Integrated punch forming control method and system for DC (direct current) micromotor brush carrier

Abstract

The invention discloses an integrated punch forming control method and system for a DC (direct current) micromotor brush carrier, and relates to the technical field of punch forming processes, and the method comprises the steps: inputting 3D (three-dimensional) data, building structure partition results in a partition manner, mapping process parameters to each structure partition, and configuring an independent error evaluation objective function; an initial parameter set is established in combination with batch material data, partition optimization is carried out on the basis of the initial parameter set, a global parameter optimization result is generated through global fusion, and integrated punch forming control is achieved. The technical problems that a traditional stamping control method is difficult to adapt to the complex structure of the motor brush holder, material difference and partition precision requirements, parameters of all regions cannot be accurately controlled, the forming quality is unstable, the motor performance is affected, and the high-precision manufacturing requirement of the motor is difficult to meet are solved, and the purposes of accurate control over integrated stamping forming, high precision and high reliability are achieved. And the stamping precision and quality stability of the brush carrier are improved, the requirements for material differences and partition precision are met, and the technical effect of the high-precision manufacturing requirement of a motor is met.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of punch forming processes, in particular to an integrated punch forming control method and system for a DC micro motor brush holder. BACKGROUND

[0002] The quality of punch forming of a motor brush holder directly affects the performance and reliability of the motor and is crucial to motor production. In the prior art, the punch forming of a motor brush holder is mostly controlled by a traditional punch control method, which relies on experience to set process parameters. These methods have a certain effect in conventional production scenarios, but as the precision and consistency requirements of motor manufacturing are improved, they expose limitations when applied to integrated punch forming of a motor brush holder. Because the structure of a motor brush holder is complex and sensitive to forming precision, the traditional method is difficult to adapt to differences in materials of different batches and precision requirements in different zones, cannot accurately control punch parameters in each zone, and leads to unstable forming quality, affecting the performance of the motor and making it difficult to meet the high-precision manufacturing requirements of the motor. SUMMARY

[0003] The application provides an integrated punch forming control method and system for a DC micro motor brush holder, which is used to solve the technical problem that the traditional punch control method is difficult to adapt to the complex structure of a motor brush holder, material differences and precision requirements in different zones, cannot accurately control parameters in each zone, leads to unstable forming quality, affects the performance of the motor and makes it difficult to meet the high-precision manufacturing requirements of the motor.

[0004] In a first aspect, the application provides an integrated punch forming control method for a DC micro motor brush holder, which comprises: inputting 3D data of the DC micro motor brush holder, performing geometric element recognition according to the 3D data, performing structure zoning of functions and precision requirements according to the geometric element recognition result, and establishing a structure zoning result; mapping process parameters to each structure zone, establishing a mapping table, configuring an independent error evaluation objective function mapped with the structure zoning result according to the mapping table; after obtaining batch material data, performing global punch parameter configuration according to the batch material data, and establishing an initial parameter set; taking the initial parameter set as a basic parameter, performing independent region optimization based on the structure zoning result by using the independent error evaluation objective function, and establishing an independent region optimization result; synchronizing the independent region optimization result to a global scheduler, performing zoning priority fusion, generating a global parameter optimization result, and performing integrated punch forming control according to the global parameter optimization result.

[0005] In a second aspect of the present application, a one-piece stamping forming control system for a DC brush holder of a DC micro motor is provided, and the system comprises: a structure partition result construction module, configured to input 3D data of the DC brush holder of the DC micro motor, perform geometric element identification based on the 3D data, perform structure partition based on function and accuracy requirement according to the geometric element identification result, and construct a structure partition result; a mapping table construction module, configured to map process parameters to each structure partition, construct a mapping table, and configure an independent error evaluation objective function mapped with the structure partition result according to the mapping table; an initial parameter set construction module, configured to perform global stamping parameter configuration based on batch material data after obtaining the batch material data, and construct an initial parameter set; an independent region optimization result construction module, configured to perform independent region optimization based on the structure partition result by taking the initial parameter set as a basic parameter and using the independent error evaluation objective function, and construct an independent region optimization result; and a global parameter optimization result construction module, configured to synchronize the independent region optimization result to a global scheduler, perform partition priority fusion, generate a global parameter optimization result, perform one-piece stamping forming control based on the global parameter optimization result.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages: The present application realizes precise control of one-piece stamping forming of the brush holder of the motor by performing 3D data analysis, structure partition, stamping parameter configuration combined with batch material data, independent region optimization, global parameter fusion, quality feedback correction compensation, effectively improves stamping precision of the brush holder, adapts to material differences, meets the demand of motor high-precision manufacturing on the quality of the brush holder, and achieves the technical effects of precise control of one-piece stamping forming, improved stamping precision and quality stability of the brush holder, adaptation to material differences and partition accuracy requirement, and meeting the requirements of motor high-precision manufacturing. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0008] Figure 1 is a flowchart of a one-piece stamping forming control method for a DC brush holder of a DC micro motor provided by the embodiments of the present application.

[0009] Figure 2 is a structure diagram of a one-piece stamping forming control system for a DC brush holder of a DC micro motor provided by the embodiments of the present application.

[0010] Reference signs: structure partition result construction module 1, mapping table construction module 2, initial parameter set construction module 3, independent area optimization result construction module 4, global parameter optimization result construction module 5. DETAILED DESCRIPTION

[0011] The application provides a one-piece stamping forming control method and system for a DC brush holder of a micro motor, to solve the technical problem that the traditional stamping control method is difficult to adapt to the complex structure of the motor brush holder, material differences and partition precision requirements, cannot accurately control the parameters of each area, leads to unstable forming quality, affects the performance of the motor, and is difficult to meet the high-precision manufacturing requirements of the motor.

[0012] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0013] It should be noted that the terms "first", "second", etc. in the specification and the above drawings of the application are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] Embodiment one, as shown in the DC micro motor brush holder one-piece stamping forming control method, wherein the method comprises: Figure 1 Step A100: input the 3D data of the DC micro motor brush holder, identify the geometric elements according to the 3D data, structure partition according to the function and precision requirement of the geometric element identification result, and establish the structure partition result. Step A100: input the 3D data of the DC micro motor brush holder, identify the geometric elements according to the 3D data, structure partition according to the function and precision requirement of the geometric element identification result, and establish the structure partition result.

