Automobile bolt forming process optimization analysis method and device

Abstract

This application relates to the field of computer-aided process optimization technology, and particularly to a method and apparatus for optimizing and analyzing automotive bolt forming processes. The method includes: acquiring a dataset of process parameters for an automotive bolt forming task and optimization trend information for bolt forming; extracting trends from the fluctuation data of each parameter in the process parameter set to obtain parameter characteristic information of the process parameter set; determining parameter fluctuation information for each process parameter set based on the parameter characteristic information; performing optimization calculations based on the parameter fluctuation information and optimization requirements in the process parameter set to obtain a target process set; determining process influence information from the parameter information corresponding to the target process set; and calculating the optimal parameter combination for the bolt forming process based on the parameter adjustment range of the process influence information and optimization trend information. The method provided in this application can solve the problem of deviations in process optimization direction, which in turn affect product quality.

Description

Technical Field

[0001] This application belongs to the field of computer-aided process optimization technology, and in particular relates to a method and apparatus for optimizing and analyzing automotive bolt forming process. Background Technology

[0002] Automotive bolts are fasteners with external threads, typically used in conjunction with nuts or threaded holes to connect different components into a whole, and are subjected to various loads such as axial tension, shear, and torque during use.

[0003] Currently, the optimization analysis of automotive bolt forming processes often employs a combination of single parameter detection and manual experience judgment, or performs local optimization based on parameter changes at a single process node. However, in actual production environments, factors such as equipment vibration, batch-to-batch variations in raw materials, fluctuations in temperature and humidity, and uneven mold wear rates can easily cause meaningless small fluctuations in parameters at local process nodes. Traditional optimization analysis methods are prone to misjudging these parameter fluctuations as process optimization nodes, leading to deviations in the direction of process optimization and ultimately affecting product quality. Summary of the Invention

[0004] This application provides a method and apparatus for optimizing automotive bolt forming process, which can solve the problem that traditional optimization analysis methods are prone to misjudging parameter fluctuations caused by influencing factors in the production environment as process optimization nodes, leading to deviations in the direction of process optimization and thus affecting product quality.

[0005] In a first aspect, embodiments of this application provide a method for optimizing and analyzing the forming process of automotive bolts, including: Obtain the process parameter dataset and optimization trend information for the automotive bolt forming task; wherein, the process parameter dataset includes at least two process parameter sets divided according to the optimization trend information; the optimization trend information is used to indicate the optimization target of bolt forming; Trend extraction is performed on the fluctuation data of each parameter in the process parameter set to obtain the parameter feature information of the process parameter set; wherein, the fluctuation data is the numerical change data of the process parameter in the continuous production process; the trend extraction is used to analyze the fluctuation data to extract the change trend of the data; Based on the parameter feature information, parameter fluctuation information for each set of process parameters is determined; wherein, the parameter fluctuation information is used to reflect the impact of parameter fluctuations of the set of process parameters on bolt forming quality; Based on the parameter fluctuation information and optimization requirements in the process parameter set, optimization calculations are performed to obtain the target process set; wherein, the optimization calculations are used to determine the parameter set that meets the optimization threshold from multiple process parameter sets; the optimization requirements are quantitative standards set based on the optimization target and used to screen the process parameter set; The parameter information corresponding to the target process set is used to determine the process impact information; Based on the parameter adjustment range of the process influence information and the optimization trend information, the optimal parameter combination for the bolt forming process is calculated.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The method for optimizing and analyzing automotive bolt forming processes provided in this application acquires a dataset of process parameters for an automotive bolt forming task and optimization trend information for bolt forming. The process parameter dataset includes at least two sets of process parameters divided according to optimization trend information. Trend extraction is performed on the fluctuation data of each parameter in the process parameter sets to obtain parameter characteristic information. Based on the parameter characteristic information, parameter fluctuation information for each process parameter set is determined. This parameter fluctuation information reflects the impact of parameter fluctuations on bolt forming quality. Optimization calculations are performed based on the parameter fluctuation information and optimization requirements in the process parameter sets to obtain a target process set. These optimization calculations determine the parameter set that meets the optimization threshold from multiple process parameter sets. The parameter information corresponding to the target process set is used to determine process influence information. Based on the parameter control range of the process influence information and optimization trend information, the optimal parameter combination for the bolt forming process is calculated. The method provided in this application divides at least two sets of process parameters according to optimization trend information, which can elevate the dimension of process optimization analysis from a single parameter to multiple levels. Then, based on parameter characteristic information, the parameter fluctuation information of each process parameter set is determined, which can quantify the degree of influence of each process parameter set on bolt forming quality. The parameter fluctuation information of each process parameter set is used as the basis for selecting key optimization nodes, rather than relying on the independent fluctuation of a single parameter. This makes the selection criteria for process optimization nodes more aligned with the process characteristics of multi-process collaborative forming of automotive bolts, thereby identifying the process parameter sets that truly affect bolt forming quality and avoiding deviations in the optimization direction. Then, based on the parameter adjustment range of process influence information and optimization trend information, the optimal parameter combination of the bolt forming process is calculated, which can achieve precise optimization of bolt forming process parameters and help improve product quality.

[0007] In one possible implementation of the first aspect, obtaining the process parameter dataset and optimization trend information for the automotive bolt forming task includes: Obtain default forming process parameters and standard forming process parameters; wherein, the standard forming process parameters include parameter range information for key process stages of bolt forming and optimization trend information for bolt forming; The default molding process parameters are adjusted based on the standard molding process parameters to obtain the adjusted process parameters. Based on the optimization trend information and the parameter range information, the parameter range information corresponding to the parameters of the key process stage of bolt forming in the adjusted process parameters is adjusted to obtain the parameter set to be optimized; wherein, the parameter set to be optimized includes the process parameter dataset.

[0008] In one possible implementation of the first aspect, the set of parameters to be optimized includes multiple sets of parameters. The step of adjusting the parameter range information corresponding to the parameters of the key bolt forming process stage in the adjusted process parameters, based on the optimization trend information and the parameter range information, to obtain the set of parameters to be optimized includes: Based on the optimization trend information and the parameter range information, the parameter range information corresponding to the parameters in the key stage of bolt forming in the adjustment process parameters is adjusted to obtain the first parameter set; Multiple process parameter adjustment values ​​of preset parameter adjustment data are obtained, and the first parameter set is fine-tuned according to the multiple process parameter adjustment values ​​to obtain multiple fine-tuning parameter sets; The set of fine-tuning parameters corresponding to different process parameter adjustment values ​​is taken as the set of parameters to be optimized.

