradient is obtained by summing the strain gradients between the current element and all its neighboring elements and taking the average value. If the strain gradient of an element is greater than the element strain gradient limit, the corresponding element is recorded as an element with excessive strain gradient. The percentage of units with strain gradient exceeding the standard is counted and denoted as the strain gradient exceeding the standard ratio.
[0009] As a further aspect of the present invention: the process for determining the stress exceedance ratio is as follows: If the peak value of the equivalent stress of an element is greater than the limit of the peak value of the equivalent stress of an element, the corresponding element is recorded as an element with excessive stress. The percentage of units with stress exceeding the standard is counted and recorded as the stress exceeding the standard ratio.
[0010] As a further aspect of the present invention: the process for determining the percentage of unfilled volume is as follows: The absolute value of the difference between the measured target volume of the mold cavity and the final volume of the forging is then taken and compared with the measured target volume of the mold cavity to obtain the percentage of unfilled volume.
[0011] As a further aspect of the present invention: the process for determining the optimal simulation parameters is as follows: All the defect risk coefficients corresponding to the low-risk parameters are integrated into a defect risk coefficient sequence in ascending order. The low-risk parameter corresponding to the first defect risk coefficient in the sequence is extracted as the optimal parameter for simulation.
[0012] As a further aspect of the present invention: the process for determining whether the forging pass rate is abnormal is as follows: The process parameters of the production equipment were strictly calibrated to the optimal parameters in the simulation, including forging temperature, die pressing speed, and friction coefficient. Referring to the defect assessment types in the simulation, which include cracks, folds, and incomplete filling, and in conjunction with actual production quality standards, the sampled samples are inspected: ultrasonic testing is used to detect internal cracks; 3D scanning is used, combined with visual inspection, to determine folds and incomplete filling defects; and the dimensions are compared with the target forging dimensions, and forgings with out-of-tolerance dimensions are statistically analyzed. Forgings with any of the three defect assessment types (cracks, folds, or incomplete filling) that exceed the tolerance are judged as unqualified samples. The number of non-conforming samples during the test run is obtained. The difference between the total number of test samples and the number of non-conforming samples during the test run is calculated to obtain the number of qualified samples. The ratio of the number of qualified samples to the total number of test samples is calculated to obtain the pass rate. If the pass rate is greater than or equal to the pass rate threshold, it indicates that the forging pass rate is normal; otherwise, it indicates that the forging pass rate is abnormal.
[0013] As a further aspect of the present invention: the process of establishing the quantization correction model is as follows: The mold is disassembled from the production equipment, impurities on the cavity surface are removed, and after drying, it is placed on the inspection platform. The mold cavity is scanned in full size using a 3D laser scanning device to generate an STL model of the worn mold. This model is then compared with the CAD model of the new mold to obtain the cavity volume of the worn area. The difference between this model and the target cavity volume is then calculated, and the absolute value is taken to obtain the mold wear amount. The surface roughness of the worn area is detected by a surface roughness meter, and the difference between the surface roughness and the initial roughness of the new mold is calculated to obtain the surface roughness increment. The correlation coefficient between mold wear and yield rate was calculated using the Spearman correlation coefficient formula, and the absolute value was then taken as the wear correlation value. The abnormal correlation value is obtained by subtracting the wear-related value from 1 and taking the absolute value. If the abnormal correlation value is less than the abnormal correlation threshold, it indicates that mold wear is the cause of the abnormal pass rate. Since mold wear is the cause of abnormal yield, a quantitative correction model is established by combining the amount of mold wear with the increase in surface roughness.
[0014] As a further aspect of the present invention: the process for determining the actual optimal parameters is as follows: The corrected friction coefficient is calculated based on the quantization correction model; In Deform-3D, the friction coefficient of the original simulation model is updated to the corrected friction coefficient. The forging temperature and pressing speed are fixed, the simulation is rerun, and the optimal parameters corresponding to the corrected minimum defect risk coefficient are extracted as the actual optimal parameters.