[0015] In the embodiments of the application, the DC micro motor is a micro motor powered by a DC power supply, and its running performance and reliability are directly affected by the stamping forming quality of key components such as the brush holder. The brush holder, as a core component, needs to be controlled by precise one-piece stamping forming to meet the high-precision manufacturing requirements of the motor.

[0016] Specifically, first, input the 3D data of the DC brush holder, which contains the overall structure size, geometric shape of each component and tolerance range, such as the diameter of the brush mounting hole in the main functional area, the thickness of the connecting bracket in the auxiliary forming area, and other detailed information. The 3D data of the DC brush holder is generated by digital modeling of the brush holder design scheme using industrial three-dimensional modeling software, or by scanning the physical brush holder or design prototype using three-dimensional scanning equipment such as a laser scanner or industrial CT, and then processing the data to obtain three-dimensional digital data containing geometric shape, size parameters, structural features, etc.

[0017] Based on the 3D data of the DC brush holder, the basic geometric information such as vertex coordinates, face domain parameters and curve equations in the three-dimensional model is first analyzed, and then the feature extraction algorithm is used to traverse the model data to identify the key structural features such as the center coordinates and diameter of the hole, the curvature radius and extension range of the surface, and the contour line direction and turning angle of the edge.

[0018] Finally, the geometric element recognition results are semantically analyzed to identify the function, and the precision requirement is configured according to the quality detection standard, and then the structure partition result is established through the two-factor decision partition analysis of precision requirement and geometric element function. The specific steps are described in detail in A110-A130.

[0019] Through such 3D data analysis and geometric element recognition, the structural differences and functional requirements of each part of the brush holder can be accurately captured, providing accurate basis for subsequent structure partitioning by function and precision, ensuring that the partitioning result matches the actual forming requirements of the brush holder, and providing accurate basic data for subsequent structure partitioning and process parameter configuration, achieving detailed control of the structural features of the brush holder.

[0020] Step A200: Map the process parameters to each structure partition to establish a mapping table, and configure an independent error evaluation objective function mapped with the structure partition result according to the mapping table.

[0021] Optionally, first, the process parameter types that need to be mapped are determined, including key parameters such as stamping pressure, holding time, mold temperature, and stamping speed. For the established structure partitions, such as the contact area, support area, and guide area, among which the main functional structure partition in the multi-level structure partition system is the contact area, and the auxiliary forming structure partition covers the support area and the guide area, the forming characteristics of each partition are analyzed: for example, the contact area requires high precision flatness, and the stamping pressure needs to be controlled at 50-60 MPa, the holding time is 1.2-1.5 s, and the mold temperature is stable at 25±2℃; the support area needs to ensure assembly strength, the stamping pressure is set to 40-50 MPa, the holding time is 0.8-1.0 s, and the mold temperature is 25±3℃; the guide area needs precise position, the stamping speed is controlled at 30-35 mm / s, and the mold temperature fluctuation is not more than ±1℃.

[0022] Subsequently, each structure partition is associated with the corresponding process parameter range to establish a mapping table. The mapping table clearly records the corresponding relationship: contact area, pressure 50-60 MPa, holding time 1.2-1.5 s, temperature 25±2℃; support area, pressure 40-50 MPa, holding time 0.8-1.0 s, temperature 25±3℃; guide area, speed 30-35 mm / s, temperature 25±1℃, etc., forming a structured data association system.

[0023] Based on the mapping table, an independent error evaluation objective function is configured for each structure partition. For the contact area, the objective function focuses on flatness error, and the formula is set as the deviation square sum of the measured flatness value and the 0.01 mm threshold, with a weight coefficient of 0.8, and the pressure fluctuation is associated, and for every 1 MPa fluctuation in pressure, the flatness error may increase by 0.002 mm; the support area target function takes size deviation as the core, calculates the absolute difference between the actual size and the design value, and the weight coefficient is 0.6, and the holding time is associated, and for every 0.1 s reduction in holding time, the size deviation may increase by 0.005 mm; the guide area target function focuses on position error, combined with speed stability parameters, and the weight coefficient is 0.7.

[0024] By mapping the process parameters to each structure partition and establishing a mapping table, and then configuring a targeted independent error evaluation objective function based on the mapping table, the precise matching of process parameters and partition characteristics is achieved, providing a quantitative evaluation basis for subsequent parameter optimization.

[0025] Step A300: After obtaining the batch material data, configure the global stamping parameters according to the batch material data to establish an initial parameter set.

[0026] In one embodiment of the present application, when acquiring batch material data, the key performance parameters of the batch metal material need to be collected, including hardness, thickness, yield strength, ductility or elongation, etc. These data are obtained by skilled persons in the art according to the material factory test report and the incoming sample test, to ensure the coverage of the material mechanical properties and geometric characteristics.

[0027] According to these batch material data, the influence law of the stamping process is analyzed. Specifically, by collecting the hardness, thickness, yield strength and other key performance parameters of the batch material, the adjustment amount of the process parameters such as stamping pressure, die gap and holding time is observed when each parameter changes, such as fixing the thickness and yield strength, recording the increase value of the stamping pressure when the hardness increases from 180HV to 190HV, and so on. The quantitative influence relationship between each material parameter and process parameter is summarized, and then the reference range of the global stamping parameters is determined in combination with the requirement of parameter synergy for the overall forming of the brush holder. For example, the basic pressure required for stamping needs to be increased by 3-5MPa for every 10HV increase in hardness; the die gap needs to be adjusted by 0.005mm for every 0.01mm deviation in thickness; and the holding time needs to be extended by 0.1-0.2s for every 20MPa increase in yield strength to avoid excessive material springback.

[0028] When configuring the global stamping parameters, the average performance of the material is taken as the reference: for the material with hardness of 190HV and thickness of 0.3mm, the global basic stamping pressure is set to 50MPa, the overall holding time is 1.0s, the initial temperature of the die is 25℃, and the reference value of the stamping speed is 30mm / s. At the same time, according to the relevance of each structure partition, the global parameters are adjusted in coordination, for example, the pressure difference between the contact area and the support area is controlled within 5-8MPa to avoid parameter conflict between the partitions leading to overall deformation.

[0029] Integrating the above configuration results, an initial parameter set is established, including specific parameters of each structure partition, such as contact area pressure 55MPa, holding time 1.2s; support area pressure 50MPa, holding time 1.0s, etc., and global coordination parameters, such as die temperature fluctuation range ±2℃, and the corresponding relationship between the parameters and the batch material data is marked to form a traceable initial parameter set.