[0009] In one possible implementation of the first aspect, before determining the process influence information from the parameter information corresponding to the target process set, the method further includes: Based on the parameter fluctuation information, at least two target process sets are determined from the multiple process parameter sets corresponding to each set of parameters to be optimized. Based on the parameter fluctuation information of the at least two target process sets, determine the fluctuation data of each set of parameters to be optimized; Based on the fluctuation data, a target set of parameters to be optimized is determined from multiple sets of parameters to be optimized. The target set of parameters to be optimized is used to determine a process parameter group; wherein, the process parameter group is used to reflect the parameter information corresponding to the target process set.

[0010] In one possible implementation of the first aspect, the optimization calculation based on the parameter fluctuation information and optimization requirements in the process parameter set to obtain the target process set includes: Based on the parameter fluctuation information and the number of targets set in the optimization requirements, a target process set corresponding to the number of targets set in the optimization requirements is determined from the set of process parameters.

[0011] In one possible implementation of the first aspect, the process parameter dataset includes multiple datasets, and the method further includes: From multiple datasets of the process parameter dataset, a second set of parameters whose parameter fluctuation information meets the target requirements is determined; The process impact information is determined based on the proportion of each of the second parameter sets in the process parameter dataset.

[0012] In one possible implementation of the first aspect, determining the process influence information based on the parameter proportion of each of the second parameter sets in the process parameter dataset includes: Based on the proportion of parameters in each of the second parameter sets in the process parameter dataset, determine the parameter influence weight corresponding to the second parameter set; The process influence information is obtained by determining the parameter influence weight corresponding to the second parameter set in each dataset of the process parameter dataset.

[0013] In one possible implementation of the first aspect, when a parameter is missing in the process parameter dataset, the process parameter dataset is supplemented with parameters.

[0014] Secondly, embodiments of this application provide an automotive bolt forming process optimization and analysis device, applied to a wire drawing device, for implementing the automotive bolt forming process optimization and analysis method described in any one of the first aspects above. The automotive bolt forming process optimization and analysis device includes: An acquisition unit is used to acquire a process parameter dataset and optimization trend information for an automotive bolt forming task; wherein the process parameter dataset includes at least two process parameter sets divided according to the optimization trend information; the optimization trend information is used to indicate the optimization target of bolt forming. An extraction unit is used to extract trends from the fluctuation data of each parameter in the process parameter set to obtain parameter feature information of the process parameter set; wherein, the fluctuation data is the numerical change data of the process parameters during continuous production; the trend extraction is used to analyze the fluctuation data to extract the data change trend; A determining unit is configured to determine parameter fluctuation information for each set of process parameters based on the parameter feature information; wherein the parameter fluctuation information is used to reflect the impact of parameter fluctuations of the set of process parameters on bolt forming quality; The calculation unit is used to perform optimization calculations based on the parameter fluctuation information and optimization requirements in the process parameter set to obtain a target process set; wherein, the optimization calculation is used to determine the parameter set that meets the optimization threshold from multiple process parameter sets; the optimization requirements are quantitative standards set based on the optimization target and used to screen the process parameter set; A generation unit is used to determine process impact information from the parameter information corresponding to the target process set; The control unit is used to calculate the optimal parameter combination for the bolt forming process based on the parameter adjustment range of the process influence information and the optimization trend information.

[0015] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the automotive bolt forming process optimization analysis method described in any one of the first aspects above.

[0016] It is understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

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

[0018] Figure 1 This is a flowchart illustrating an embodiment of the automotive bolt forming process optimization analysis method provided in this application; Figure 2 This is a timing diagram of an embodiment of the automotive bolt forming process optimization analysis method provided in this application; Figure 3 This is a schematic diagram of the structure of an automotive bolt provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of the automotive bolt forming process optimization and analysis device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if the described condition or event is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once the described condition or event is detected," or "in response to the detection of the described condition or event."

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] In related technologies, automotive bolts are fasteners with external threads, typically used in conjunction with nuts or threaded holes to connect different components into a whole, and subjected to various loads such as axial tension, shear, and torque during use.

[0026] Currently, the optimization analysis of automotive bolt forming processes often employs a combination of single parameter detection and manual experience judgment, or performs local optimization based on parameter changes at a single process node. However, in actual production environments, factors such as equipment vibration, batch-to-batch variations in raw materials, fluctuations in temperature and humidity, and uneven mold wear rates can easily cause meaningless small fluctuations in parameters at local process nodes. Traditional optimization analysis methods are prone to misjudging these parameter fluctuations as process optimization nodes (for example, determining the fluctuation threshold of a single parameter and selecting parameter nodes exceeding the threshold as process optimization points), leading to deviations in the direction of process optimization and consequently affecting product quality.

[0027] To address the aforementioned issues, this application provides a method and apparatus for optimizing automotive bolt forming processes.

[0028] This method involves acquiring a dataset of process parameters for automotive bolt forming and optimization trend information for bolt forming. The process parameter dataset includes at least two sets of process parameters divided according to optimization trend information. Trend extraction is performed on the fluctuation data of each parameter in the process parameter sets to obtain parameter feature information. Based on the parameter feature information, parameter fluctuation information for each process parameter set is determined. This parameter fluctuation information reflects the impact of parameter fluctuations on bolt forming quality. Optimization calculations are performed based on the parameter fluctuation information and optimization requirements in the process parameter sets to obtain a target process set. These optimization calculations determine the parameter set that meets the optimization threshold from multiple process parameter sets. The parameter information corresponding to the target process set is used to determine process influence information. Based on the parameter control range of the process influence information and optimization trend information, the optimal parameter combination for the bolt forming process is calculated. The method provided in this application divides at least two sets of process parameters according to optimization trend information, which can elevate the dimension of process optimization analysis from a single parameter to multiple levels. Then, based on parameter characteristic information, the parameter fluctuation information of each process parameter set is determined, which can quantify the degree of influence of each process parameter set on bolt forming quality. The parameter fluctuation information of each process parameter set is used as the basis for selecting key optimization nodes, rather than relying on the independent fluctuation of a single parameter. This makes the selection criteria for process optimization nodes more aligned with the process characteristics of multi-process collaborative forming of automotive bolts, thereby identifying the process parameter sets that truly affect bolt forming quality and avoiding deviations in the optimization direction. Then, based on the parameter adjustment range of process influence information and optimization trend information, the optimal parameter combination of the bolt forming process is calculated, which can achieve precise optimization of bolt forming process parameters and help improve product quality.

[0029] The automotive bolt forming process optimization analysis method provided in this application embodiment can be applied to electronic devices. In this case, the electronic device is the executing subject of the automotive bolt forming process optimization analysis method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0030] For example, electronic devices can be PLCs, microcontrollers, mobile phones, tablets, laptops, ultra-mobile personal computers (UMPCs), netbooks, desktop computers, computing devices, or computers connected to wireless modems, laptops, handheld communication devices, handheld computing devices, etc.

[0031] To better understand the automotive bolt forming process optimization analysis method provided in the embodiments of this application, the specific implementation process of the automotive bolt forming process optimization analysis method provided in the embodiments of this application will be described by way of example below.