[0015] The beneficial effects of this invention are as follows: This invention integrates Deform-3D rigid-plastic finite element simulation with actual experimental verification to achieve precise selection and dynamic correction of forging parameters. First, a simulation model is constructed using static data. Through parameterized batch processing and quantitative assessment of defect risk coefficients, high- and low-risk parameters are efficiently distinguished, and the optimal simulation parameters are selected, avoiding the blindness of traditional trial-and-error methods and reducing trial production costs. Then, the actual adaptability of the simulation parameters is verified through experimental operation, and the comprehensiveness of the pass rate assessment is ensured by combining full-project inspection. For abnormal pass rate situations, a quantitative correction model is established based on die wear and surface roughness increments to accurately correct the friction coefficient and obtain the actual optimal parameters, effectively solving quality problems caused by die wear. The entire process achieves deep integration of simulation data and actual production, normalizes and integrates multiple types of defect risks, improves the scientific rigor and intuitiveness of forging quality assessment, and ultimately significantly improves the pass rate and production stability of forging manufacturing. Attached Figure Description
[0016] The invention will now be further described with reference to the accompanying drawings.
[0017] Figure 1 This is a flowchart illustrating the steps of an improvement and optimization method for a forging manufacturing process according to an embodiment of the present invention. Figure 2 This is a system block diagram of an improvement and optimization system for forging manufacturing process according to an embodiment of the present invention. Detailed Implementation
[0018] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.
[0019] Example 1 Please see Figure 1 As shown in the embodiment of the present invention, a method for improving and optimizing a forging manufacturing process includes the following steps: Step 1: Collect static data of the forging under the current working conditions, construct a rigid-plastic finite element simulation model based on the Deform-3D rigid-plastic finite element algorithm, simulate the influence of different forging parameters on the forming quality of the forging, and evaluate the defect risk of the forging in the simulated forging process under the forging parameters. The static data includes the forging blank, the target forging, the CAD model of the new mold, the initial temperature of the forging, and the preheating temperature of the mold; In this step, the process of constructing the rigid-plastic finite element simulation model is as follows: Geometric model: Import CAD models of forging blanks, target forgings, and new molds, simplify non-critical features, and define forging directions consistent with reality; Material Model: For the forging, a rigid-plastic material model is selected, and the flow stress constitutive curve obtained from the hot tensile test is imported; for the die, a rigid material model is selected, and parameters such as elastic modulus and thermal conductivity are set. Boundary conditions: Set the initial temperature of the forging and the preheating temperature of the die; set the die pressing speed and friction coefficient, and select a shear friction model suitable for high-temperature forging; Meshing and Solving: The forging uses a tetrahedral element adaptive mesh with an initial mesh size of 5mm, and mesh remapping is enabled; the mold uses a rigid mesh with a size of 10mm; the rigid-plastic finite element solver is selected, and the convergence accuracy and iteration steps are set. A rigid-plastic finite element simulation model is constructed based on the geometric model, material model, boundary conditions, mesh, and solution. The process for assessing the defect risk of the forging during the simulated forging process under forging parameters is as follows: Set the parameters to be optimized, including forging temperature, die pressing speed, and friction coefficient. In Deform-3D, enable batch processing mode, and automatically modify the forging temperature, pressing speed, and friction coefficient by writing a parameterized script file. Run the simulation in sequence and output the result file. The results file includes the cumulative equivalent strain of the elements, the peak equivalent stress of the elements, the coordinates of the element nodes, and the final volume of the forging. Based on any given cell, traverse all adjacent cells of the current cell, and combine the cell node coordinates using the formula... The strain gradient between element i and its adjacent element j was calculated. , This represents the cumulative equivalent variable value of unit i. This represents the cumulative equivalent variable value of unit j. The center distance between element i and element j is calculated using the element node coordinates. The element strain gradient is obtained by summing the strain gradients between the current element and all its neighboring elements and taking the average value. The strain gradient limit of an element is set by a person skilled in the art based on historical experience. If