[0030] By acquiring batch material data and configuring global stamping parameters accordingly, an initial parameter set that adapts to the current material characteristics is established, providing a scientific and reasonable basis for subsequent independent regional optimization based on structure partition.

[0031] Step A400: Taking the initial parameter set as the basis parameter, the independent regional optimization result is established by using the independent error evaluation objective function based on the structure partition result.

[0032] Specifically, meta-information tags including function importance degree, etc. are configured for each structure partition, an optimizer template is configured based on the tags, fitness evaluation is performed with an initial parameter set, and iterative optimization is performed with the evaluation result and the template to complete independent region optimization. Details are described in A410-A440.

[0033] Step A500: The independent region optimization result is synchronized to a global scheduler, partition priority fusion is performed, a global parameter optimization result is generated, and integrated stamp forming control is performed according to the global parameter optimization result.

[0034] Specifically, a collaborative influence grouping is established based on structure partition influence evaluation of the independent region optimization result, a grouping evaluation function is configured after calculating the importance degree of each partition to perform parameter fusion, and a global parameter optimization result is generated in combination with the optimization result not affected. Details are described in A510-A540.

[0035] Then, when the raw material batch is changed, a change data set is established based on batch material data to correct the global parameter optimization result, and integrated stamp forming control is performed using the migration update result. Details are described in A550-A560.

[0036] Further, the step A400 in the method provided in the embodiment of the application includes: A410: Configure a meta-information tag for each structure partition according to the structure partition result, wherein the meta-information tag includes a function importance degree tag, a forming error tolerance tag, a nonlinearity degree tag of an optimization target, a parameter change response strength tag of an error, and a physical model available to guide an optimization direction tag.

[0037] A420: Configure an optimizer template for the structure partition based on the meta-information tag.

[0038] A430: Use the initial parameter set as the initial parameter of each structure partition, perform fitness evaluation with an independent error evaluation target function mapped by each structure partition, and establish a fitness evaluation result.

[0039] A440: Perform iterative optimization with the fitness evaluation result and the optimizer template to complete independent region optimization.

[0040] In the embodiment of the application, the optimizer template is configured for each structure partition based on the meta-information tag of the structure partition, and is used as a template for subsequent iterative optimization.

[0041] Specifically, first, according to the structure partition result, configure the meta-information label for each partition. The function importance label of the main function structure partition (such as the contact area) is set to high, and the forming error tolerance label is set to ≤0.01 mm, because the error in the stamping forming has a significant impact on the motor performance; the function importance label of the auxiliary forming structure partition (such as the support area) is set to medium, and the forming error tolerance label is set to ≤0.03 mm. In terms of the nonlinearity degree label of the optimization target, the curved surface forming area is marked as high, and the plane forming area is marked as low; in the label of the intensity of the parameter change on the error response, the material thickness sensitive area is marked as strong, and a thickness fluctuation of 0.01 mm can cause an error increase of 0.005 mm, and the area with strong rigidity is marked as weak; in the label of the available physical model guiding the optimization direction, the simple geometric area is marked as yes, and the classical plastic mechanics model can be used, and the complex special-shaped area is marked as no.

[0042] Then, configure the optimizer template based on the above meta-information label. For the partition with high function importance and high nonlinearity degree, configure the genetic algorithm optimizer template, with 50 iterations and a crossover probability of 0.7; for the partition with medium function importance and high linearity degree, configure the gradient descent optimizer template with a learning rate of 0.01. For the partition with a strong parameter response intensity, add a sensitivity weight coefficient of 1.2 in the template; for the partition that can use a physical model, associate the corresponding model parameter library in the template.

[0043] Then, take the initial parameter set, such as the contact area pressure of 55 MPa and the support area pressure of 50 MPa, as the initial parameters of each partition, and call the independent error evaluation objective function mapped by each partition to evaluate the fitness. The contact area calculates the fitness formula based on the flatness error: 1-error / 0.01 mm, and the score ≥0.8 is qualified; the support area calculates the fitness formula based on the size deviation: 1-deviation / 0.03 mm, and the score ≥0.7 is qualified, and finally the fitness evaluation results of each partition are formed.

[0044] Finally, read the reference search step length configured in the current iteration period, configure the first search influence factor according to the fitness evaluation result, configure the second search influence factor according to the optimizer template, and then update the parameter set based on the three to complete the independent area optimization. The specific steps are described in detail in A441-A444.

[0045] By configuring the targeted meta-information label for each structure partition, matching the optimizer template, and evaluating the fitness based on the initial parameters, the evaluation standard and optimization tool that fit the characteristics of the partition are provided for subsequent iteration optimization, laying the foundation for accurate independent area optimization.

[0046] Further, the step A440 in the method provided by the embodiment of the application includes: A441: read the current iteration period, and configure the reference search step length according to the iteration period.

[0047] A442: configuring a first search influence factor according to the fitness evaluation result.

[0048] A443: configuring a second search influence factor according to the optimizer template.

[0049] A444: performing parameter set iteration update based on the reference search step, the first search influence factor, and the second search influence factor to complete independent region optimization.

[0050] In the embodiments of the present application, the reference search step is a search step configured according to the current iteration period.

[0051] Optionally, the current iteration period is read, and the corresponding reference search step is configured according to the different iteration stages. In the early stage of optimization (such as iteration period 1-10), in order to cover a wider parameter space, the reference search step is set to a larger value, for example, for the stamping pressure parameter, the step is 0.5 MPa; as the iteration goes deeper (such as period 11-20), in order to achieve fine optimization, the step is adjusted to 0.2 MPa; in the later iteration (period 21 and above), the step is further reduced to 0.1 MPa to avoid excessive parameter fluctuations.

[0052] Next, a first search influence factor is configured according to the fitness evaluation result, which reflects the adaptation degree of the current parameters. For example, if the fitness evaluation result of a certain structure partition is 0.9 (full score is 1.0), it means that the parameters are close to the optimal, and the first search influence factor is set to 0.3 to weaken the step adjustment amplitude; if the fitness is 0.6, the factor is set to 0.7 to moderately expand the search range; if the fitness is less than 0.5, the factor is set to 1.0 to strengthen the step adjustment to quickly find better parameters.

[0053] The second search influence factor is configured according to the optimizer template, and different templates correspond to different optimization strategies. When the genetic algorithm template is used, the second search influence factor is set to 1.2 because it needs strong global exploration ability; when the gradient descent template is used, the factor is set to 0.8 to ensure the stability of the optimization; when the simulated annealing template is used, the factor is set to 1.0 to balance exploration and convergence.