[0032] Figure 1This paper presents a schematic flowchart of an automotive bolt forming process optimization analysis method provided in an embodiment of this application. The automotive bolt forming process optimization analysis method includes: S100: Obtain the process parameter dataset and optimization trend information for the automotive bolt forming task. The process parameter dataset includes at least two sets of process parameters divided according to the optimization trend information. The optimization trend information is used to indicate the optimization objective for bolt forming.

[0033] It can be understood that the process parameter dataset can be a collection of all process parameters involved in the entire process of processing automotive bolts from raw materials (such as high-strength steel) into finished bolts. Examples include cold heading, thread rolling, heat treatment, and straightening processes. The process parameters corresponding to each process are included in the process parameter dataset. Specifically, the process parameter dataset can include parameters such as cold heading pressure, cold heading speed, cold heading temperature, thread rolling speed, thread rolling pressure, heat treatment temperature, heat treatment time, and straightening force. Optimization trend information is used to indicate the optimization goals of bolt forming. For example, these can be predetermined based on production needs. Optimization trend information can include bolt forming dimensional accuracy optimization (such as bolt shank diameter deviation and thread pitch accuracy), mechanical strength optimization (such as tensile strength and yield strength), surface defect optimization (such as cracks and burrs), and production efficiency optimization (such as forming cycle time). The process parameter dataset includes at least two process parameter sets divided according to optimization trend information. The process parameter dataset of the whole process can be divided into multiple independent and targeted parameter sets according to the requirements corresponding to the optimization objectives in the optimization trend information. Each parameter set corresponds to a subdivision dimension of the optimization objective in the optimization trend information, or corresponds to a key forming process.

[0034] For example, the process parameter data of the entire process of a continuous production batch (such as 100 batches) can be collected in real time through sensors and data acquisition terminals on the automotive bolt forming production line to obtain a process parameter dataset.

[0035] For example, if the optimization trend information is dimensional accuracy + mechanical strength, then the process parameter dataset can be divided into at least two parameter sets. For instance, the division can be done in two ways: the first is by process, dividing it into a cold heading process parameter set (including cold heading pressure, cold heading speed, and cold heading temperature), a thread rolling process parameter set (including thread rolling speed, thread rolling pressure, and thread depth), and a heat treatment process parameter set (including heat treatment temperature, heat treatment time, and cooling rate); the second is by the subdivision of the optimization objective, dividing it into a dimensional accuracy-related parameter set (including cold heading pressure, thread rolling speed, and thread depth) and a mechanical strength-related parameter set (including heat treatment temperature, heat treatment time, and cold heading temperature). Regardless of the division method used, the parameters within each parameter set must be directly related to the optimization objective, and there should be no duplicate parameters between parameter sets to avoid parameter redundancy.

[0036] In one possible implementation, S100, the process parameter dataset and optimization trend information of the automotive bolt forming task are obtained, including: S110, obtain the default forming process parameters and the standard forming process parameters. The standard forming process parameters include parameter range information for key process stages of bolt forming and optimization trend information for bolt forming.

[0037] It is understandable that default forming process parameters can be a set of process parameters currently in use or to be optimized on the automotive bolt forming production line. These parameters can originate from sources such as production equipment settings, parameter data from historical production records, and default parameters input based on manual experience. Standard forming process parameters can be standardized process parameters established in advance based on the product design requirements, material properties (such as forming requirements for high-strength steel), industry standards (such as automotive bolt forming quality specifications), and the company's production experience. For example, the company can access its pre-established automotive bolt standard process database and match the corresponding standard forming process parameters according to the bolt model currently being produced. Parameter range information can be the standardized allowable fluctuation range of each process parameter under each critical process stage.

[0038] S120, adjusts the default molding process parameters based on the standard molding process parameters to obtain the adjusted process parameters.

[0039] It is understandable that parameter adjustment can be done by comparing and correcting the original, potentially biased, default molding process parameters with the benchmark parameters in the standard molding process parameters, eliminating obviously abnormal, out-of-range, or unsuitable parameter values, so that the parameters as a whole come into a reasonable, analyzable, and optimizable range.

[0040] For example, for values ​​in the default molding process parameters that exceed the range specified in the standard molding process parameters, they are corrected to the nearest boundary value of the standard range. For instance, if a set of cold heading pressure records in the default molding process parameters is 115MPa, while the upper limit of cold heading pressure in the standard molding process parameters is 100MPa, this value exceeds the standard range and is considered abnormal data, so it is adjusted to 100MPa (the standard upper limit).

[0041] S130, based on optimization trend information and parameter range information, adjust the parameter range information corresponding to the parameters of the key process stages of bolt forming in the adjustment process parameters to obtain the parameter set to be optimized. The parameter set to be optimized includes the process parameter dataset.

[0042] It is understandable that adjusting the parameter range information corresponding to the parameters of the key process stage of bolt forming in the process parameters adjustment can be an adjustment of the parameter range information of the process parameters.

[0043] For example, based on the optimization trend information, the range of parameters that most significantly affect the optimization objective in the optimization trend information is narrowed. For instance, the cold heading pressure range is narrowed from 80MPa to 100MPa to 85MPa to 95MPa. All parameter combinations and corresponding parameter ranges obtained after the above adjustments constitute the set of process parameters to be optimized. This setting can solve the problems of inaccurate default molding process parameters and inconsistent standards, providing a high-quality and reliable data foundation for subsequent optimization and analysis.

[0044] In one possible implementation, the set of parameters to be optimized includes multiple parameter sets. S130, based on optimization trend information and parameter range information, the parameter range information corresponding to the parameters in the key process stage of bolt forming is adjusted to obtain the set of parameters to be optimized, including: S131, based on the optimization trend information and parameter range information, adjust the parameter range information corresponding to the parameters of the key stage of bolt forming in the adjustment process parameters to obtain the first parameter set.

[0045] For example, the parameter ranges for key process stages (such as cold heading, wire rolling, and heat treatment) can be adjusted. These adjustments may include parameters such as cold heading pressure, cold heading temperature, wire rolling speed, wire rolling pressure, heat treatment temperature, and heat treatment time. For instance, based on the optimization objective of the optimization trend information, the parameter range most significantly affecting that objective can be narrowed to improve control accuracy. As another example, based on parameter range information, a parameter range that still leads to defects can be eliminated. All parameter combinations and corresponding parameter ranges obtained after the above adjustments constitute the first parameter set.

[0046] S132, obtain multiple process parameter adjustment values ​​of preset parameter adjustment data, and fine-tune the first parameter set according to the multiple process parameter adjustment values ​​to obtain multiple fine-tuning parameter sets.

