SPC quality modeling method, device, equipment, medium and product

Abstract

The invention discloses an SPC quality modeling method, device and equipment, a medium and a product, and belongs to the technical field of production quality control, and the method comprises the steps: obtaining a sampling value of a target parameter in a specified time range under a target process and the specification upper and lower limits of the target parameter; calculating a capability index of the target parameter in a specified time range according to the sampling value of the target parameter and the upper and lower limits of the specification; and displaying a header by adopting an Element UI according to the capability index, and drawing a control chart reflecting the stability and capability of the production process by adopting Echart. According to the method, key indexes in the production process are monitored in real time, a statistical method is used for analyzing data, and production deviation or potential quality problems are found in time, so that accurate control over the production process is achieved, and the production quality and process stability of the lithium battery are improved.

Description

Technical Field

[0001] This invention relates to the field of production quality control technology, and in particular to an SPC quality modeling method, apparatus, equipment, medium, and product. Background Technology

[0002] In the various stages of lithium battery production, verifying whether the production process is in a statistical steady state and whether the process capability is sufficient are key steps to ensure product quality. Control limits can be calculated using data collected during the production process, and analytical control charts, histograms, or process capability analyses can be performed. If any of these conditions are not met, on-site personnel must identify the cause, make improvements, and repeat production and analysis.

[0003] After analysis, control charts are used for monitoring. At this point, the control limits of the control chart have been determined based on the results of the analysis phase. Production process data is promptly plotted onto the control chart, and the chart is closely observed. Fluctuations in the points on the control chart can indicate whether the process is under control or out of control. If out of control is detected, the cause must be identified and its impact eliminated as soon as possible. Monitoring fully demonstrates the preventive control role of SPC (Statistical Process Control).

[0004] In practical factory applications, each control item must go through the above two stages, and this process from analysis to monitoring is repeated when necessary. Current technology lacks a complete system for displaying various data points and uses an outdated framework, requiring manual statistical analysis, which wastes human and material resources and fails to achieve accurate process capability analysis. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an SPC quality modeling method, apparatus, equipment, medium and product, which solves the technical problems of the prior art being wasteful of human and material resources and unable to accurately achieve process capability analysis.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] In a first aspect, the present invention provides an SPC quality modeling method, comprising:

[0008] Obtain the sampled values ​​of the target parameters within a specified time range and the upper and lower limits of the target parameters' specifications under the target process;

[0009] Calculate its capability index within a specified time range based on the sampled values ​​and upper and lower limits of the target parameters;

[0010] Based on the aforementioned capability indicators, the header is displayed using Element UI, and control charts reflecting the stability and capability of the production process are drawn using Echarts.

[0011] The SPC quality modeling method provided by this invention aims to achieve precise control of the production process and improve the production quality and process stability of lithium batteries by monitoring key indicators in the production process in real time, analyzing data using statistical methods, and promptly identifying production deviations or potential quality problems.

[0012] Optionally, the sampling values ​​of the target parameters can be obtained through random sampling and full inspection.

[0013] Sampling inspection is less expensive and more efficient, making it particularly suitable for large-volume products; full inspection is very expensive and less efficient, making it suitable for products with extremely high quality requirements where non-conforming products can have serious consequences; staff can choose the required testing method based on the actual application scenario.

[0014] Optionally, the capability indicators include mean, range, standard deviation, UCL, LCL, CP, CPL, CPU, and CK.

[0015] Optionally, when the UCL and LCL are calculated based on the mean-range plot, the control plot coefficients are adjusted. Make dynamic adjustments;

[0016]

[0017] When the UCL and LCL are calculated based on the range diagram, the control chart coefficients are... Make dynamic adjustments;

[0018]

[0019]

[0020] In the formula, These are the dynamically adjusted control chart coefficients. This is the sensitivity coefficient. , For the range coefficient of variation, , For the range The standard deviation and mean.

[0021] When calculating the upper and lower control limits (UCL and LCL), the fixed control chart coefficients in the existing technology are transformed into variables that are dynamically adjusted with the range coefficient of variation. The process fluctuation status is fed back in real time through the range coefficient of variation, so as to achieve flexible optimization of the upper and lower control limits and improve the accuracy of control.

[0022] Optionally, abnormal data points in the capability indicators can be highlighted in red in the table header and the control chart.

[0023] Highlighting anomalies in red allows staff to quickly identify them and facilitates their analysis and resolution. Continuously collecting and analyzing historical anomaly data not only enables immediate optimization of current production but also provides data support for future process improvements.