[0054] Next, a preset backtracking window is configured and the same group of parameter chain iteration backtracking is executed when triggered, and the first and second authentication results are obtained by using the window fitness growth authentication threshold and the trend authentication. If either of them fails, the corresponding parameter chain is tabooed and a new parameter group is added to complete the independent region optimization. The specific steps are described in detail in A444-1-A444-4.

[0055] By configuring the benchmark search step in stages, combining the fitness evaluation result and the optimizer template to dynamically adjust the influence factor, the pertinence and efficiency of the parameter set iteration update are realized, a precise search strategy is provided for independent region optimization, and the accuracy and convergence speed of the optimization result are improved.

[0056] Further, step A444 in the method provided by the embodiment of the application includes: A444-1: configuring a preset backtracking window, and performing iteration backtracking when the iteration number triggers the preset backtracking window, the iteration backtracking being update backtracking of the same group of parameter chains.

[0057] A444-2: configuring a window fitness growth authentication threshold, and performing fitness change authentication of the initial solution and the window solution of the same group of parameter chains by using the window fitness growth authentication threshold, to establish a first authentication result.

[0058] A444-3: taking the preset backtracking window as a starting point, calling the same group of parameter chains to perform fitness trend authentication, to establish a second authentication result.

[0059] A444-4: if any authentication result in the first authentication result or the second authentication result is a failure result, then tabuing the corresponding same group of parameter chains, and adding a parameter group in the unsearched space to complete independent region optimization.

[0060] In the embodiment of the application, the initial solution of the same group of parameter chains refers to the parameter solution corresponding to the parameter chain at the start of the preset backtracking window. The window solution refers to the parameter solution corresponding to the parameter chain at the end of the preset backtracking window.

[0061] Specifically, the preset backtracking window is configured, for example, the window size is set to 10 iterations, that is, when the iteration number reaches 10, 20, 30, and so on, the iteration backtracking mechanism is triggered. The iteration backtracking is performed on the update backtracking of the same group of parameter chains (for example, the stamping pressure parameter sequence of a certain structure partition continuously iterated for 10 times: 52 MPa→53 MPa→55 MPa→...→54 MPa). In the backtracking process, the historical update record of the parameter chain is extracted, the correlation between the parameter adjustment direction and the fitness change is analyzed, for example, it is judged whether the fitness is significantly improved when the pressure increases from 53 MPa to 55 MPa.

[0062] Next, the window fitness growth authentication threshold is configured, and it is assumed that the threshold is set to 8%. The fitness change authentication is performed on the initial solution (for example, the fitness of the initial iteration of the window is 0.7) and the window solution (for example, the fitness of the end iteration of the window is 0.75) of the same group of parameter chains, the fitness growth rate is calculated as (0.75-0.7) / 0.7≈7.1%, and since the growth rate is lower than the threshold of 8%, the first authentication result is a failure. If the fitness of the window solution is 0.76, the growth rate is about 8.6%, and the first authentication result is a pass.

[0063] Then, taking the preset backtracking window as the starting point, the same group parameter chain, i.e., the parameters of the first to tenth iterations, is called for fitness trend authentication, and the trend is determined by fitting the fitness change curve: if the curve shows a continuous rise, such as from 0.7→0.72→0.75, the second authentication result is passed; if the curve rises first and then falls, such as 0.7→0.73→0.71, the trend authentication fails.

[0064] If either the first authentication result (the growth rate 7.1% is less than the set threshold 8%) or the second authentication result (the trend decreases) fails, the corresponding same group parameter chain, such as the sequence 52MPa→53MPa→...→54MPa, is marked as taboo, and is prohibited from being used in subsequent iterations. At the same time, new parameter groups are added in the unsearched parameter space, such as the original search pressure range 50-60MPa, and the new ranges 48-50MPa and 60-62MPa, and the iteration optimization continues to ensure that the optimization process is not limited to a local space.

[0065] By configuring the backtracking window, performing fitness authentication, and processing the failed results, the method effectively avoids the parameter optimization from falling into a local optimal solution, and by taboosing invalid parameter chains and expanding the search space, the overall and accuracy of the independent area optimization is improved.

[0066] Further, the step A500 in the method provided by the embodiment of the application comprises: A510: performing structure partition influence evaluation according to the independent area optimization result, and establishing a cooperative influence grouping.

[0067] A520: calculating the partition importance of each structure partition by using the structure partition result.

[0068] A530: configuring a grouping evaluation function by using the partition importance and the cooperative influence grouping, and performing parameter fusion of the cooperative influence grouping by using the grouping evaluation function.

[0069] A540: generating a global parameter optimization result according to the parameter fusion result and the independent area optimization result that is not affected.

[0070] Specifically, according to the independent area optimization result, the influence of each structure partition is evaluated, and the correlation degree of the parameter change of different partitions to the forming quality of other partitions is analyzed: Step a: determine the combination of the target partition to be analyzed and the affected partition, for example, all possible partition combinations such as the contact zone→the support zone, the support zone→the guide zone. For each combination, set the disturbance range of the key process parameters (such as the stamping pressure and the holding time) of the target partition, which is usually ±10% of the current optimal value, for example, the disturbance range of the stamping pressure of the contact zone is 45-55MPa.

[0071] Step b: Keep other zone parameters unchanged when adjusting the target zone parameter within the perturbation range. Record the forming error change of the affected zone continuously by high-precision detection equipment (such as a three-dimensional coordinate measuring instrument). For example, when the contact zone pressure increases from 50 MPa to 55 MPa, measure the size deviation change value of the support zone. If the original deviation is 0.02 mm and the changed value is 0.028 mm, then the error change is 0.008 mm.

[0072] Step c: Calculate the influence coefficient by dividing the error change of the affected zone by the forming error tolerance of the zone, which is the maximum allowable error. For example, the forming error tolerance of the support zone is 0.03 mm, and the above error change of 0.008 mm corresponds to an influence coefficient of 0.008 ÷ 0.03 ≈ 0.27. If the contact zone pressure adjustment results in an error change of 0.01 mm in the support zone, then the influence coefficient is 0.01 ÷ 0.03 ≈ 0.33.

