A precision spare parts matching process parameter closed loop adjustment method and device

Abstract

This application provides a closed-loop adjustment method and apparatus for the process parameters of precision component selection, belonging to the field of industrial precision assembly technology. It measures the key dimensions of components A and B used for assembly and mating. Based on a preset target stroke, current compensation value, measured dimensions of component A and component B, the selection dimensions of component C are calculated, and the measured dimensions of component C are obtained by outbound measurement. The theoretical stroke of the assembled assembly is calculated based on the measured dimensions of components A, B, and C. The overall dimensions of the assembled assembly are measured to obtain the measured stroke, and the stroke deviation is calculated. Based on multiple sets of stroke deviation data within a batch, the batch average deviation is calculated, and a new compensation value is obtained by combining it with a learning coefficient for closed-loop adaptive adjustment. Thus, by constructing a full-process closed-loop control mechanism, online adaptive and autonomous optimization of process parameters is achieved, reducing manual debugging operations.

Description

Technical Field

[0001] This application belongs to the field of industrial precision assembly technology, and specifically relates to a closed-loop adjustment method and device for the process parameters of precision parts selection. Background Technology

[0002] In precision industrial assembly, component size selection is a core element in ensuring assembly quality. Currently, the industry mainstream adopts an open-loop control mode for size selection and assembly operations. Process parameters are preset offline by technicians and fixed throughout the process, relying primarily on manual experience and theoretical calculations. On-site measuring equipment only collects component dimensions and determines product quality; the collected measurement data cannot be used to adjust process parameters. Furthermore, parameters such as tolerance ranges, weighting coefficients, and target clearances in the selection logic are fixed values, lacking real-time feedback and correction capabilities. When disturbances occur during production, such as batch differences in components, ambient temperature drift, tool wear, or fixture deformation, the existing system cannot respond autonomously, requiring manual shutdown and on-site modification of process parameters, severely impacting production continuity.

[0003] Therefore, the existing technology has many shortcomings: First, the process parameters are fixed and cannot adapt to changes in on-site working conditions, resulting in large fluctuations in product assembly accuracy; Second, the measurement and control links are disconnected, which can only achieve post-production quality inspection and cannot carry out process-based quality control; Third, the consistency of component selection is poor, the process capability index CPK is unstable, and the product rework rate and scrap rate remain high; Fourth, the frequency of manual intervention in the production process is high, the equipment debugging cycle is long, and the degree of automation and intelligence of the production line is low. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method and device for closed-loop adjustment of process parameters for precision parts selection. By constructing a closed-loop control mechanism for the entire process, the process parameters can be adapted and optimized online, reducing manual debugging work.

[0005] This application provides a closed-loop adjustment method for the process parameters of precision component selection, the method comprising the following steps: The key dimensions of component A and component B used for assembly and mating were measured respectively to obtain the actual measured dimensions of component A and component B. Calculate the selected dimensions of component C based on the preset target stroke, current compensation value, actual measured dimensions of component A, and actual measured dimensions of component B; Based on the selected dimensions, component C is shipped out, and key dimensions of component C are measured to obtain the actual measured dimensions of component C. Calculate the theoretical stroke of the assembled component based on the measured dimensions of component A, component B, and component C. The overall dimensions of the assembled component are measured to obtain the actual stroke of the component. Calculate the travel deviation between the measured travel and the theoretical travel; Based on multiple sets of travel deviation data within a batch, the average deviation of the batch is calculated, and a new compensation value is obtained by updating the learning coefficient. The new compensation value is used as the updated process parameter for closed-loop adaptive adjustment.

[0006] In some embodiments, the optional dimensions of component C are calculated using the following formula: = - + -

[0007] Among them, the measured dimensions of component A and the measured dimensions of component B are respectively... , , For the preset target itinerary, This is the current compensation value.

[0008] In some embodiments, the theoretical stroke of the assembled component and the stroke deviation between the measured stroke and the theoretical stroke are calculated using the following formula: = -

[0009] = - +

[0010] in The measured dimensions are for component C. , These are the theoretical and measured strokes of the assembled components, respectively.