[0047] It is understandable that the preset parameter adjustment data can be the maximum range within which parameters can be fine-tuned, pre-set based on the adjustment accuracy of the automotive bolt forming equipment, the fluctuation characteristics of raw materials, and the range of changes in the production environment. The preset parameter adjustment data includes multiple process parameter adjustment values, used to fine-tune parameters within all parameter combinations obtained after adjustment. Fine-tuning the first parameter set indicates that, based on each process parameter adjustment value, the key process parameters of the first parameter set are slightly adjusted. The adjustment targets only the parameters of the key process stages; parameters of non-key process stages remain unchanged.

[0048] For example, if the process parameter adjustment value is cold heading pressure -3MPa, and the cold heading pressure in the first parameter set is 90MPa, then the parameter after fine-tuning the first parameter set is 87MPa.

[0049] S133, the set of fine-tuning parameters corresponding to different process parameter adjustment values ​​is taken as the set of parameters to be optimized.

[0050] For example, the set of fine-tuned parameters and the corresponding parameter range can be obtained by fine-tuning the parameters.

[0051] This setup allows for fine-tuning of the first set of parameters to adapt to variations in the adjustment precision of the automotive bolt forming equipment, the fluctuation characteristics of raw materials, and the range of changes in the production environment. Meanwhile, multiple sets of parameters to be optimized can be adapted to different production conditions, providing more comprehensive data support for subsequent optimization analysis.

[0052] S200 extracts trends from the fluctuation data of each parameter in the process parameter set to obtain parameter feature information. The fluctuation data refers to the numerical changes of process parameters during continuous production. Trend extraction is used to analyze the fluctuation data and extract its changing trends.

[0053] It can be understood that fluctuation data can be the numerical changes of process parameters during continuous production. For example, the cold heading pressure in the cold heading process parameter set might have 100 values ​​across 100 batches of production. These values ​​would fluctuate due to factors such as raw material differences, equipment wear, and changes in ambient temperature. Trend extraction can be achieved by using mathematical algorithms to analyze the fluctuation data and extract features such as the trend, amplitude, and dispersion of the data, transforming the discrete fluctuation data into quantifiable feature indicators. For example, a combination of time series analysis algorithms and statistical analysis algorithms can be used to achieve trend extraction. Parameter feature information refers to the feature indicators corresponding to each set of process parameters. It consists of the trend characteristics of each parameter in that set, including but not limited to: parameter fluctuation mean, parameter fluctuation variance, parameter fluctuation extreme values ​​(maximum and minimum values), parameter fluctuation slope (trend), and parameter fluctuation frequency (number of fluctuations). Ultimately, the parameter feature information of each set of process parameters is a summary of the trend characteristics of all parameters in that set, used to subsequently measure the overall impact of that parameter set on the molding quality.

[0054] For example, for each parameter in each process parameter set (such as the cold heading pressure in the cold heading process parameter set), a statistical analysis algorithm is used to calculate its trend characteristics. For instance, the mean of parameter fluctuation is calculated: this is the average value of the parameter in consecutive production batches, reflecting the overall control level of the parameter. The closer the mean is to the standard parameter value, the more stable the parameter control. The variance of parameter fluctuation is calculated to reflect the dispersion of parameter fluctuation; the smaller the variance, the smaller the parameter fluctuation and the more stable the forming quality. The extreme values ​​of parameter fluctuation are calculated to determine the maximum and minimum values ​​of parameter fluctuation and to judge whether the parameter exceeds the controllable range of the equipment or the standard parameter range. The slope of parameter fluctuation is calculated: a linear regression algorithm is used to fit the parameter fluctuation data to obtain the slope of the fitted line. A positive slope indicates an upward trend, and a negative slope indicates a downward trend. The larger the absolute value of the slope, the more obvious the parameter change trend. The frequency of parameter fluctuation is calculated: the number of times the parameter fluctuation exceeds the preset fluctuation range is counted. The higher the frequency, the worse the parameter control stability. The calculated trend characteristics of all parameters in each process parameter set are summarized to form the parameter characteristic information of that process parameter set. For example, the parameter feature information of the cold heading process parameter set includes the mean, variance, extreme values, slope, and fluctuation frequency of the cold heading pressure, as well as five trend features of the cold heading speed and cold heading temperature, for a total of 15 feature indicators, to reflect the overall fluctuation characteristics of the cold heading process parameter set.

[0055] S300 determines the parameter fluctuation information for each process parameter set based on parameter characteristic information. This parameter fluctuation information reflects the impact of parameter fluctuations in the process parameter set on bolt forming quality.

[0056] It can be understood that parameter fluctuation information refers to a numerical index calculated by a preset algorithm based on the parameter characteristic information of the process parameter set, which is used to quantify the degree of influence of the parameter set on the bolt forming quality. The larger the value, the greater the influence of the parameter fluctuation of the process parameter set on the bolt forming quality; the smaller the value, the smaller the influence of the parameter fluctuation of the process parameter set on the bolt forming quality.

[0057] It should be noted that the calculation of parameter fluctuation information can be based on bolt forming quality data (such as dimensional deviations and mechanical strength test results) to establish a correlation between parameter characteristic information and quality indicators, so that the calculation results can truly reflect the logic of parameter fluctuation → quality impact. For example, if the larger the variance of parameter fluctuation in a certain process parameter set, the larger the corresponding bolt dimensional deviation, then the greater the parameter fluctuation information of that parameter set, indicating that its impact on dimensional accuracy is more significant.

[0058] For example, the parameter feature information of each process parameter set is used as the input variable, and the corresponding bolt forming quality index (such as dimensional deviation and tensile strength) is used as the output variable. A correlation model is constructed using a multiple linear regression algorithm. This correlation model is used to describe the linear relationship between parameter feature information and quality index.

[0059] For example, assuming the parameter feature information of the cold heading process parameter set includes three principal component features (F1, F2, F3), and the corresponding bolt size deviation is Y, the correlation model can be expressed as: Y = a1×F1 + a2×F2 + a3×F3 + b, where F1, F2, and F3 can be three features selected from the mean, variance, extreme values, slope, and fluctuation frequency of the parameter feature information; a1, a2, and a3 are regression coefficients; and b is a constant term. The larger the absolute value of the regression coefficient, the greater the influence of the corresponding principal component feature on the size deviation. Based on the above correlation model, the sum of the absolute values ​​of the regression coefficients corresponding to each process parameter set is extracted as the parameter fluctuation information of that process parameter set. The specific calculation formula is: G = Σ|ai| (i = 1, 2, ..., n), where G is the parameter fluctuation information, ai is the regression coefficient corresponding to each principal component feature of the process parameter set, and n is the number of principal component features.

[0060] For example, the regression coefficients of the cold heading process parameter set are a1=0.6, a2=0.3, and a3=0.1, so its parameter fluctuation information G=0.6+0.3+0.1=1.0; the regression coefficients of the thread rolling process parameter set are a1=0.4, a2=0.2, and a3=0.3, so its parameter fluctuation information G=0.4+0.2+0.3=0.9; and the regression coefficients of the heat treatment process parameter set are a1=0.2, a2=0.1, and a3=0.2, so its parameter fluctuation information G=0.2+0.1+0.2=0.5. Therefore, it can be seen that the cold heading process parameter set has the greatest impact on bolt forming quality, while the heat treatment process parameter set has the least impact.

