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CN-122022564-A - Quality control method and system based on battery manufacturing whole process

CN122022564ACN 122022564 ACN122022564 ACN 122022564ACN-122022564-A

Abstract

The invention discloses a quality control method and a system based on a full process of battery manufacturing, wherein the method comprises the steps of acquiring full process quality data covering a supplier end, a factory end and an after-sales end, developing process statistics analysis and process system analysis based on the data, developing a quality control system integrating four functional modules, wherein the process statistics analysis realizes 9-large core process threshold judgment and AI exception handling through a key parameter comprehensive evaluation formula, and the process system analysis builds a three-large management system through a quantitative evaluation formula and combines a quality adaptation degree evaluation formula to optimize a process scheme. The invention realizes transformation from manual driving to system driving, ensures stable and consistent quality of batch batteries, and is suitable for the technical field of battery manufacturing.

Inventors

  • ZHANG LIANG

Assignees

  • 一汽大众动力科技有限公司

Dates

Publication Date
20260512
Application Date
20260116

Claims (10)

  1. 1. A quality control method based on the whole battery manufacturing process is characterized by comprising the steps of obtaining the whole battery manufacturing process quality data, wherein the whole battery manufacturing process quality data cover a supplier end, a factory end and an after-sales end, respectively developing process statistical analysis and process system analysis based on the whole battery manufacturing process quality data, and developing a quality control system according to the process statistical analysis result and the process system analysis result, wherein the quality control system is provided with a whole quality state display module, a product quality inspection module, a production process control module and a quality tracing and knowledge management module.
  2. 2. The method of claim 1, wherein obtaining battery manufacturing full-process quality data comprises obtaining supplier side quality data comprising part size, material inspection results, strength inspection results, production process quality data, part lot, warehouse time, and passivation time, obtaining factory side quality data comprising personnel data, dimension data, material inspection data, product inspection data, quality management data, and equipment data, and obtaining after-market side quality data comprising after-market fault type, fault occurrence time, and vehicle base data.
  3. 3. The method of claim 1, wherein developing a process statistical analysis based on the full process quality data comprises comprehensively evaluating a formula by key parameters: ; threshold value determination is carried out on key process parameters in the battery manufacturing process, wherein the key process parameters comprise dimensions, welding parameters, gluing parameters, waxing parameters, milling heights, tightening data, airtight values, pressing parameters, temperature and humidity and electrical inspection data, and the key process parameters comprise The comprehensive deviation degree of the technological parameters is characterized, Is the actual value of the jth critical process parameter, Is the mean value of the jth key process parameter, The standard deviation for the jth critical process parameter, k is the total number of critical process parameters, The method comprises the steps of obtaining a process capability index of a jth key process parameter, carrying out artificial intelligent analysis on auxiliary data of a corresponding process according to the condition of the critical process parameter out of tolerance or abnormality, wherein the auxiliary data comprise positioning pin measured values, clamp adjustment records, humiture, single-piece size and defect sample data, triggering alarm on the abnormal condition of the process parameter according to a preset business rule, outputting abnormality reasons and associated data, and simultaneously realizing independent fine adjustment of the process parameter or prediction of the replacement time of the critical part based on an analysis result.
  4. 4. The method of claim 1, wherein developing a process system analysis based on the full process quality data comprises quantitatively evaluating a formula for a process system effectiveness as: ; Wherein the method comprises the steps of Compliance term duty cycle in the personnel management system, To produce the normal operation item duty ratio in the security system, To be the closed loop problem duty ratio in the quality closed loop management system, Weighted for each system and The method comprises the steps of establishing a personnel management system through the formula, wherein the personnel management system comprises personnel qualification training and examination, post card punching tracing, task allocation reminding and electronic standard card operation monitoring, establishing a production guarantee system, wherein the production guarantee system comprises equipment fault grading treatment, parameter change management, clamp locating pin management, material validity period management and control, batch tracing and trial installation management and control, and establishing a quality closed loop management system, wherein the quality closed loop management system comprises dynamic inspection and updating, product release grading judgment, problem statistics analysis and measure verification, process self-adaption adjustment and environment temperature and humidity and cleanliness management and control.
  5. 5. The method of claim 1, wherein the quality fitness evaluation formula is used when developing a quality control system: ; Wherein the method comprises the steps of The processing complexity is characterized by the fact that, Is the number of process links of the ith procedure, The minimum process precision requirement of the ith working procedure is met, and n is the total number of working procedures; The quality cost is characterized by that, For the quality inspection cost of the i-th process, Is the abnormal quality reworking cost of the ith working procedure, Input cost for quality prevention of the ith process; is preset with weight index The maximum value of the scheme fitness corresponds to the optimal process scheme.
  6. 6. The method of claim 3, wherein the algorithms employed in the artificial intelligence analysis include a random forest algorithm for correlating multidimensional auxiliary data to locate anomaly sources, a neural network model for dynamic adjustment of process parameters, a neural network model for life prediction of parts, a linear regression algorithm for quality state determination of electrical inspection, a decision tree model for torque-angle curve defect identification in the tightening process, and an image identification technique.
  7. 7. The method of claim 4, wherein the personnel management system is implemented by performing on-line and off-line combined training according to post electronic operation instruction books, inputting employee information into post lists after qualification, scanning codes and punching cards at stations before the employees go up the posts, automatically binding employee information and product quality data produced on the same day by a system, scanning codes to trigger an alarm when the post adjustment is untrained, prompting a team regulator when the defect rate of the products exceeds standard, generating a future week task list according to a production plan, receiving task notification by the employees through a mobile phone end, a webpage end or a mailbox and prompting 1 hour before expiration, automatically distributing tasks and acquiring relevant experience data when the defects of the products or regular tasks occur, managing task completion conditions in a background in real time and preferentially pushing emergency tasks, making each post operation standard and step into an electronic label card, defining QRK post daily sampling content, automatically comparing the employee sampling data with the standard, triggering an alarm when the post adjustment exceeds standard, setting visual monitoring equipment to monitor operation steps and inspection duration in real time, automatically recording and prompting rule violations and correcting.