[0011] In some embodiments, calculating the batch average deviation based on multiple sets of travel deviation data within a batch includes the following steps: Preset maximum travel deviation Minimum deviation from travel ; For the travel deviation corresponding to the kth product in the batch Filter and retain those that meet the requirements. Effective travel deviation data; Based on the total quantity M of a single batch of products and the effective travel deviation data after screening, according to the formula The average batch deviation was calculated.

[0012] In some embodiments, the new compensation value is obtained using the following formula:

[0013] in, The parameters are for learning, and 0 < <1.

[0014] In some embodiments, after obtaining the new compensation value, the following steps are further included: Based on the preset compensation value upper limit Lower limit of compensation value The obtained new compensation value is then judged and revised; whereby, if the obtained new compensation value is greater than the upper limit of the compensation value, The new compensation value is limited to If the new compensation value is less than the lower limit of the compensation value. The new compensation value is limited to If the new compensation value is at the upper limit of the compensation value. Compensation value lower limit In between, the original calculation results remain unchanged.

[0015] In some embodiments, the method further includes the following steps: Measurement data, calculation data, and process parameters are stored in real time during the closed-loop adaptive adjustment process for traceability.

[0016] In some embodiments, a closed-loop adjustment device for the process parameters of precision components is also provided, the device comprising: The first dimension measurement unit is used to measure the key dimensions of component A and component B for assembly and mating, respectively, to obtain the actual measured dimensions of component A and component B. The first calculation module is used to calculate the optional dimensions of component C based on the preset target stroke, the current compensation value, the actual measured dimensions of component A, and the actual measured dimensions of component B. The second dimension measurement module is used to complete the delivery of part C according to the selected dimensions, and to perform key dimension measurements on part C to obtain the actual measured dimensions of part C; The second calculation module is used to calculate the theoretical stroke of the assembled component after parts A, B, and C are assembled, based on the measured dimensions of parts A, B, and C. The third measurement module is used to measure the overall dimensions of the assembled assembly to obtain the actual measured stroke of the assembly. The third calculation module is used to calculate the travel deviation between the measured travel and the theoretical travel. The update module is used to calculate the batch average deviation based on multiple sets of travel deviation data within the batch, and update the new compensation value by combining the learning coefficient. The adjustment module is used to perform closed-loop adaptive adjustment by using the new compensation value as the updated process parameter.

[0017] In some embodiments, an electronic device is also provided, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the closed-loop adjustment method for precision component selection process parameters described in any of the above embodiments are performed.

[0018] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, performs the steps of the closed-loop adjustment method for precision component matching process parameters as described in any one of the preceding embodiments.

[0019] This application describes a closed-loop adjustment method and device for the process parameters of precision parts selection. It constructs an integrated closed-loop control process encompassing measurement, calculation, feedback, and adjustment, enabling online adaptive optimization of process parameters. Relying on a dual measurement mechanism of single-piece measurement and optional part retesting, coupled with deviation anomaly rejection and compensation value limit protection, it effectively offsets disturbances such as batch differences, temperature drift, and tooling wear, significantly improving assembly accuracy and product consistency, stabilizing production process indicators, and reducing rework and scrap rates. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of the closed-loop adjustment method for the process parameters of precision component selection according to an embodiment of this application is shown; Figure 2 This paper shows a schematic diagram of the closed-loop adjustment device for the process parameters of precision components according to an embodiment of this application. Figure 3 A schematic diagram of the structure of the electronic device described in an embodiment of this application is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0023] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0025] In view of the technical problems mentioned in the background art, this application provides a method and device for closed-loop adjustment of process parameters for precision parts, which can realize online self-adaptation and autonomous optimization of process parameters, and reduce manual debugging work.

[0026] See the instruction manual appendix Figure 1 This application provides a closed-loop adjustment method for the process parameters of precision parts selection, the method comprising the following steps: S1. Measure the key dimensions of component A and component B used for assembly and mating, and obtain the actual measured dimensions of component A and component B. S2. Calculate the selected dimensions of component C based on the preset target stroke, current compensation value, actual measured dimensions of component A, and actual measured dimensions of component B. S3. Based on the selected dimensions, complete the outbound delivery of component C, and perform key dimension measurements on component C to obtain the actual measured dimensions of component C; S4. Calculate the theoretical stroke of the assembled component after parts A, B, and C are assembled based on the actual measured dimensions of parts A, B, and C. S5. Measure the overall dimensions of the assembled components to obtain the measured stroke of the components. S6. Calculate the travel deviation between the measured travel and the theoretical travel; S7. Based on multiple sets of travel deviation data within a batch, calculate the average deviation of the batch and update the new compensation value by combining the learning coefficient. S8. Use the new compensation value as the updated process parameter and perform closed-loop adaptive adjustment.