the strain gradient of an element is greater than the strain gradient limit, the corresponding element is recorded as an element with excessive strain gradient. If the strain gradient of an element is less than or equal to the element strain gradient limit, then the corresponding element is recorded as a normal strain gradient element. The peak value of equivalent stress of a unit is set by a person skilled in the art. If the peak value of equivalent stress of a unit is greater than the peak value of equivalent stress of a unit, the corresponding unit is recorded as a stress-over-limit unit. If the peak value of the equivalent stress of an element is less than or equal to the limit of the peak value of the equivalent stress of an element, then the corresponding element is recorded as a stress-normal element. The percentage of units with strain gradient exceeding the limit in all units is recorded as the strain gradient exceeding the limit ratio. The percentage of units with stress exceeding the standard is calculated and recorded as the stress exceeding standard ratio. The absolute value of the difference between the measured target volume of the mold cavity and the final volume of the forging is then taken and compared with the measured target volume of the mold cavity to obtain the percentage of unfilled volume. The defect risk coefficient is obtained by multiplying the strain gradient excess ratio, stress excess ratio, and unfilled volume ratio. It should be noted that the defect risk coefficient is a quantitative index constructed based on the original data of Deform-3D rigid-plastic finite element simulation. It represents the comprehensive risk level of three typical defects—cracks, folds, and incomplete filling—that occur during the forming process of forgings under specific combinations of forging parameters. The risk of cracks is represented by the proportion of stress-exceeding elements, the risk of folds by the proportion of strain gradient-exceeding elements, and the risk of incomplete filling by the proportion of unfilled volume. The defect risk coefficient achieves the normalization and integration of multiple types of defect risks and is a key bridge connecting the simulation physical field data and the actual pass rate of forgings. It provides an intuitive and effective basis for quantitatively evaluating the forming quality of forgings and selecting the optimal simulation parameters. If the defect risk coefficient is greater than or equal to the defect risk coefficient threshold, it indicates that the forging has a high defect risk in the simulated forging process under the forging parameters, and the corresponding forging parameters are recorded as high-risk parameters. If the defect risk coefficient is less than the defect risk coefficient threshold, it means that the defect risk of the forging is low during the simulated forging process under the forging parameters, and the corresponding forging parameters are recorded as low-risk parameters. Step 2: If the defect risk is low, extract the defect risk coefficient corresponding to the low-risk parameters and determine the optimal simulation parameters; All the defect risk coefficients corresponding to the low-risk parameters are integrated into a defect risk coefficient sequence in ascending order. The low-risk parameter corresponding to the first defect risk coefficient in the sequence is extracted as the optimal parameter for simulation. Step 3: Conduct test runs on the forgings based on the optimal simulation parameters, preset the test run period, and determine whether the forging pass rate is abnormal by performing quantitative deviation analysis on the actual pass rate of the forgings during the test run period. The process parameters of the production equipment are strictly calibrated to the optimal parameters for simulation, including forging temperature, die pressing speed, and friction coefficient. Among these, forging temperature and die pressing speed need to be monitored in real time by the equipment's sensors. Referring to the defect assessment types in the simulation, which include cracks, folds, and incomplete filling, and in conjunction with actual production quality standards, the sampled samples are inspected: ultrasonic testing is used to detect internal cracks; 3D scanning is used, combined with visual inspection, to determine folds and incomplete filling defects; and the dimensions are compared with the target forging dimensions, and forgings with out-of-tolerance dimensions are statistically analyzed. Forgings with any of the three defect assessment types (cracks, folds, or incomplete filling) that exceed the tolerance are judged as unqualified samples. The number of non-conforming samples during the test run is obtained. The difference between the total number of test samples and the number of non-conforming samples during the test run is calculated to obtain the number of qualified samples. The ratio of the number of qualified samples to the total number of test samples is calculated to obtain the pass rate. If the pass rate is greater than or equal to the pass rate threshold, it indicates that the pass rate of forgings is normal. If the pass rate is less than the pass rate threshold, it indicates that the forging pass rate is abnormal. Step 4: If the pass rate is abnormal, analyze the actual mold wear, combine the mold wear