  8. 8. The method of claim 4, wherein the specific implementation of the production guarantee system comprises the steps of classifying equipment faults into A, B, C types, immediately stopping an A type alarm and notifying maintenance personnel of field processing, triggering a manual processing reminder for 3 times within 1 hour when the B type alarm reaches 5 times, automatically generating spot inspection tasks, attaching fault information to upper and lower process quality information, a processing method and historical experience data, enabling process parameter change to be confirmed through quality department examination, recording a change application form by a system, automatically comparing actual parameters with new parameters after the change application form, judging unqualified and triggering alarms when deviation exceeds +/-2%, establishing a history file for each clamp positioning pin, checking a designated positioning pin when 3 times of size out-of-tolerance alarms continuously occur, performing conventional diameter measurement once every 3 months, enabling measured value and original deviation to exceed a preset value, triggering replacement reminding, performing two-dimensional code or information on production materials and single piece production time, automatically judging the effective period before use, automatically alarming when the effective period is close to the effective period reminding, enabling the tracking glue barrel to be used in a scanning mode, enabling the error to be judged and the effective period to be within the effective period, enabling the special part quality to be only required to be detected when the system is installed and the quality of the special part is required to be updated, enabling the system to enter a special quality control data to be only when the quality of the special part is required to be updated, and the quality of the special part is required to be updated when the quality is required to be detected.
  9. 9. The method of claim 4 wherein the implementation of the quality closed loop management system comprises automatically summarizing quality inspection data and after-market complaint information daily or weekly by the system, updating TOP problem list and pushing to related posts, checking the execution of previous process improvement measures in the next process production, judging whether to add inspection actions in combination with electronic standard cards, formulating a release algorithm and product release rules for each process, setting up an important parameter deviation exceeding + -5% immediate release analysis prohibition release, counting the number of defects while the general parameter deviation is between + -5% and + -10%, periodically counting production process problem data by the system, generating a first time cross-check qualification rate and TOP problem list, providing support for deep analysis by the related process data, automatically sending to corresponding equipment and staff after the engineer enters the improvement measures, generating an execution tracking account, automatically collecting related production data after the improvement measures are validated, comparing with the execution data to verify the validity, generating a problem list and assigning tasks according to the quality defect database, automatically marking closed loop or re-triggering the automatic verification rules after the tasks are completed, generating a special-purpose defect count by the system, automatically counting the production process problem data by the system, collecting the related process problem list by the system, analyzing the quality defect list by the system, analyzing the related process data by the system, analyzing the quality error count down time, analyzing the related process problem list, generating a histogram, generating a real-time map, and the related process data by the related process data, providing a real-time analysis data, and analyzing the related process data, and analyzing the quality analysis data by the related to the process data, and analyzing the process data, and providing a special statistics to be processed by the system, and analyzing the related to the quality analysis data, and the process data, and the real-time-needed by the process has real-time analysis data, and real-time statistics is adjusted by the related, the heating equipment is used for adjusting the temperature and the humidity in a linkage way, recording the exceeding time when the humidity exceeds the standard, continuously placing the heating equipment for a specified duration in a qualified temperature and humidity environment to enable the heating equipment to continue processing, periodically issuing an environment cleanliness checking task, uploading checking photos and data by staff, counting the number and the proportion of defects of the cleanliness of the product, and triggering improvement reminding when the proportion of defects of a certain type exceeds 10%.
  10. 10. The quality control system based on the whole battery manufacturing process is characterized by being used for implementing the method of any one of claims 1 to 9, and comprises a whole-end quality state display module, a front page visual billboard, a main data display area, a factory data display area and a post-sale data display area, wherein the main data display area is used for setting a supplier end, a factory end and a post-sale end, and a data drilling function is used for supporting penetrating and viewing of bottom layer detail data; the system comprises a product quality inspection module, a development Audi test function, a development station state management function, a quality tracing and knowledge management module, a development problem management function, a development knowledge stock storage function, a knowledge document uploading and downloading and searching support, a document checking mechanism and a development period task reminding function, wherein the development Audi test function supports recording test scores and defect descriptions and generating reports, the development strength test function is used for automatically collecting data and judging results, the development quality review function supports recording test data on line, automatically compares standard ranges and triggers unqualified alarms, and associates unique product identification codes to realize tracing, the production process management module is used for collecting parameters in real time and drawing trend curves, the parameters exceed a preset range to trigger audible and visual alarms, the development station state management function displays station states in Gantt charts, supports task allocation and adjustment, the development period task reminding function generates task lists and pushes according to preset periods, records completion conditions and triggers overtime reminding, the quality tracing and knowledge management module is used for supporting recording quality problems and associating related data, a processing flow node is set and sends node reminding, and a one-file tracing function is used for constructing a full-flow data chain through a unique product identification code, supporting tracing result export, a knowledge stock storage function is used for establishing document classification catalogue, and knowledge document uploading and downloading and searching support.