[0027] Step S1 mainly involves collecting the original dimensions of the parts to be assembled, accurately obtaining the actual key dimensions of parts A and B, eliminating the basic errors caused by individual differences in parts, and providing real and reliable original data for the subsequent selection and matching calculation of part C.

[0028] Specifically, high-precision inspection is conducted on the key dimensions of the assembly fit between parts A and B to be assembled. The measuring equipment uses industrial precision dimensional sensors to ensure measurement accuracy meets the requirements of precision assembly conditions. Parts sequentially enter their corresponding measuring stations, and after the equipment completes positioning, sampling, and data reading, it accurately obtains the measured dimensions of part A. Measured dimensions of component B After the measurement is completed, the two sets of measured dimensional data are uploaded in real time to the computing control unit via an industrial communication link for storage and preprocessing. The independent measurement at both workstations avoids interference between parts, ensuring the accuracy of the basic measurement data.

[0029] Step S2 mainly combines the product design standards, current dynamic compensation parameters and the measured dimensions in step S1 to calculate the theoretical optimal matching dimensions of component C, so that the three components approach the preset standard stroke after assembly, thereby controlling the assembly gap and assembly accuracy from the source; at the same time, the compensation value is used to offset systematic working condition errors such as temperature, tooling, and tool wear.

[0030] Specifically, the target stroke is preset according to the product assembly process requirements. This represents the standard target travel of the assembled assembly after components A, B, and C are assembled; the currently used compensation value. These are the dynamic process compensation parameters currently in use after the previous closed-loop process iteration, used to offset systematic errors such as temperature drift, tooling deformation, tool wear, and batch differences in parts. They are calculated according to a preset formula. = - + - The theoretical selection dimensions for component C are obtained by solving the problem. These selection dimensions are the preferred outgoing dimensions for component C, ensuring that the assembled three parts approximate the standard target stroke. After the calculation, the selection dimensions are used... Proceed with the outbound shipment.

[0031] Step S3 involves two aspects: firstly, automatically shipping component C according to the theoretically selected dimensions to achieve precise size matching; secondly, performing a second retest on component C after shipping to identify errors and establish a dual measurement and verification mechanism to further improve data accuracy.

[0032] Specifically, the system automatically selects matching component C from the component material warehouse and transports it to the measurement station. Then, using a high-precision measuring device, the key assembly dimensions of component C are re-measured with high precision to eliminate dimensional deviations caused by material storage, transportation, and clamping, ultimately obtaining the actual measured dimensions of component C. After the retest is completed, the actual measured dimensions of component C will be recorded. Uploaded to the computing control unit in real time.

[0033] Step S4 mainly involves deriving the ideal theoretical stroke of the assembled component based on the measured dimensions of all three components A, B, and C, establishing a theoretical assembly benchmark, and comparing it with the actual assembly results to quantify the assembly deviation.

[0034] Specifically, the calculation and control unit summarizes the three sets of measured dimensional data. , , Based on the calculation formula corresponding to the assembly and mating relationship of the parts. = - + Calculate the theoretical travel of the assembly after parts A, B, and C are assembled.

[0035] Step S5 mainly involves collecting the actual travel dimensions of the assembly after the parts are assembled, obtaining the actual assembly results, and forming an actual assembly benchmark to reflect the actual assembly state under the current whole process.

[0036] Specifically, components A, B, and C are assembled according to the assembly process to form an integrated mating part D, which is then transported to the measurement station. High-precision measurement is then performed on the overall assembly stroke of the assembly to obtain the measured stroke of the assembly in its actual assembled state. The measured data is then uploaded to the computing control unit in real time.

[0037] Step S6 mainly involves calculating the difference between theoretical and measured values ​​to quantify the assembly error of a single product group and intuitively determine whether the current process parameters are suitable for the on-site working conditions.