amount with the surface roughness increment, establish a quantitative correction model, correct the mold friction coefficient, determine the actual optimal parameters, and improve the forging manufacturing pass rate. The mold is disassembled from the production equipment, impurities on the cavity surface are removed, and after drying, it is placed on the inspection platform. The mold cavity is scanned in full size using a 3D laser scanning device to generate an STL model of the worn mold. This model is then compared with the CAD model of the new mold to obtain the cavity volume of the worn area. The difference between this model and the target cavity volume is then calculated, and the absolute value is taken to obtain the mold wear amount. The surface roughness of the worn area is detected by a surface roughness meter, and the difference between the surface roughness and the initial roughness of the new mold is calculated to obtain the surface roughness increment. The correlation coefficient between mold wear and yield rate was calculated using the Spearman correlation coefficient formula, and the absolute value was then taken as the wear correlation value. The abnormal correlation value is obtained by subtracting the wear-related value from 1 and taking the absolute value. If the abnormal correlation value is greater than or equal to the abnormal correlation threshold, it means that the die wear is not the cause of the abnormal pass rate. Other factors affecting the abnormal pass rate need to be analyzed. Other factors affecting the abnormal pass rate include, but are not limited to, fluctuations in die pressing speed and forging temperature. If the abnormal correlation value is less than the abnormal correlation threshold, it indicates that mold wear is the cause of the abnormal pass rate. Based on the premise that mold wear is a cause of abnormal yield rates, a quantitative correction model is established by combining mold wear amount and surface roughness increment, using the formula: The corrected friction coefficient was calculated. In the formula, Indicates the increase in surface roughness. This indicates the initial surface roughness of the new mold. , For correction factor, Indicates the amount of mold wear. Indicates the target volume of the mold cavity; It should be noted that the correction factor is determined based on the matching type of the forging material and the die material. For example, if the forging is carbon steel and the die is H13 steel, =0.7, =0.4, calibrated through small-batch testing; In Deform-3D, the friction coefficient of the original simulation model is updated to the corrected friction coefficient. The forging temperature and pressing speed are fixed, the simulation is rerun, and the optimal parameters corresponding to the corrected minimum defect risk coefficient are extracted as the actual optimal parameters. The technical solution of this invention is as follows: Static data of the forging under current working conditions is collected; a rigid-plastic finite element simulation model is constructed based on the Deform-3D rigid-plastic finite element algorithm to simulate the influence of different forging parameters on the forming quality of the forging; the defect risk of the forging during the simulated forging process is evaluated under the forging parameters; if the defect risk is low, the defect risk coefficient corresponding to the low-risk parameter is extracted to determine the optimal simulation parameters; the forging is tested based on the optimal simulation parameters, a test run is preset, and the actual pass rate of the forging during the test run is quantitatively analyzed to determine whether the pass rate is abnormal; if the pass rate is abnormal, the actual die wear is analyzed, and a quantitative correction model is established by combining the die wear amount and surface roughness increment to correct the die friction coefficient, determine the actual optimal parameters, and improve the forging manufacturing pass rate; this invention integrates Deform-3D... Rigid-plastic finite element simulation and actual experimental verification enabled precise selection and dynamic correction of forging parameters: First, a simulation model was constructed using static data. Through parametric batch processing and quantitative assessment of defect risk coefficients, high- and low-risk parameters were efficiently distinguished, and the optimal simulation parameters were selected, avoiding the blindness of traditional trial-and-error methods and reducing trial production costs. Then, the actual adaptability of the simulation parameters was verified through experimental operation, and the comprehensiveness of the pass rate assessment was ensured by combining full-project inspection. For abnormal pass rate situations, a quantitative correction model was established based on die wear and surface roughness increments to accurately correct the friction coefficient and obtain the actual optimal parameters, effectively solving quality problems caused by die wear. The entire process achieved deep integration of simulation data and actual production, normalized and integrated multiple types of defect risks, improved the scientific nature and intuitiveness of forging quality assessment, and ultimately significantly improved the pass rate and production stability of forging manufacturing.