[0073] Step d: Repeat the above steps for all zone combinations to obtain a complete influence coefficient matrix. When the coefficient ≥ 0.3, it is determined that there is significant synergistic influence between the two zones, and they are classified into the same synergistic influence group; when the coefficient < 0.3, it is determined that there is weak correlation, and the affected zone is treated as an independent zone. Through this quantitative calculation, 2-3 synergistic influence groups (such as the contact zone and the support zone, the connecting zone and the reinforcing rib zone) and independent zones (such as the guide zone and the edge transition zone) can be accurately divided.

[0074] Next, the importance of each zone is calculated using the structure zone result, and the functional attributes and precision requirements are assigned: the main functional structure zone (such as the contact zone) has a functional weight of 0.6 due to its direct influence on current conduction, and a precision requirement weight (flatness ≤ 0.01 mm) of 0.4, so the importance = 0.6 × 0.8 + 0.4 × 0.9 = 0.84; the auxiliary forming structure zone (such as the support zone and the guide zone) has a functional weight of 0.4 and a precision requirement weight (size deviation ≤ 0.03 mm) of 0.3, so the importance = 0.4 × 0.6 + 0.3 × 0.5 = 0.39. This clearly defines the priority of each zone.

[0075] Based on the zone importance and synergistic influence group, a group evaluation function is configured, which includes importance weight, error tolerance, etc. When parameter fusion is performed on the synergistic influence group, a weighted average method is used, with the weight being the importance of each zone, for example, a synergistic group containing a contact zone (importance 0.84) and a support zone (0.39), the stamping pressure after fusion = (contact zone pressure × 0.84 + support zone pressure × 0.39) ÷ (0.84 + 0.39), and the fusion parameters are verified by the group evaluation function to determine whether they meet the error tolerance of the two groups (contact zone ≤ 0.01 mm, support zone ≤ 0.03 mm).

[0076] Finally, the parameter fusion result of the synergistically affected group is integrated with the independent partition optimization result (such as the position degree parameter of the guide area) of the unaffected independent partition to form a global parameter optimization result, which includes specific parameters such as the stamping pressure, pressure holding time, and mold temperature of each partition, and ensures that there is no conflict between parameters, such as a pressure difference between adjacent partitions ≤8MPa.

[0077] By synergistically analyzing the structure partition, quantifying the importance, and fusing the parameters, and combining the independent optimization result, the generated global parameter optimization result not only takes into account the partition synergy, but also preserves the optimization accuracy of the key area, thereby providing a scientific and reasonable parameter basis for integrated stamping forming.

[0078] Further, the step A100 in the method provided in the embodiment of the application comprises: A110: performing element semantic analysis according to the geometric element recognition result to identify the function of the geometric element.

[0079] A120: obtaining a quality detection standard of the DC brushless motor, and configuring the precision requirement of the geometric element recognition result according to the quality detection standard.

[0080] A130: performing two-factor decision partition analysis according to the precision requirement and the function of the geometric element, and establishing a structure partition result.

[0081] Specifically, based on the geometric element recognition result of the DC brushless motor, the extracted structural features such as hole position, curved surface, and edge are first subjected to element semantic analysis. When performing element semantic analysis, first, based on the specific geometric parameters such as the extracted hole diameter, curved surface curvature, and edge perpendicularity, a rule library associating structural features with functions is established, the rule library contains preset association logic such as "the flat surface in contact with the brush needs to meet the surface roughness Ra≤0.8 μm to ensure conduction" and "the protruding structure height deviation connected to the motor shell needs to be ≤0.05 mm to ensure assembly", and the specific preset association logic is determined by a person skilled in the art according to the actual working condition.

[0082] Subsequently, the identified geometric element parameters are compared and matched with a rule library. For example, when a plane with an area of 2 mm x 3 mm, a surface roughness Ra = 0.6 μm, and located on the brush movement trajectory is detected, a current conduction associated rule is triggered; when a protrusion with a height of 1.5 mm and a positional deviation of ≤0.03 mm from the shell assembly hole is detected, a fixed support associated rule is triggered. At the same time, the matching results are verified again in combination with the dynamic working conditions of the brush holder during motor operation (such as brush friction frequency and shell vibration intensity). If the adaptation degree of the curvature radius of a curved surface to the brush wear track is ≥95%, it is confirmed as a guide area function. The functions of different functional areas in the DC brush holder and the corresponding condition parameters are shown in Table 1. Through such parameter matching, rule triggering, and working condition verification, the functional properties of each geometric element are finally determined.

[0083] Subsequently, the quality detection standard of the DC brush holder is obtained, which clearly defines the precision thresholds of different functional areas: the flatness tolerance of the contact area needs to be ≤0.01 mm to avoid poor current conduction; the size deviation of the support area needs to be ≤0.03 mm to ensure stable assembly; the positional tolerance of the guide area needs to be ≤0.02 mm to ensure smooth operation of the brush. According to these standards, the corresponding precision requirements are configured for each identified functional geometric element.

[0084] Finally, based on the precision requirements and the functions of the geometric elements, a two-factor decision partition analysis is performed: the contact area with high functional importance and strict precision requirements is divided into an independent main functional partition, and the support area and the guide area with moderate precision requirements are divided into auxiliary functional partitions, while ensuring the continuity of the partition boundaries and the matching of the geometric features. Finally, a structure partition result containing main and auxiliary functional partitions is established.

[0085] Through the above steps, precise partitioning based on the two dimensions of function and precision is achieved, providing clear basis for the subsequent targeted mapping of process parameters and the establishment of error evaluation objective functions.

[0086] Table 1: DC brush holder functional area condition parameter table ; ; Further, the method provided in the embodiments of the present application comprises the following step A500: A550: When the original material batch is changed, a changed batch material data set is established according to the batch material data.

[0087] A560: The changed batch material data set is used to migrate and correct the global parameter optimization result, and the integrated stamping forming control is executed using the migration update result.

[0088] In one embodiment, when the raw material batch is changed, first, a comprehensive test is performed on the new batch of materials to collect key performance parameters to establish a changed batch material dataset. The test content includes material hardness, thickness, yield strength, ductility, etc. These data are obtained through the material factory quality inspection report and sampling test when entering the factory, covering the core mechanical and geometric properties affecting the stamping forming. Finally, a structured changed batch material dataset is formed, including specific values and fluctuation ranges of each parameter.