[0061] S400 performs optimization calculations based on parameter fluctuation information and optimization requirements within the process parameter set to obtain the target process set. The optimization calculations are used to determine the parameter set that meets the optimization threshold from multiple process parameter sets. The optimization requirements are quantitative standards set based on the optimization objective and used to screen the parameter set.

[0062] It is understandable that optimization requirements can be quantitative standards used to screen parameter sets based on optimization objectives. For example, optimization requirements could be to sort parameter fluctuation information from largest to smallest and select a target set of process parameters. The number of targets can be set according to actual production needs, ensuring that the selected target parameter set has sufficient influence while avoiding excessive screening that would increase the complexity of subsequent analysis.

[0063] For example, the parameter fluctuation information of all process parameter sets is sorted in descending order. For instance, the order of the three process parameter sets is: cold heading process parameter set (G=1.0) > thread rolling process parameter set (G=0.9) > heat treatment process parameter set (G=0.5). From the sorting results, the target number of process parameter sets (such as the first two) are selected as the target process sets, namely the cold heading process parameter set and the thread rolling process parameter set. These two parameter sets have the greatest impact on bolt forming quality, and subsequent optimization will only target these two parameter sets, eliminating the heat treatment process parameter set with less impact, thus simplifying the optimization process.

[0064] Optionally, if there are multiple similar cases of parameter fluctuation information (such as the difference between the parameter fluctuation information of two parameter sets being less than 0.1), the number of targets can be appropriately increased, and parameter sets with similar parameter fluctuation information can be included in the target process set to avoid omitting potential key parameter sets.

[0065] S500 determines the process impact information by using the parameter information corresponding to the target process set.

[0066] It is understandable that the parameter information corresponding to the target process set can be the inherent attributes and fluctuation characteristics of each parameter in the target process set, as well as information on its correlation with bolt forming quality indicators. This can include the standard range, fluctuation variance, and correlation coefficient with quality indicators of the parameters. The process influence information can be the process parameters in the target process set that have the most significant impact on bolt forming quality and whose fluctuations have the greatest impact on quality stability.

[0067] For example, taking a defined set of cold heading process parameters (such as cold heading pressure, cold heading speed, and cold heading temperature) and a set of thread rolling process parameters (such as thread rolling speed, thread rolling pressure, and thread depth) as examples, parameter information is extracted, including: variance fluctuation and correlation coefficient with dimensional accuracy / mechanical strength. For example, the variance fluctuation can be calculated as follows: the average value of the 100 cold heading pressure data (denoted as μ) is calculated using the formula μ=(x1+x2+……+x100)÷100. Based on the mean μ, the variance fluctuation (denoted as σ²) is calculated using the variance calculation formula σ²=[(x1-μ)²+(x2-μ)²+……+(x100-μ)²]÷(n-1). For example, the correlation coefficient can be calculated by using the fluctuation data of cold heading pressure (such as production data of 100 batches of cold heading pressure) as the independent variable and the dimensional deviation detection data of 100 batches of bolts as the dependent variable, and then calculating the ratio of the covariance of the two variables to their respective standard deviations. For example, the calculated cold heading pressure has a variance of 5.2 and a correlation coefficient of 0.85 with dimensional deviation. Cold heading speed has a variance of 1.3 and a correlation coefficient of 0.42 with dimensional deviation. Cold heading temperature has a variance of 8.5 and a correlation coefficient of 0.78 with tensile strength. Thread rolling speed has a variance of 12.3 and a correlation coefficient of 0.91 with thread pitch accuracy. Thread rolling pressure has a variance of 3.1 and a correlation coefficient of 0.53 with thread pitch accuracy. Thread machining depth has a variance of 0.2 and a correlation coefficient of 0.67 with thread accuracy. Selection criteria (such as optimization requirements) are set, i.e., the absolute value of the correlation coefficient with the quality index is ≥0.7, and the variance is ≥5.0. Parameters that simultaneously meet these conditions are identified as process influence information. For example, cold heading pressure: correlation coefficient 0.85 ≥ 0.7, fluctuation variance 5.2 ≥ 5.0, meets the conditions and is included in the process influence information; cold heading temperature: correlation coefficient 0.78 ≥ 0.7, fluctuation variance 8.5 ≥ 5.0, meets the conditions and is included in the process influence information; thread rolling speed: correlation coefficient 0.91 ≥ 0.7, fluctuation variance 12.3 ≥ 5.0, meets the conditions and is included in the process influence information; other parameters (cold heading speed, thread rolling pressure, thread machining depth) do not meet both conditions at the same time and are not included in the process influence information.

[0068] In one possible implementation, before determining the process influence information based on the parameter information corresponding to the target process set in step S500, the automotive bolt forming process optimization analysis method further includes: S510, based on parameter fluctuation information, determine at least two target process sets from the multiple process parameter sets corresponding to each set of parameters to be optimized.

[0069] It is understandable that each set of parameters to be optimized corresponds to a set of process parameters, which may include a cold heading process parameter set (including cold heading pressure and cold heading temperature), a wire rolling process parameter set (including wire rolling speed and wire rolling pressure), and a heat treatment process parameter set (including heat treatment temperature and heat treatment time). The rule for determining at least two target process sets from the multiple process parameter sets corresponding to each set of parameters to be optimized can be to sort the parameter fluctuation values ​​from largest to smallest, and select at least two of the top-ranked values, i.e., select the process parameter set with the largest fluctuation amplitude and the most significant impact on quality as the target process set.

[0070] S520, based on the parameter fluctuation information of at least two target process sets, determines the fluctuation data of each set of parameters to be optimized.

[0071] It can be understood that fluctuation data can be the sum of the fluctuation values ​​of all parameters within the current target process set. The larger the fluctuation data, the more drastic the parameter fluctuations in this set of parameters to be optimized, and the more significant the impact on bolt forming quality; the smaller the fluctuation data, the smoother the fluctuations in this set of parameters, and the better the quality stability.

[0072] For example, consider a set of parameters to be optimized: the parameter fluctuation information for the cold heading parameter set is 0.9, the parameter fluctuation information for the thread rolling parameter set is 0.8, and the parameter fluctuation information for the heat treatment parameter set is 0.6. The parameters are sorted as follows: cold heading parameter set (0.9) > thread rolling parameter set (0.8) > heat treatment parameter set (0.6). The cold heading parameter set and the thread rolling parameter set are selected as the target process sets. The fluctuation data is calculated as: cold heading parameter set (0.9) + thread rolling parameter set (0.8) = 1.7. This process is repeated for multiple parameter sets to be optimized.