[0020] Example 2 Based on the same inventive concept as the method for improving and optimizing a forging manufacturing process in the foregoing embodiments, such as Figure 2 As shown, this application provides a system for improving and optimizing the forging manufacturing process, wherein the system specifically includes: Defect Risk Assessment Module: Static data of forgings under current working conditions is collected, and a rigid-plastic finite element simulation model is constructed based on the Deform-3D rigid-plastic finite element algorithm to simulate the influence of different forging parameters on the forming quality of forgings and assess the defect risk of forgings in the simulated forging process under forging parameters. Simulation optimal parameter determination module: If the defect risk is low, extract the defect risk coefficient corresponding to the low-risk parameters to determine the simulation optimal parameters; Test run analysis module: Based on the optimal simulation parameters, the forging is tested and run. The test run period is preset. By performing quantitative deviation analysis on the actual pass rate of the forging during the test run period, it is determined whether the pass rate of the forging is abnormal. Wear Correction Module: If the pass rate is abnormal, the actual mold wear is analyzed, and a quantitative correction model is established by combining the mold wear amount and the surface roughness increment. The mold friction coefficient is corrected, the actual optimal parameters are determined, and the pass rate of forging manufacturing is improved.
[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for improving and optimizing forging manufacturing processes, characterized in that: include: Static data of the forging under the current working condition is collected. Based on the rigid-plastic finite element algorithm of Deform-3D, a rigid-plastic finite element simulation model is constructed to simulate the influence of different forging parameters on the forming quality of the forging and to evaluate the defect risk of the forging in the simulated forging process under the forging parameters. If the defect risk is low, extract the defect risk coefficient corresponding to the low-risk parameters to determine the optimal simulation parameters; The forgings were tested based on the optimal simulation parameters. The test run period was preset. The actual pass rate of the forgings during the test run period was quantitatively analyzed to determine whether the pass rate of the forgings was abnormal. If the pass rate is abnormal, analyze the actual mold wear, combine the mold wear amount with the surface roughness increment, establish a quantitative correction model, correct the mold friction coefficient, determine the actual optimal parameters, and improve the pass rate of forging manufacturing.
2. The method for improving and optimizing forging manufacturing process according to claim 1, characterized in that: The process of constructing the rigid-plastic finite element simulation model is as follows: Geometric Model: Import CAD models of the forging blank, target forging, and new mold; Material Model: For the forging, a rigid-plastic material model is selected, and the flow stress constitutive curve obtained from the hot tensile test is imported; for the die, a rigid material model is selected, and parameters such as elastic modulus and thermal conductivity are set. Boundary conditions: Set the initial temperature of the forging and the preheating temperature of the die; set the die pressing speed and friction coefficient, and select a shear friction model suitable for high-temperature forging; Meshing and Solving: Tetrahedral elements are used for the forgings and rigid meshes are used for the dies. A rigid-plastic finite element solver is selected, and the convergence accuracy and iteration steps are set. A rigid-plastic finite element simulation model is constructed based on the geometric model, material model, boundary conditions, mesh, and solution.
3. The method for improving and optimizing the forging manufacturing process according to claim 1, characterized in that: The process for assessing the defect risk of the forging during the simulated forging process under forging parameters is as follows: Set the parameters to be optimized, including forging temperature, die pressing speed, and friction coefficient. In Deform-3D, enable batch processing mode, and automatically modify the forging temperature, pressing speed, and friction coefficient by writing a parameterized script file. Run the simulation in sequence and output the result file. The results file includes the cumulative equivalent strain of the elements, the peak equivalent stress of the elements, the coordinates of the element nodes, and the final volume of the forging. By analyzing the data in the results file, the strain gradient exceeding ratio, stress exceeding ratio, and unfilled volume ratio are determined. The defect risk coefficient is obtained by multiplying the strain gradient exceeding ratio, stress exceeding ratio, and unfilled volume ratio. If the defect risk coefficient is greater than or equal to the defect risk coefficient threshold, it indicates that the defect risk of the forging is high during the simulated forging process under the forging parameters, and the corresponding forging parameters are recorded as high-risk parameters; otherwise, it indicates that the defect risk of the forging is low during the simulated forging process under the forging parameters, and the corresponding forging parameters are recorded as low-risk parameters.
4. The method for improving and optimizing the forging manufacturing process according to claim 3, characterized in that: The process for determining the strain gradient exceedance ratio is as follows: Based on any given element, traverse all adjacent elements of the current element, and calculate the strain gradient between the element and its adjacent elements by combining the element node coordinates. The element strain gradient is obtained by summing the strain gradients between the current element and all its neighboring elements and taking the average value. If the strain gradient of an element is greater than the element strain gradient limit, the corresponding element is recorded as an element with excessive strain gradient. The percentage of units with strain gradient exceeding the standard is counted and denoted as the strain gradient exceeding the standard ratio.