[0089] The changed batch material dataset is used to migrate and correct the global parameter optimization results, and the parameter values are adjusted according to the correlation rules of material parameters and process parameters. For example, if the hardness of the new batch of materials is increased by 10HV compared with the original batch, according to the rule that the stamping pressure needs to be increased by 3-5MPa for every 10HV increase in hardness, the global basic stamping pressure is corrected from 50MPa to 53-55MPa; if the thickness of the new batch of materials is increased by 0.02mm compared with the original batch, according to the rule that the mold gap is adjusted by 0.005mm for every 0.01mm deviation in thickness, the mold gap is adjusted from 0.1mm to 0.11mm; if the yield strength is reduced by 20MPa, the holding time is shortened from 1.0s to 0.8-0.9s to adapt to the rebound characteristics of the material.

[0090] The globally migrated and corrected parameters (such as the adjusted stamping pressure, mold gap, holding time, etc.) are applied to the integrated stamping forming control process, and real-time data during the forming process, such as actual stamping pressure fluctuation and material deformation, are recorded and compared with the quality detection standard to ensure that the forming precision of each structure partition of the brush holder under the new parameters still meets the requirements, such as the contact area flatness ≤0.01mm and the size deviation of the support area ≤0.03mm.

[0091] By establishing a changed batch material dataset and migrating and correcting the global parameter optimization results, the rapid adaptation to the change of the raw material batch is realized, and the stability of the integrated stamping forming control and the consistency of the brush holder product quality are ensured.

[0092] Further, step A130 in the method provided by the embodiment of the application comprises: A131: The structure partition result is a multi-level structure partition, including a main function structure partition and an auxiliary forming structure partition, and each structure partition in the structure partition result is configured with a dynamic adjustment boundary.

[0093] Optionally, after completing the double-factor structure partitioning based on function and precision, a multi-level structure partitioning system is further constructed. The main function structure partitioning focuses on the core function implementation of the brush holder, such as the contact area directly involved in current conduction, which accounts for about 30% of the area and needs to meet the high-precision requirement of flatness ≤0.01 mm; the auxiliary forming structure partitioning covers the support area, guide area, etc., which accounts for about 70% of the area, such as the support area which needs to meet the assembly requirement of size deviation ≤0.03 mm, and the guide area which needs to ensure the positioning accuracy of position degree ≤0.02 mm.

[0094] On this basis, a dynamic adjustment boundary is configured for each partition. The boundary parameter is preset with an initial value, such as the initial setting of the contact area boundary at 0.5 mm from the edge, and the initial setting of the support area boundary at 0.3 mm from the contact area boundary. When the material batch hardness fluctuation of ±5% is detected during the stamping process, the contact area boundary can be automatically adjusted by 0.02 mm in the radial direction, and the auxiliary forming partition boundary is correspondingly adjusted by 0.05 mm; when the forming error of a certain area exceeds the threshold value by 1.2 times, the boundary will be fine-tuned by 0.01-0.03 mm according to the error direction to adapt to the actual forming state.

[0095] The trigger mechanism of the dynamic adjustment boundary is linked with the stamping parameter optimization process. In the independent area optimization stage, if the optimizer template feedback indicates that the parameter sensitivity of a certain partition is improved, the boundary will be shrunk or expanded in real time to ensure that the partition range matches the current process conditions; in the global parameter fusion stage, the boundary adjustment will take into account the synergistic effect of adjacent partitions to avoid misalignment of the overall structure due to changes in a single partition.

[0096] By constructing a multi-level partitioning and configuring a dynamic adjustment boundary, the priority of the core and auxiliary areas is clearly defined, and the partition range is adapted to the material properties and forming error in real time, providing a flexible and stable structure foundation for the accurate mapping and global optimization of subsequent process parameters.

[0097] Further, the step A500 in the method provided by the embodiment of the application comprises: A571: configuring a quality detection unit to perform quality detection of the integrated stamping forming, and establishing quality detection feedback.

[0098] A572: performing control consistency analysis using the quality detection feedback, and establishing control deviation.

[0099] A573: correcting and compensating the global parameter optimization result according to the control deviation.

[0100] In the embodiment of the application, the quality detection unit is a unit that performs quality detection of the integrated stamping forming and establishes quality detection feedback.

[0101] In one embodiment, a quality detection unit is configured, which integrates a laser flatness detector, an image size measuring instrument and other equipment, and detects all parameters of the integrated stamping formed brush holder. The detection frequency is set to sample 10 pieces every 50 pieces produced, and the key indicators such as the flatness of the contact area, the size deviation of the support area, and the position degree of the guide area are detected. The measurement accuracy is ±0.001 mm, ±0.002 mm, and ±0.001 mm, respectively. The detection data is compared with the quality standard, such as flatness ≤0.01 mm and size deviation ≤0.03 mm, to form quality detection feedback, including actual error value, error fluctuation range and out-of-tolerance item record of each sub-area.

[0102] Then, the quality detection feedback is used for control consistency analysis, and the error mean and standard deviation of the continuous 3 batches of products are calculated. For example, the contact area flatness detection feedback shows that the mean of batch 1 is 0.009 mm, the standard deviation is 0.001 mm, the mean of batch 2 is 0.010 mm, the standard deviation is 0.0015 mm, the mean of batch 3 is 0.011 mm, and the standard deviation is 0.002 mm, showing a gradual increasing trend, and 2 pieces of the third batch are out of tolerance (0.012 mm). Through trend analysis, it is judged that there is a systematic control deviation, and the deviation value is the difference between the current mean and the standard value, which is 0.011 mm-0.01 mm=+0.001 mm.

[0103] Finally, the global parameter optimization result is corrected and compensated according to the control deviation. For the +0.001 mm systematic deviation of the contact area flatness, a parameter compensation model is called, which collects the forming error data such as flatness deviation and position degree standard deviation corresponding to different process parameters such as stamping pressure and mold positioning accuracy in historical production, uses statistical analysis and regression algorithm to mine the quantitative correlation between error and parameter, such as the deviation increases by 0.001 mm, the pressure needs to be increased by 1.5 MPa, and then the correlation coefficient is adjusted through multiple batch experiments, and finally an error-parameter compensation mapping model is formed which can be directly called. The original global parameter of the contact area stamping pressure is adjusted from 55 MPa to 56.5 MPa; at the same time, for the increasing fluctuation of the guide area position degree, the standard deviation increases from 0.001 mm to 0.0018 mm, the mold positioning accuracy is fine-tuned by 0.0005 mm. After compensation, continuous detection is carried out, and the mean of the contact area flatness of the new batch of products is reduced to 0.010 mm, and the standard deviation is 0.0012 mm, which meets the control consistency requirement.