[0038] Specifically, the theoretical travel distance retrieved by the calculation control unit. Compared with the measured journey Perform interpolation = - The travel deviation is obtained. Travel deviation The deviation value directly reflects the assembly accuracy of a single product: the smaller the deviation value, the closer the assembly effect is to the theoretical standard and the better the assembly consistency; the larger the deviation value, the more it indicates that the current process compensation parameters are no longer suitable for the on-site working conditions and parameter correction is required.

[0039] Step S7 mainly involves statistically analyzing deviations on a production batch basis to avoid the impact of random interference and accidental errors in individual products, and accurately assessing the overall operating conditions of the entire production line for the whole batch; combined with the learning coefficient, the compensation value is iteratively updated to achieve adaptive optimization of process parameters and proactively compensate for various systematic errors.

[0040] Specifically, an abnormal data removal step is added before performing batch deviation statistics and compensation value updates. A maximum travel deviation value is preset. Minimum deviation from travel Two judgment thresholds are used to verify the travel deviation of each product set within a batch. This represents the travel deviation data corresponding to the kth set of assembled products within this batch. Only data within the range shown is retained. Within the valid travel deviation data range, abnormal data exceeding the threshold is automatically removed. Then, based on the total quantity M of a single batch of products and the filtered valid travel deviation data, the formula is applied... The batch average deviation is calculated. This step filters out random outliers caused by accidental defects in parts, momentary equipment interference, and human error, preventing abnormal data from interfering with the overall deviation statistics. This ensures the accuracy of subsequent batch average deviation calculations and process parameter updates, and improves system operational stability and parameter adjustment reliability.

[0041] Then a preset learning coefficient is introduced. (0< <1, select the smaller value for large fluctuations, and the larger value for small fluctuations (generally 0.1~0.3), combined with the currently used compensation value. Deviation from batch average Perform iterative calculations according to the formula. The new process compensation value adapted to the current operating conditions is calculated. Learning coefficient. It can be flexibly set according to the fluctuation of production line conditions to ensure the stability and convergence of parameter adjustments.

[0042] Step S8 mainly involves replacing and issuing process parameters so that subsequent production can directly use the optimized new parameters. By cyclically executing all steps, a permanent closed-loop control link is established, which enables assembly deviations to continuously converge and maintains assembly accuracy and matching consistency in the long term, without the need for manual shutdown to change parameters throughout the process.

[0043] Specifically, the calculation control unit will calculate the new compensation value. The original compensation value is replaced as the standard process parameter for the next cycle and distributed to each execution unit. The entire system continues to run the production line online, continuously executing steps S1-S8 in a loop. As production continues, the process compensation value is continuously iterated and optimized in small increments based on the assembly deviation of each batch, and the assembly stroke deviation gradually converges, ultimately maintaining the assembly accuracy and part selection consistency within a stable range over the long term.

[0044] It should be noted that, in order to prevent abnormal data from being misinterpreted, this application also adds a compensation value. Limit protection, setting upper and lower limits for compensation values. , This is used to define the legal operating range of the compensation value. The updated compensation value is judged and corrected according to the following rules: =

[0045] That is, if the new compensation value obtained is greater than the upper limit of the compensation value. The new compensation value is limited to If the new compensation value is less than the lower limit of the compensation value. The new compensation value is limited to If the new compensation value is at the upper limit of the compensation value. Compensation value lower limit The original calculation results remain unchanged. This measure can prevent compensation values ​​from exceeding limits due to extreme deviations in data or sudden operating conditions, avoid assembly failures and system malfunctions caused by abnormal deviations in process parameters, ensure that compensation parameters are always iteratively adjusted within a reasonable range, and further improve the safety and stability of the entire method.

[0046] Furthermore, the method of this application collects and persistently stores component measurement data, intermediate calculation results, and dynamically updated process parameters generated at each stage throughout the entire process. All data is archived according to production batch and product number, forming a complete data chain. Relying on this data storage system, bidirectional linkage and traceability between quality inspection data and process parameters can be achieved. It can not only reverse-check the part dimensions and process compensation parameters at the corresponding stage based on assembly quality anomalies, but also review the variation patterns of assembly accuracy by combining historical process parameters. At the same time, complete historical data can also provide data support for subsequent process optimization, algorithm iteration, and production problem analysis, helping to continuously improve the production process.