[0024] Optionally, if the number of target parameters with abnormal capability indicators exceeds the quantity threshold, a quality warning will be issued.

[0025] Quality early warnings can promptly alert staff to analyze and handle anomalies, and also play a role in adjusting subsequent process parameters, thus helping to improve product quality.

[0026] Secondly, the present invention provides an SPC quality modeling apparatus, characterized in that it comprises:

[0027] The data acquisition module is configured to acquire sampled values ​​of target parameters within a specified time range and the upper and lower limits of the target parameters' specifications under the target process.

[0028] The indicator calculation module is configured to calculate the capability indicator of the target parameter within a specified time range based on the sampled value and upper and lower limits of the specification.

[0029] The chart drawing module is configured to display the table header using Element UI based on the capability indicators, and to draw control charts reflecting the stability and capability of the production process using Echart.

[0030] Thirdly, the present invention provides an electronic device, including a processor and a storage medium;

[0031] The storage medium is used to store instructions;

[0032] The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0033] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0034] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0036] This invention provides an SPC quality modeling method, apparatus, equipment, medium, and product. The modeling method monitors key indicators in the production process in real time, analyzes data using statistical methods, and promptly identifies production deviations or potential quality problems. It employs a combination of various graphical and chart-based approaches to comprehensively analyze the process capability of the production process, thereby achieving precise control over the production process and improving the production quality and process stability of lithium batteries. Furthermore, a dynamic coefficient adjustment algorithm based on process stability further reduces the false positive rate and enhances process capability stability. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the SPC quality modeling method provided in an embodiment of the present invention;

[0038] Figure 2 This is an example diagram of the SPC full inspection header data provided in an embodiment of the present invention;

[0039] Figure 3 This is an example diagram of the SPC full inspection scatter plot provided in the embodiment of the present invention;

[0040] Figure 4 This is an example diagram of the normal distribution of SPC full inspection provided in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0042] Example 1:

[0043] like Figure 1 As shown, this embodiment of the invention provides an SPC quality modeling method, including the following steps:

[0044] Step S1: Obtain the sampled values ​​of the target parameters within the specified time range and the upper and lower limits of the target parameters under the target process.

[0045] The upper and lower limits of the specification are denoted as USL and LSL, respectively. USL is the maximum value of the quality characteristic allowed by the customer or standard. Exceeding this value constitutes a non-conformity. LSL is the minimum value of the quality characteristic allowed by the customer or standard. Falling below this value constitutes a non-conformity.

[0046] The sampled equipment values ​​of the target process parameters are crucial data for analyzing whether the production process conforms to statistical stability, and this is the first step. The upper and lower limits of the set parameter specifications are also important elements for identifying anomalies. By quantifying the correlation between parameters and their upper and lower limits, we ensure that parameter optimization decisions are based on objective data and not subjective experience, thus improving the accuracy of control decisions.

[0047] The methods for obtaining sampled values ​​of target parameters include random sampling and full inspection.

[0048] Sampling inspection is less expensive and more efficient, making it particularly suitable for large-volume products; full inspection is very expensive and less efficient, making it suitable for products with extremely high quality requirements where non-conforming products can have serious consequences; staff can choose the required testing method based on the actual application scenario.

[0049] Step S2: Calculate the capability index of the target parameter within the specified time range based on the sampled values ​​and upper and lower limits of the specification.

[0050] Specifically, in this embodiment, the capability indicators include mean, range, standard deviation, UCL, LCL, CP, CPL, CPU, and CK.

[0051] UCL and LCL are the upper and lower limits of control, calculated based on process data, and are used to determine whether the process is stable.

[0052] When UCL and LCL are calculated based on the mean-range plot, the center line CP is the overall average. ,but:

[0053]

[0054] Divide the specified time range into multiple consecutive time periods, group the sampled values ​​within each time period into a subgroup, and calculate the average value of each subgroup. Range And calculate the overall average based on the number of groups. and the mean of the range .

[0055] When UCL and LCL are calculated based on the range diagram, the center line CP is the mean of the range. ,but:

[0056]

[0057] Specifically, in this embodiment, the control chart coefficients Make dynamic adjustments;

[0058]

[0059] When calculating UCL and LCL based on range charts, the control chart coefficients... Make dynamic adjustments;

[0060]

[0061]

[0062] In the formula, These are the dynamically adjusted control chart coefficients. This is the sensitivity coefficient. , For the range The coefficient of variation measures the degree of fluctuation in the range itself. , For the range The standard deviation and mean. Standard deviation The larger the value, the greater the difference in range between different subgroups. Sometimes the fluctuation is small, and sometimes it is large, indicating that the process is very unstable.