[0073] S530 determines the target set of parameters to be optimized from multiple sets of parameters to be optimized based on fluctuation data.

[0074] It is understandable that determining the target set of parameters to be optimized from multiple sets of parameters to be optimized can be done by sorting the values ​​of the fluctuating data from largest to smallest and selecting the top-ranked parameter set (such as the top k) as the target set of parameters to be optimized.

[0075] S540, determine the process parameter group from the target set of parameters to be optimized. The process parameter group reflects the parameter information corresponding to the target process set.

[0076] For example, the identified set of target parameters to be optimized is used to determine the process parameter set.

[0077] This setup, using parameter fluctuation information as the core quantitative basis and combining it with the bolt forming process rules, avoids the blind selection of key parameters and provides data support for subsequent optimization.

[0078] In one possible implementation, S400 performs optimization calculations based on parameter fluctuation information and optimization requirements in the process parameter set to obtain the target process set, including: Based on the parameter fluctuation information and the number of targets set in the optimization requirements, determine the target process set corresponding to the number of targets set in the optimization requirements from the process parameter set.

[0079] It is understandable that the number of targets can be determined from the set of process parameters. For example, it can be set to 2, 3, etc., according to actual production needs. This ensures that the selected target parameter set has sufficient influence, while avoiding excessive selection that would increase the complexity of subsequent analysis.

[0080] For example, the parameter fluctuation information of all process parameter sets is sorted in descending order. From the sorting results, the target number of process parameter sets (such as the first two) are selected as the target process sets, namely the cold heading process parameter set and the thread rolling process parameter set. These two parameter sets have the greatest impact on the bolt forming quality, and subsequent optimization will only be carried out on these two parameter sets, eliminating the heat treatment process parameter sets with less impact, thus simplifying the optimization process.

[0081] In one possible implementation, the process parameter dataset includes multiple datasets, and the automotive bolt forming process optimization analysis method also includes: S550 determines a second set of parameters from multiple datasets in the process parameter dataset that meets the target requirements for parameter fluctuation information.

[0082] It is understandable that the target requirement can be that the value of the parameter fluctuation information is greater than or equal to the target value (such as 0.6, 0.7, etc.).

[0083] For example, the parameter fluctuation information corresponding to each process parameter dataset is compared with the target value. If it is greater than or equal to the target value, the dataset is determined as the second parameter set.

[0084] S570, based on the proportion of each second parameter set in the process parameter dataset, determines the process impact information.

[0085] It can be understood that the parameter percentage can be the proportion of the total number of parameters in the second parameter set to the total number of parameters in the entire process parameter dataset. The calculation method is: Parameter percentage = (Total number of parameters in the second parameter set ÷ Total number of parameters in the process parameter dataset) × 100%. All process parameters within the second parameter set with the highest percentage, sorted from largest to smallest, are used to determine the process impact information.

[0086] For example, the total number of parameters in the process parameter dataset is: 3 stages × 3 process parameter sets × 2 parameters = 18 parameters; Second parameter set 1 (e.g., cold heading parameter set): 3 stages × 2 parameters = 6 parameters; Second parameter set 2 (e.g., wire rolling parameter set): 3 regions × 1 parameter = 3 parameters. Therefore, the parameter percentage of second parameter set 1 is 33.33%, and the parameter percentage of second parameter set 2 is 16.67%. Thus, the process influence information for all process parameters within second parameter set 1 is determined.

[0087] This setup uses the parameter fluctuation information of each process parameter set as the basis for selecting key optimization nodes, rather than relying on the independent fluctuation of a single parameter. This avoids the limitations of single-dimensional selection and makes the selection criteria for process optimization nodes more aligned with the process characteristics of multi-process collaborative forming of automotive bolts. In this way, it identifies the process parameter set that truly affects the bolt forming quality and avoids deviations in the optimization direction.

[0088] In one possible implementation, S570, based on the parameter proportion of each second parameter set in the process parameter dataset, determines process impact information, including: S571, based on the parameter proportion of each second parameter set in the process parameter dataset, determine the parameter influence weight corresponding to the second parameter set.

[0089] It's understandable that the parameter influence weight can be obtained by weighting and summing the parameter's proportion and the corresponding parameter fluctuation information according to a preset ratio, such as 50% or 60%. For example, parameter influence weight = (parameter proportion × 50%) + (normalized value of parameter fluctuation information × 50%). The normalized value of parameter fluctuation information can be obtained by converting the parameter fluctuation information into a value between 0 and 1. For example, the normalized value of parameter fluctuation information = parameter fluctuation information of this parameter set ÷ maximum parameter fluctuation information across all parameter sets.

[0090] For example, if the parameter fluctuation information of the cold heading parameter set is 0.9 and the parameter fluctuation information of the thread rolling parameter set is 0.8, then the normalized value corresponding to the cold heading parameter set is 0.9 ÷ 0.9 = 1, and the normalized value corresponding to the thread rolling parameter set is 0.8 ÷ 0.9 = 0.89. Therefore, the parameter influence weight corresponding to the cold heading parameter set is 33.33% × 0.5 + 1 × 0.5 = 0.67. The parameter influence weight corresponding to the thread rolling parameter set is 16.67% × 0.5 + 0.89 × 0.5 = 0.53.

[0091] S572, determine the parameter influence weights corresponding to the second parameter set in each dataset of the process parameter dataset to obtain process influence information.

[0092] For example, sort the parameters by their influence weight from largest to smallest, and then select all process parameters in the second parameter set with the largest influence weight to determine the process influence information.

[0093] This setup, by weighting and sorting the parameter proportions and fluctuation information of the second parameter set, quickly identifies the parameter set that has the greatest impact on the process. The parameter influence weight of each parameter set is used as the basis for selecting key optimization nodes, rather than relying on the independent fluctuation of a single parameter. This makes the selection criteria for process optimization nodes more in line with the process characteristics of multi-process collaborative molding of automotive bolts, thereby identifying the process parameter set that truly affects the bolt forming quality and avoiding deviations in the optimization direction.

[0094] S600 calculates the optimal parameter combination for the bolt forming process based on the parameter adjustment range of process influence information and optimization trend information.

[0095] It is understandable that the parameter control range for optimizing trend information can be the range of parameters that can be stably controlled for each process influence information, provided that the optimization objectives (such as dimensional accuracy and mechanical strength) are met. Calculating the optimal parameter combination for the bolt forming process can be done by using an optimization algorithm within the parameter control range of each process influence information to find the parameter combination that achieves the optimal core optimization objectives (such as the highest dimensional accuracy, the strongest mechanical strength, and the best production efficiency). This combination can be a set of optimal values ​​for multiple key parameters, not necessarily the optimal value of a single parameter.