[0047] As can be seen, the proposed method for closed-loop adjustment of process parameters for precision parts constructs a closed-loop control mechanism covering measurement, calculation, adjustment feedback, and re-measurement, eliminating assembly errors caused by clamping, environment, and parts batches, improving the dimensional consistency of mating parts and the first-pass yield of products; enabling online adaptive and autonomous optimization of process parameters, reducing manual debugging work; and building a traceable and iterative intelligent selection control system to meet the needs of automated assembly of various precision parts.

[0048] Based on the same inventive concept, this application also provides a closed-loop adjustment device for the process parameters of precision parts selection. Since the principle of the device in this application is similar to the closed-loop adjustment method for the process parameters of precision parts selection described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0049] As per the instruction manual Figure 2 As shown in the figure, this application embodiment also provides a closed-loop adjustment device for the process parameters of precision parts selection, the device comprising: The first dimension measurement unit 201 is used to measure the key dimensions of component A and component B for assembly and mating, respectively, to obtain the actual measured dimensions of component A and component B. The first calculation module 202 is used to calculate the optional dimensions of component C based on the preset target stroke, the current compensation value, the actual measured dimensions of component A, and the actual measured dimensions of component B. The second dimension measurement module 203 is used to complete the delivery of part C according to the selected dimensions, and to perform key dimension measurements on part C to obtain the actual measured dimensions of part C; The second calculation module 204 is used to calculate the theoretical stroke of the assembled component after the components A, B, and C are assembled based on the measured dimensions of component A, component B, and component C. The third measurement module 205 is used to measure the overall dimensions of the assembled assembly to obtain the actual measured stroke of the assembly. The third calculation module 206 is used to calculate the travel deviation between the measured travel and the theoretical travel. The update module 207 is used to calculate the batch average deviation based on multiple sets of travel deviation data within the batch, and update the new compensation value by combining the learning coefficient. The adjustment module 208 is used to perform closed-loop adaptive adjustment by using the new compensation value as the updated process parameter.

[0050] The precision component selection process parameter closed-loop adjustment device described in this application achieves online self-adaptation and autonomous optimization of process parameters by constructing a full-process closed-loop control mechanism, thereby reducing manual debugging operations.

[0051] Based on the same concept of the present invention, as shown in the appendix to the specification. Figure 3 As shown in the figure, an embodiment of this application provides the structure of an electronic device 300, which includes: at least one processor 301, at least one network interface 304 or other user interface 303, memory 305, and at least one communication bus 302. The communication bus 302 is used to realize the connection and communication between these components. The electronic device 300 may optionally include a user interface 303, including a display (e.g., touch screen, LCD, CRT, holographic imaging, or projector, etc.), a keyboard, or a clicking device (e.g., mouse, trackball, touchpad, or touch screen, etc.).

[0052] Memory 305 may include read-only memory and random access memory, and provides instructions and data to processor 301. A portion of memory 305 may also include non-volatile random access memory (NVRAM).

[0053] In some implementations, memory 305 stores executable modules or data structures, or subsets thereof, or extended sets thereof: The 3051 operating system contains various system programs used to implement various basic business functions and handle hardware-based tasks. Application module 3052 contains various applications, such as desktop (launcher), media player (MediaPlayer), browser (Browser), etc., to implement various application services.

[0054] In this embodiment of the application, by calling the program or instructions stored in the memory 305, the processor 301 is used to execute the steps of a closed-loop adjustment method for the process parameters of precision parts selection.

[0055] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs steps such as those in a closed-loop adjustment method for process parameters of precision parts.

[0056] Specifically, the storage medium can be a general-purpose storage medium, such as a portable disk or hard drive. When the computer program on the storage medium is run, it can achieve online self-adaptation and autonomous optimization of process parameters by constructing a closed-loop control mechanism for the entire process, thereby reducing manual debugging work.

[0057] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, and the indirect coupling or communication connection of the apparatus or units may be electrical, mechanical, or other forms.

[0058] 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.

[0059] In addition, the functional units in the embodiments provided in this application 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.