[0104] By configuring a quality detection unit to establish feedback, analyze control deviation and perform parameter correction and compensation, a closed-loop control mechanism is formed, which effectively suppresses the cumulative trend of forming error and ensures the long-term stability and precision consistency of the integrated stamping of DC direct current micro motor brush holder.

[0105] In summary, the integrated stamping and forming control method for DC micro motor brush holders provided in this application has the following technical effects: This application identifies geometric elements and partitions the structure by inputting 3D data of the DC micro motor brush holder. It maps process parameters to each partition and configures independent error evaluation objective functions. After establishing an initial parameter set based on batch material data, it performs independent region optimization and then merges the results through a global scheduler to generate global parameter optimization results. This achieves integrated stamping forming control of the DC micro motor brush holder, ensuring greater accuracy and stability in brush holder stamping forming. It achieves precise control of integrated stamping forming, improves the stamping accuracy and quality stability of the brush holder, adapts to material differences and partition accuracy requirements, and meets the technical effect of meeting the high-precision manufacturing requirements of motors.

[0106] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides an integrated stamping and forming control system for a DC micro motor brush holder, the system comprising: The structural partitioning result construction module 1 is used to input 3D data of DC micro motor brush holder, perform geometric element identification based on the 3D data, perform structural partitioning based on functional and accuracy requirements based on the geometric element identification results, and establish structural partitioning results.

[0107] The mapping table construction module 2 is used to map process parameters to each structural partition, establish a mapping table, and configure an independent error evaluation objective function that maps the results of the structural partitions according to the mapping table.

[0108] The initial parameter set construction module 3 is used to configure global stamping parameters and establish an initial parameter set after acquiring batch material data.

[0109] Independent region optimization result construction module 4 is used to perform independent region optimization based on the structural partitioning results using the initial parameter set as the basic parameters and the independent error evaluation objective function, and to establish independent region optimization results.

[0110] The global parameter optimization result construction module 5 is used to synchronize the optimization results of the independent regions to the global scheduler, perform partition priority fusion, generate global parameter optimization results, and perform integrated stamping and forming control based on the global parameter optimization results.

[0111] Furthermore, the independent region optimization result construction module 4 is used to perform the following steps: According to the structure partition result, configure the meta-information label of each structure partition, the meta-information label includes a functional importance label, a forming error tolerance label, a non-linear degree label of an optimization target, a parameter change response intensity label, and a physical model available guiding optimization direction label; configure an optimizer template of the structure partition based on the meta-information label; use the initial parameter set as the initial parameter of each structure partition, perform fitness evaluation by using the independent error evaluation objective function mapped by each structure partition, and establish a fitness evaluation result; perform iterative optimization by using the fitness evaluation result and the optimizer template to complete independent area optimization.

[0112] Further, the independent area optimization result construction module 4 is configured to perform the following steps: read the current iteration period, and configure a reference search step length according to the iteration period; configure a first search influence factor according to the fitness evaluation result; configure a second search influence factor according to the optimizer template; and perform parameter set iterative update based on the reference search step length, the first search influence factor, and the second search influence factor to complete independent area optimization.

[0113] Further, the independent area optimization result construction module 4 is configured to perform the following steps: configure a preset backtracking window, perform iteration backtracking when the iteration number triggers the preset backtracking window, the iteration backtracking is update backtracking of the same group parameter chain; configure a window fitness growth authentication threshold, perform fitness change authentication of the initial solution and the window solution of the same group parameter chain by using the window fitness growth authentication threshold, and establish a first authentication result; take the preset backtracking window as a starting point, call the same group parameter chain to perform fitness trend authentication, and establish a second authentication result; if any authentication result in the first authentication result or the second authentication result is a failure result, taboo the corresponding same group parameter chain, and add a parameter group in the unsearched space to complete independent area optimization.

[0114] Further, the global parameter optimization result construction module 5 is configured to perform the following steps: perform structure partition influence evaluation according to the independent area optimization result, establish a cooperative influence grouping, calculate the partition importance of each structure partition by using the structure partition result, configure a grouping evaluation function by using the partition importance and the cooperative influence grouping, perform parameter fusion of the cooperative influence grouping by using the grouping evaluation function, and generate a global parameter optimization result according to the parameter fusion result and the independent area optimization result without influence.

[0115] Further, the structure partition result construction module 1 is configured to perform the following steps: According to the geometric element identification result, element semantic analysis is performed to identify the function of the geometric element, the quality detection standard of the DC direct current micro motor brush holder is acquired, and the precision requirement of the geometric element identification result is configured according to the quality detection standard; and according to the precision requirement and the function of the geometric element, two-factor decision partition analysis is performed to establish a structure partition result.

[0116] Further, the global parameter optimization result construction module 5 is configured to perform the following steps: When the raw material batch is changed, a changed batch material data set is established according to batch material data; the global parameter optimization result is corrected by migration using the changed batch material data set, and integrated stamping forming control is performed using the migration update result.

[0117] Further, the structure partition result construction module 1 is configured to perform the following steps: The structure partition result is a multi-level structure partition, including a main function structure partition and an auxiliary forming structure partition, and each structure partition in the structure partition result is configured with a dynamic adjustment boundary.

[0118] Further, the global parameter optimization result construction module 5 is configured to perform the following steps: A quality detection unit is configured to perform quality detection of integrated stamping forming, and quality detection feedback is established; control consistency analysis is performed using the quality detection feedback, and control deviation is established; and the global parameter optimization result is corrected and compensated according to the control deviation.

[0119] The integrated stamping forming control system of the DC direct current micro motor brush holder provided by the embodiment of the application can perform the integrated stamping forming control method of the DC direct current micro motor brush holder provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method.

[0120] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific name of each functional unit is only for easy mutual differentiation, and does not limit the protection scope of the present application.