[0096] For example, 100 parameter combinations are randomly selected within the parameter control range. Each parameter combination (Pi, Ti, Ri) includes 3 key parameters, and the value of each parameter falls within the parameter control range. A weighted summation method is used to integrate the two optimization objectives into a single fitness value F, with the formula: F = w1 × (1 ÷ Y1) + w2 × (Y2 ÷ Y2max), where Y2max is the historical maximum tensile strength (e.g., Y2max can be 900), w1 and w2 are weighting coefficients (e.g., w1 = 0.6, w2 = 0.4). Y1 is the dimensional deviation function: f(P,T,R) = k1 × P + k2 × T + k3 × R + c1, where k1, k2, and k3 are the regression coefficients corresponding to cold heading pressure P, cold heading temperature T, and wire rolling speed R, respectively, and c1 is a constant term (e.g., c1 can be -0.15). Y2 is the tensile strength function: g(P,T,R)=m1×P+m2×T+m3×R+c2, where m1, m2, and m3 are the regression coefficients corresponding to cold heading pressure P, cold heading temperature T, and wire rolling speed R, respectively, and c2 is a constant term, such as c2 can be 100, etc.

[0097] For example, S1: 100 randomly selected parameter combinations are substituted into Y1 and Y2 to determine whether they meet the requirements of dimensional accuracy (Y1≤0.02mm) and tensile strength (Y2≥800MPa). If Y1≤0.02mm and Y2≥800MPa are not met, the parameter combination is discarded. For those that meet Y1≤0.02mm and Y2≥800MPa, the fitness value F is calculated. Then, S2: The fitness values ​​F are arranged from largest to smallest, and the top 50 parameter combinations are selected as the parent population. For the 50 parameter combinations in the parent population, they are randomly paired, and the corresponding parameters of each pair are cross-fused to generate 50 new parameter combinations (i.e., a new population). For example, if parent generation 1 has parameter combinations of (P1, T1, R1) and parent generation 2 has parameter combinations of (P2, T2, R2), the resulting new populations after crossover will be (P1, T2, R1) and (P2, T1, R2). For each parameter combination in the new population, the value of a single parameter is randomly mutated (the mutated value still falls within the parameter's control range). For example, if a new population has parameters of (88 MPa, 230℃, 550 r / min), and cold heading pressure triggers mutation, the value will be randomly adjusted to 88.5 MPa (still within the 85-95 MPa range). The best parameter combinations that were not eliminated from the parent population (e.g., the top 10 fitness values) and the mutated parameter combinations are used as the parameter combinations for the next calculation. This process is repeated for S1 and S2 until the target number of times (e.g., 50 times) is reached, or until the fitness values ​​of the population show no significant change for five consecutive generations (e.g., error ≤ m, where m can be 0.1, 0.01, etc., set according to the actual situation). That is, the fitness values ​​for the nth, n+1th, n+2th, n+3th, and n+4th times are such that the difference between any two adjacent fitness values ​​is ≤ 0.01. At this point, the repetition is terminated, and the current population is considered to have approached the optimal solution. Finally, from the optimal solutions corresponding to multiple populations, the parameter combination with the highest fitness value is selected, which is the optimal parameter combination for the bolt forming process.

[0098] For example, automotive bolts can then be produced based on the optimal parameter combination to obtain finished automotive bolt products (such as...). Figure 3 ).

[0099] This setup, dividing the process parameter sets into at least two based on optimization trend information, elevates the dimension of process optimization analysis from a single parameter to multiple levels. Then, by determining the parameter fluctuation information for each process parameter set based on parameter characteristic information, the impact of each process parameter set on bolt forming quality can be quantified. Using the parameter fluctuation information of each process parameter set as the basis for selecting key optimization nodes, rather than relying on the independent fluctuation of a single parameter, makes the selection criteria for process optimization nodes more aligned with the multi-process collaborative forming characteristics of automotive bolts. This allows for the identification of the process parameter sets that truly affect bolt forming quality, avoiding deviations in the optimization direction. Subsequently, based on the parameter adjustment range of process influence information and optimization trend information, the optimal parameter combination for the bolt forming process can be calculated, enabling precise optimization of bolt forming process parameters and contributing to improved product quality.

[0100] In one possible implementation, when parameters are missing from the process parameter dataset, the process parameter dataset is supplemented with parameters based on standard molding process parameters.

[0101] For example, for missing values ​​of parameters in the process parameter dataset due to sensor failure or data transmission interruption, a preset supplementation method is used to supplement them: if other parameters in the batch to which the parameter belongs are normal, the average value of the parameters in the same process of the batch is used to supplement them; if the parameters of the entire batch are missing, the median value of the corresponding parameter in the standard molding process parameters is used to supplement them.

[0102] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0103] Corresponding to the automotive bolt forming process optimization analysis method described in the above embodiments, this application also provides an automotive bolt forming process optimization analysis device, the various units of which can realize the various steps of the automotive bolt forming process optimization analysis method. Figure 4 The diagram shows a structural block diagram of the automotive bolt forming process optimization and analysis device provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0104] Reference Figure 4 The automotive bolt forming process optimization and analysis device includes: Obtain the process parameter dataset and optimization trend information for the automotive bolt forming task; wherein, the process parameter dataset includes at least two process parameter sets divided according to the optimization trend information; the optimization trend information is used to indicate the optimization target of bolt forming; Trend extraction is performed on the fluctuation data of each parameter in the process parameter set to obtain the parameter feature information of the process parameter set; wherein, the fluctuation data is the numerical change data of the process parameter in the continuous production process; the trend extraction is used to analyze the fluctuation data to extract the change trend of the data; Based on the parameter feature information, parameter fluctuation information for each set of process parameters is determined; wherein, the parameter fluctuation information is used to reflect the impact of parameter fluctuations of the set of process parameters on bolt forming quality; Based on the parameter fluctuation information and optimization requirements in the process parameter set, optimization calculations are performed to obtain the target process set; wherein, the optimization calculations are used to determine the parameter set that meets the optimization threshold from multiple process parameter sets; the optimization requirements are quantitative standards set based on the optimization target and used to screen the process parameter set; The parameter information corresponding to the target process set is used to determine the process impact information; Based on the parameter adjustment range of the process influence information and the optimization trend information, the optimal parameter combination for the bolt forming process is calculated.

[0105] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] This application also provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 5 Only one is shown in the image), at least one memory 61 ( Figure 5(Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60, wherein when the processor 60 executes the computer program 62, it causes the electronic device 6 to perform the steps in any of the above-described embodiments of the automotive bolt forming process optimization analysis method, or causes the electronic device 6 to perform the functions of each unit in the above-described device embodiments.

[0108] For example, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the electronic device 6.