[0060] If a function 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, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] Finally, it should be noted that the above embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, 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. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A closed-loop adjustment method for process parameters of precision parts selection, characterized in that, The method includes the following steps: The key dimensions of component A and component B used for assembly and mating were measured respectively to obtain the actual measured dimensions of component A and component B. Calculate the optional dimensions of component C based on the preset target stroke, current compensation value, actual measured dimensions of component A, and actual measured dimensions of component B; Based on the selected dimensions, component C is shipped out, and key dimensions of component C are measured to obtain the actual measured dimensions of component C. Calculate the theoretical stroke of the assembled component based on the measured dimensions of component A, component B, and component C. The overall dimensions of the assembled component are measured to obtain the actual stroke of the component. Calculate the travel deviation between the measured travel and the theoretical travel; Based on multiple sets of travel deviation data within a batch, the average deviation of the batch is calculated, and a new compensation value is obtained by updating the learning coefficient. The new compensation value is used as the updated process parameter for closed-loop adaptive adjustment.

2. The closed-loop adjustment method for process parameters of precision parts selection according to claim 1, characterized in that, in, The optional dimensions of component C are calculated using the following formula: = - + - Among them, the measured dimensions of component A and the measured dimensions of component B are respectively... , , For the preset target itinerary, This is the current compensation value.

3. The closed-loop adjustment method for process parameters of precision parts selection according to claim 2, characterized in that, in, The theoretical stroke of the assembled component, and the stroke deviation between the measured stroke and the theoretical stroke, are calculated using the following formula: = - = - + in, The measured dimensions are for component C. , These are the theoretical and measured strokes of the assembled components, respectively.

4. The closed-loop adjustment method for process parameters of precision parts according to claim 3, characterized in that, The calculation of the batch average deviation based on multiple sets of travel deviation data within a batch includes the following steps: Preset maximum travel deviation Minimum deviation from travel ; For the travel deviation corresponding to the kth product in the batch Filter and retain those that meet the requirements. Effective travel deviation data; Based on the total quantity M of a single batch of products and the effective travel deviation data after screening, according to the formula The average batch deviation was calculated.

5. The closed-loop adjustment method for process parameters of precision parts according to claim 4, characterized in that, The new compensation value is obtained using the following formula: in, The parameters are for learning, and 0 < <1.

6. The closed-loop adjustment method for process parameters of precision parts according to claim 5, characterized in that, After obtaining the new compensation value, the following steps are also included: Based on the preset compensation value upper limit Lower limit of compensation value The obtained new compensation value is then judged and revised; whereby, if the obtained new compensation value is greater than the upper limit of the compensation value, The new compensation value is limited to If the new compensation value is less than the lower limit of the compensation value. The new compensation value is limited to If the new compensation value is at the upper limit of the compensation value. Compensation value lower limit In between, the original calculation results remain unchanged.

7. The closed-loop adjustment method for process parameters of precision parts according to claim 6, characterized in that, The method further includes the following steps: Measurement data, calculation data, and process parameters during the closed-loop adaptive adjustment process are stored in real time for traceability.

8. A closed-loop adjustment device for process parameters of precision parts, characterized in that, The device includes: The first dimension measurement unit is used to measure the key dimensions of component A and component B for assembly and mating, respectively, to obtain the actual measured dimensions of component A and component B. The first calculation module is used to calculate the optional dimensions of component C based on the preset target stroke, the current compensation value, the actual measured dimensions of component A, and the actual measured dimensions of component B. The second dimension measurement module is used to complete the delivery of part C according to the selected dimensions, and to perform key dimension measurements on part C to obtain the actual measured dimensions of part C; The second calculation module is used to calculate the theoretical stroke of the assembled component after parts A, B, and C are assembled, based on the measured dimensions of parts A, B, and C. The third measurement module is used to measure the overall dimensions of the assembled assembly to obtain the actual measured stroke of the assembly. The third calculation module is used to calculate the travel deviation between the measured travel and the theoretical travel. The update module is used to calculate the batch average deviation based on multiple sets of travel deviation data within the batch, and update the new compensation value by combining the learning coefficient. The adjustment module is used to perform closed-loop adaptive adjustment by using the new compensation value as the updated process parameter.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of a closed-loop adjustment method for process parameters of precision component selection as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of a closed-loop adjustment method for precision component selection process parameters as described in any one of claims 1 to 7.

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