[0121] The foregoing DETAILED DESCRIPTION, including the above section titled "Detailed Description," is not to be taken as limiting the scope of the application. Various modifications, combinations, and equivalents can be apparent to those skilled in the art and can be made once the nature of the application is understood. Any modification, combination, or equivalent, which falls within the principles and the scope of the present application, is intended to be included in the present application. In some instances, the actions or steps can be performed in different order from those described herein, and still achieve desirable results. Additionally, the process depicted in the figures can not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

Claims

1. A method for controlling the integrated stamping forming of a DC brushless micromotor commutator, characterized in that, The method comprises: Input 3D data of a DC direct micro motor brush holder, perform geometric element identification according to the 3D data, perform structure partitioning of functions and precision requirements according to the geometric element identification result, and establish a structure partitioning result; Map process parameters to each structure partition, establish a mapping table, and configure an independent error evaluation objective function mapped with the structure partitioning result according to the mapping table; After obtaining batch material data, perform global stamping parameter configuration according to the batch material data, and establish an initial parameter set; Use the independent error evaluation objective function to perform independent area optimization based on the structure partitioning result, and establish an independent area optimization result, taking the initial parameter set as a basic parameter; Synchronize the independent area optimization result to a global scheduler, perform partition priority fusion, generate a global parameter optimization result, and perform integrated stamping forming control according to the global parameter optimization result.

2. The method of claim 1, wherein the DC commutator brush holder is formed by a one-step stamping process. The use of the independent error evaluation objective function to perform independent area optimization based on the structure partitioning result, and the establishment of an independent area optimization result, comprise: Configure a meta-information tag for each structure partition according to the structure partitioning result, the meta-information tag comprising a function importance tag, a forming error tolerance tag, a nonlinearity degree tag of an optimization target, a parameter change error response intensity tag, and a physical model guided optimization direction tag; Configure an optimizer template for the structure partition based on the meta-information tag; Take the initial parameter set as the initial parameter of each structure partition, perform fitness evaluation using the independent error evaluation objective function mapped by each structure partition, and establish a fitness evaluation result; Use the fitness evaluation result and the optimizer template to perform iterative optimization to complete independent area optimization.

3. The method of claim 2, wherein the step of forming the DC commutator brush holder is performed by a one-step stamping process. The use of the fitness evaluation result and the optimizer template to perform iterative optimization to complete independent area optimization, comprises: Read the current iteration period, and configure a reference search step according to the iteration period; Configure a first search influence factor according to the fitness evaluation result; Configure a second search influence factor according to the optimizer template; Perform parameter set iterative updating based on the reference search step, the first search influence factor, and the second search influence factor to complete independent area optimization.

4. The method of claim 3, wherein the one-piece stamping forming control method of the DC commutator motor brush holder is characterized by, The use of the fitness evaluation result and the optimizer template to perform iterative optimization to complete independent area optimization, comprises: Configure a preset backtracking window, perform iterative backtracking when the iteration number triggers the preset backtracking window, and the iterative backtracking is an update backtracking of a same group parameter chain; Configure a window fitness growth authentication threshold, use the window fitness growth authentication threshold to perform fitness change authentication of an initial solution and a window solution of the same group parameter chain, and establish a first authentication result; Take the preset backtracking window as a starting point, call the same group parameter chain to perform fitness trend authentication, and establish a second authentication result; If any authentication result in the first authentication result or the second authentication result is a failure result, taboo the corresponding same group parameter chain, and add a parameter group in the unsearched space to complete independent area optimization.

5. The integrated stamping and forming control method for DC micro motor brush holder as described in claim 1, characterized in that, The independent area optimization result is synchronized to a global scheduler, a partition priority fusion is performed, and a global parameter optimization result is generated, including: According to the independent area optimization result, a structure partition influence evaluation is performed, and a collaborative influence grouping is established; The partition importance of each structure partition is calculated using the structure partition result; The partition importance, the collaborative influence grouping, and the grouping evaluation function are configured, and the parameter fusion of the collaborative influence grouping is performed using the grouping evaluation function; According to the parameter fusion result and the independent area optimization result that is not influenced, a global parameter optimization result is generated.

6. The method of claim 1, wherein the method further comprises: forming the DC commutator brush holder from a single piece of material. The structure partition according to the geometric element identification result is performed according to the function and precision requirement, and the structure partition result is established, including: According to the geometric element identification result, element semantic analysis is performed, and the function of the geometric element is identified; The quality detection standard of the DC brush holder is obtained, and the precision requirement of the geometric element identification result is configured according to the quality detection standard; According to the precision requirement and the function of the geometric element, a two-factor decision partition analysis is performed, and a structure partition result is established.

7. The integrated stamping and forming control method for DC micro motor brush holder as described in claim 1, characterized in that, The integrated stamping forming control according to the global parameter optimization result includes: When the raw material batch is changed, a changed batch material data set is established according to the batch material data; The global parameter optimization result is migrated and corrected using the changed batch material data set, and the integrated stamping forming control is performed using the migration update result.

8. The integrated stamping and forming control method for DC micro motor brush holder as described in claim 1, characterized in that, The structure partition result is a multi-level structure partition, including a main function structure partition and an auxiliary forming structure partition, and each structure partition in the structure partition result is configured with a dynamic adjustment boundary.

9. The integrated stamping and forming control method for DC micro motor brush holder as described in claim 1, characterized in that, The integrated stamping forming control according to the global parameter optimization result further includes: A quality detection unit is configured to perform quality detection of integrated stamping forming, and quality detection feedback is established; Control consistency analysis is performed using the quality detection feedback, and control deviation is established; According to the control deviation, the global parameter optimization result is corrected and compensated.

10. A control system for the integrated stamping forming of a DC brushless micromotor commutator, characterized in that it comprises: The integrated stamping forming control method for the DC brush holder of any one of claims 1-9, the system comprising: A structure partition result construction module for inputting 3D data of the DC brush holder, identifying geometric elements according to the 3D data, and performing structure partition according to the function and precision requirement of the geometric element identification result, and establishing a structure partition result; A mapping table construction module for mapping process parameters to each structure partition, establishing a mapping table, and configuring an independent error evaluation objective function mapped with the structure partition result according to the mapping table; An initial parameter set construction module for establishing an initial parameter set by configuring global stamping parameters according to batch material data after obtaining the batch material data; An independent area optimization result construction module for establishing an independent area optimization result by performing independent area optimization based on the structure partition result using the independent error evaluation objective function with the initial parameter set as the basic parameter; An independent area optimization result construction module for establishing an independent area optimization result by performing independent area optimization based on the structure partition result using the independent error evaluation objective function with the initial parameter set as the basic parameter; The global parameter optimization result construction module is configured to synchronize the independent area optimization results to a global scheduler, perform partition priority fusion, generate a global parameter optimization result, and perform integrated stamp forming control according to the global parameter optimization result.

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