5. The method for improving and optimizing the forging manufacturing process according to claim 3, characterized in that: The process for determining the stress exceedance ratio is as follows: If the peak value of the equivalent stress of an element is greater than the limit of the peak value of the equivalent stress of an element, the corresponding element is recorded as an element with excessive stress. The percentage of units with stress exceeding the standard is counted and recorded as the stress exceeding the standard ratio.
6. The method for improving and optimizing the forging manufacturing process according to claim 3, characterized in that: The process for determining the percentage of unfilled volume is as follows: The absolute value of the difference between the measured target volume of the mold cavity and the final volume of the forging is then taken and compared with the measured target volume of the mold cavity to obtain the percentage of unfilled volume.
7. The method for improving and optimizing the forging manufacturing process according to claim 3, characterized in that: The process for determining the optimal simulation parameters is as follows: All the defect risk coefficients corresponding to the low-risk parameters are integrated into a defect risk coefficient sequence in ascending order. The low-risk parameter corresponding to the first defect risk coefficient in the sequence is extracted as the optimal parameter for simulation.
8. The method for improving and optimizing the forging manufacturing process according to claim 7, characterized in that: The process for determining whether the forging pass rate is abnormal is as follows: The process parameters of the production equipment were strictly calibrated to the optimal parameters in the simulation, including forging temperature, die pressing speed, and friction coefficient. Referring to the defect assessment types in the simulation, which include cracks, folds, and incomplete filling, and in conjunction with actual production quality standards, the sampled samples were inspected: ultrasonic testing was used to detect internal cracks; 3D scanning was used, combined with visual inspection, to determine folds and incomplete filling defects. Compared with the target forging size, forgings with out-of-tolerance dimensions are statistically analyzed simultaneously. Forgings with any of the following defect assessment types (cracks, folds, incomplete filling) that exceed the tolerance are judged as unqualified samples. The number of non-conforming samples during the test run is obtained. The difference between the total number of test samples and the number of non-conforming samples during the test run is calculated to obtain the number of qualified samples. The ratio of the number of qualified samples to the total number of test samples is calculated to obtain the pass rate. If the pass rate is greater than or equal to the pass rate threshold, it indicates that the forging pass rate is normal; otherwise, it indicates that the forging pass rate is abnormal.
9. The method for improving and optimizing a forging manufacturing process according to claim 8, characterized in that: The process of establishing the quantitative correction model is as follows: The mold is disassembled from the production equipment, impurities on the cavity surface are removed, and after drying, it is placed on the inspection platform. The mold cavity is scanned in full size using a 3D laser scanning device to generate an STL model of the worn mold. This model is then compared with the CAD model of the new mold to obtain the cavity volume of the worn area. The difference between this model and the target cavity volume is then calculated, and the absolute value is taken to obtain the mold wear amount. The surface roughness of the worn area is detected by a surface roughness meter, and the difference between the surface roughness and the initial roughness of the new mold is calculated to obtain the surface roughness increment. The correlation coefficient between mold wear and yield rate was calculated using the Spearman correlation coefficient formula, and the absolute value was then taken as the wear correlation value. The abnormal correlation value is obtained by subtracting the wear-related value from 1 and taking the absolute value. If the abnormal correlation value is less than the abnormal correlation threshold, it indicates that mold wear is the cause of the abnormal pass rate. Since mold wear is the cause of abnormal yield, a quantitative correction model is established by combining the amount of mold wear with the increase in surface roughness.
10. The method for improving and optimizing a forging manufacturing process according to claim 9, characterized in that: The process for determining the actual optimal parameters is as follows: The corrected friction coefficient is calculated based on the quantization correction model; In Deform-3D, the friction coefficient of the original simulation model is updated to the corrected friction coefficient. The forging temperature and pressing speed are fixed, the simulation is rerun, and the optimal parameters corresponding to the corrected minimum defect risk coefficient are extracted as the actual optimal parameters.