[0109] The electronic device 6 may be a PLC, microcontroller, mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, desktop computer, computing device, or computer, laptop computer, handheld communication device, handheld computing device, etc. connected to a wireless modem. The electronic device 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0110] The processor 60 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0111] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may be an external storage device of the electronic device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 6. Furthermore, the memory 61 may include both internal and external storage units of the electronic device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0112] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0113] This application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the steps in any of the above method embodiments.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0115] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0116] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the embodiments provided in this application, it should be understood that the disclosed automotive bolt forming process optimization analysis device / electronic device and automotive bolt forming process optimization analysis method can be implemented in other ways. For example, the embodiments of the automotive bolt forming process optimization analysis device / electronic device described above are merely illustrative. For instance, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing and analyzing the forming process of automotive bolts, characterized in that, The method includes: Obtain the process parameter dataset and optimization trend information for the automotive bolt forming task; wherein, the process parameter dataset includes at least two process parameter sets divided according to the optimization trend information; the optimization trend information is used to indicate the optimization target of bolt forming; Trend extraction is performed on the fluctuation data of each parameter in the process parameter set to obtain the parameter feature information of the process parameter set; wherein, the fluctuation data is the numerical change data of the process parameter in the continuous production process; the trend extraction is used to analyze the fluctuation data to extract the change trend of the data; Based on the parameter feature information, parameter fluctuation information for each set of process parameters is determined; wherein, the parameter fluctuation information is used to reflect the impact of parameter fluctuations of the set of process parameters on bolt forming quality; Based on the parameter fluctuation information and optimization requirements in the process parameter set, optimization calculations are performed to obtain the target process set; wherein, the optimization calculations are used to determine the parameter set that meets the optimization threshold from multiple process parameter sets; the optimization requirements are quantitative standards set based on the optimization target and used to screen the process parameter set; The parameter information corresponding to the target process set is used to determine the process impact information; Based on the parameter adjustment range of the process influence information and the optimization trend information, the optimal parameter combination for the bolt forming process is calculated.

2. The method for optimizing and analyzing the forming process of automotive bolts as described in claim 1, characterized in that, The acquisition of the process parameter dataset and optimization trend information for the automotive bolt forming task includes: Obtain default forming process parameters and standard forming process parameters; wherein, the standard forming process parameters include parameter range information for key process stages of bolt forming and optimization trend information for bolt forming; The default molding process parameters are adjusted based on the standard molding process parameters to obtain the adjusted process parameters. Based on the optimization trend information and the parameter range information, the parameter range information corresponding to the parameters of the key process stage of bolt forming in the adjusted process parameters is adjusted to obtain the parameter set to be optimized; wherein, the parameter set to be optimized includes the process parameter dataset.

3. The method for optimizing and analyzing the automotive bolt forming process as described in claim 2, characterized in that, The set of parameters to be optimized includes multiple parameter sets. Based on the optimization trend information and the parameter range information, the parameter range information corresponding to the parameters of the key process stage of bolt forming in the adjusted process parameters is adjusted to obtain the set of parameters to be optimized, including: Based on the optimization trend information and the parameter range information, the parameter range information corresponding to the parameters in the key stage of bolt forming in the adjustment process parameters is adjusted to obtain the first parameter set; Multiple process parameter adjustment values ​​of preset parameter adjustment data are obtained, and the first parameter set is fine-tuned according to the multiple process parameter adjustment values ​​to obtain multiple fine-tuning parameter sets; The set of fine-tuning parameters corresponding to different process parameter adjustment values ​​is taken as the set of parameters to be optimized.

4. The method for optimizing and analyzing the automotive bolt forming process as described in claim 2, characterized in that, Before determining the process influence information from the parameter information corresponding to the target process set, the method further includes: Based on the parameter fluctuation information, at least two target process sets are determined from the multiple process parameter sets corresponding to each set of parameters to be optimized. Based on the parameter fluctuation information of the at least two target process sets, determine the fluctuation data of each set of parameters to be optimized; Based on the fluctuation data, a target set of parameters to be optimized is determined from multiple sets of parameters to be optimized. The target set of parameters to be optimized is used to determine a process parameter group; wherein, the process parameter group is used to reflect the parameter information corresponding to the target process set.

5. The method for optimizing and analyzing the forming process of automotive bolts as described in claim 1, characterized in that, The optimization calculation based on the parameter fluctuation information and optimization requirements in the process parameter set yields the target process set, including: Based on the parameter fluctuation information and the number of targets set in the optimization requirements, a target process set corresponding to the number of targets set in the optimization requirements is determined from the set of process parameters.

6. The method for optimizing and analyzing the automotive bolt forming process as described in claim 1, characterized in that, The process parameter dataset includes multiple datasets, and the method further includes: From multiple datasets of the process parameter dataset, a second set of parameters whose parameter fluctuation information meets the target requirements is determined; The process impact information is determined based on the proportion of each of the second parameter sets in the process parameter dataset.

7. The method for optimizing and analyzing the automotive bolt forming process as described in claim 6, characterized in that, The step of determining the process impact information based on the parameter proportion of each of the second parameter sets in the process parameter dataset includes: Based on the proportion of parameters in each of the second parameter sets in the process parameter dataset, determine the parameter influence weight corresponding to the second parameter set; The process influence information is obtained by determining the parameter influence weight corresponding to the second parameter set in each dataset of the process parameter dataset.

8. The method for optimizing and analyzing the forming process of automotive bolts as described in claim 1, characterized in that, When a parameter is missing in the process parameter dataset, the process parameter dataset is supplemented with parameters.

9. A device for optimizing and analyzing the forming process of automotive bolts, characterized in that, Applied to electronic devices, for implementing the automotive bolt forming process optimization analysis method as described in any one of claims 1 to 8, wherein the automotive bolt forming process optimization analysis device comprises: An acquisition unit is used to acquire a process parameter dataset and optimization trend information for an automotive bolt forming task; wherein the process parameter dataset includes at least two process parameter sets divided according to the optimization trend information; the optimization trend information is used to indicate the optimization target of bolt forming. An extraction unit is used to extract trends from the fluctuation data of each parameter in the process parameter set to obtain parameter feature information of the process parameter set; wherein, the fluctuation data is the numerical change data of the process parameters during continuous production; the trend extraction is used to analyze the fluctuation data to extract the data change trend; A determining unit is configured to determine parameter fluctuation information for each set of process parameters based on the parameter feature information; wherein the parameter fluctuation information is used to reflect the impact of parameter fluctuations of the set of process parameters on bolt forming quality; The calculation unit is used to perform optimization calculations based on the parameter fluctuation information and optimization requirements in the process parameter set to obtain a target process set; wherein, the optimization calculation is used to determine the parameter set that meets the optimization threshold from multiple process parameter sets; the optimization requirements are quantitative standards set based on the optimization target and used to screen the process parameter set; A generation unit is used to determine process impact information from the parameter information corresponding to the target process set; The control unit is used to calculate the optimal parameter combination for the bolt forming process based on the parameter adjustment range of the process influence information and the optimization trend information.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1 to 7.

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