Description

Quality control method and system based on battery manufacturing whole process Technical Field The invention relates to the technical field of battery manufacturing, in particular to a quality control method and system based on a battery manufacturing whole process. Background With the rapid development of new energy industry, the battery is used as a core energy storage component, and the stability of the mass production process directly determines the safety and reliability of downstream terminal products. In the battery manufacturing process, accurate analysis, statistics and association integration are required to be carried out on the whole process, and all elements of a man-machine material method are controlled through a multi-dimensional management method, so that the quality stability of batch products can be ensured. However, the conventional battery manufacturing quality control mode mainly relies on manual operation, and has significant technical drawbacks. In the traditional mode, quality data collection depends on manual statistics, so that the timeliness of information collection is poor, real-time changes in the production process cannot be captured timely, a statistical method is single, the problem investigation period is long, and the abnormality of technological parameters is difficult to respond quickly. Meanwhile, errors or counterfeiting easily occur in manual recording, so that a quality analysis result is separated from an actual production condition, reliable data support cannot be provided for process optimization, and potential association problems among process parameters are difficult to identify. In addition, the whole process control needs to cover a complete link of a process start-problem closed loop, but partial links are easy to miss in the manual pushing process, the process requirements are not completely followed, an effective node tracking mechanism is lacked, and the quality problem discovery-treatment-verification closed loop period is long and the efficiency is low. The manual work is also difficult to integrate multi-link and multi-dimensional data resources, even if a single quality problem is found, the problem of locating the upstream and downstream ring nodes cannot be quickly associated, and even batch quality hidden danger can be caused by flow break points, so that the quality stability and consistency of battery products are seriously affected. Therefore, a technical scheme capable of breaking the limitation of manual control and realizing full-flow digital and intelligent quality control is needed, and the core problems of multiple information breakpoints, analysis lag, low closed-loop efficiency and the like in the traditional mode are solved. Disclosure of Invention The invention aims to solve the defects in the prior art, and provides a quality control method based on a battery manufacturing whole process, which comprises the following steps of obtaining battery manufacturing whole process quality data, wherein the whole process quality data covers a supplier end, a factory end and an after-sale end; developing a quality control system according to the process statistical analysis result and the flow system analysis result, wherein the quality control system is provided with a full-end quality state display module, a product quality inspection module, a production process control module and a quality tracing and knowledge management module. Preferably, acquiring the full-process quality data of battery manufacturing comprises acquiring supplier end quality data, wherein the supplier end quality data comprises part size, material inspection result, strength inspection result, production process quality data, part batch, warehouse-in time, warehouse-out time and passivation time, acquiring factory end quality data, wherein the factory end quality data comprises personnel data, size data, material inspection data, product inspection data, quality management data and equipment data, and acquiring after-sale end quality data, wherein the after-sale end quality data comprises after-sale fault type, fault occurrence time and vehicle basic data. Further preferably, the developing process statistical analysis based on the full-process quality data includes comprehensively evaluating a formula by key parameters: ; threshold value determination is carried out on key process parameters in the battery manufacturing process, wherein the key process parameters comprise dimensions, welding parameters, gluing parameters, waxing parameters, milling heights, tightening data, airtight values, pressing parameters, temperature and humidity and electrical inspection data, and the key process parameters comprise The comprehensive deviation degree of the technological parameters is characterized,Is the actual value of the jth critical process parameter,Is the mean value of the jth key process parameter,The standard deviation for the jth critical process parameter, k is the tot