[0063] Specification limits are externally set "passing grades," defining the standard for product qualification. Control limits are the "normal fluctuation range" of an internal process, calculated from the process's own data, used to determine process stability and predictability. A stable and capable process should have control limits within specification limits. When calculating control limits (UCL and LCL), the fixed control chart coefficients in existing technologies are transformed into variables dynamically adjusted with the range coefficient of variation. The range coefficient of variation provides real-time feedback on process fluctuations, enabling flexible optimization of control limits and improving control accuracy.

[0064] CPU and CPL are the upper and lower limits of process capability indices.

[0065]

[0066] CPU measures the mean The degree to which the sample approaches the upper specification limit (USL); a smaller value indicates a higher risk of exceeding the USL. CPL measures the mean. The degree to which it approaches the lower specification limit (LSL) is indicated by the value; the smaller the value, the higher the risk of falling below the LSL.

[0067] Based on process parameters, production parameters and their upper and lower limits are obtained. Statistical formulas are then used to determine multiple data values ​​reflecting production capacity, including the "average level" or "center point" of a data set. This helps factory quality control understand the overall data situation; measure the deviation of data points from the average; assess the range or magnitude of data variation; and determine production process capacity metrics such as CPK, CPL, and CL. Comprehensive analysis of this data clearly and intuitively shows whether the production process conforms to statistical stability characteristics under specific equipment, production time periods, on-site operators, and material conditions. Based on data and real-time feedback, various factors affecting production process capacity are continuously optimized, thereby improving the work-in-process yield.

[0068] Step S3: Display the header using Element UI based on the capability indicators, and use Echart to draw a control chart reflecting the stability and capability of the production process.

[0069] Vue2: A progressive framework for building user interfaces that can be applied layer by layer from the bottom up.

[0070] ElementUI: A component library built on Vue 2, designed for developers, designers, and product managers. It provides supporting design resources to help websites quickly take shape.

[0071] Echart: Apache ECharts is an open-source JavaScript charting library that supports more than 20 chart types and more than a dozen components, enabling smooth interaction and multi-dimensional analysis of tens of millions of data points.

[0072] The front-end visualization uses the Vue2 framework + ElementUI for visualization, such as... Figure 2 The image shown is a sample header diagram of the capability index generated using the initial EOL test of the process, the target parameter being AC internal resistance, and the sampling method being full inspection.

[0073] Control charts include mean-range plots, normal distribution plots, scatter plots, etc. Figure 3 and Figure 4 These are examples of scatter plots and normal distribution plots for SPC full inspection, respectively.

[0074] In SPC quality control, the core is the analysis of quality characteristic data after products are rationally grouped. Normality analysis is integral to the entire quality characteristic data analysis process and is the most important tool for quality control. When using tools such as normal distribution curves, histograms, and control charts to analyze and control quality characteristic data, the normal distribution is the most crucial tool because, under normal circumstances, the distribution of product quality characteristic values ​​generally follows a normal or approximately normal distribution. Furthermore, the use of control charts requires that the sampled data conform to a normal distribution as a prerequisite. Finally, process capability analysis must also meet the condition of achieving a normal distribution. Therefore, the normal distribution is integral to the entire quality characteristic data analysis process. The significance of the normal distribution chart in SPC analysis lies in providing an intuitive and effective method to monitor and evaluate the stability and capability of the production process. Through the normal distribution chart, the distribution of data, outliers, and the range of data fluctuations can be clearly seen. This information is crucial for timely detection of problems in the production process, taking corrective measures, and improving product quality. To better assess the stability and capacity of the production process, we have also added a normal distribution plot, which includes a histogram and a normal curve.

[0075] In the headers and control charts, abnormal data points in the capability indicators are highlighted in red. This highlighting helps staff identify anomalies immediately and facilitates their analysis and resolution. Continuously collecting and analyzing historical anomaly data not only enables immediate optimization of current production but also provides data support for future process improvements.

[0076] If the number of target parameters with abnormal capability indicators exceeds the threshold, a quality warning will be issued. Quality warnings promptly alert staff to analyze and address these anomalies, and also play a role in adjusting subsequent process parameters, ultimately helping to improve product quality.

[0077] Through the above steps, the system can analyze whether the production process is statistically stable, display abnormal data that occurs during production based on configuration options, and automatically and promptly provide feedback on production capacity. This effectively prevents the production of large quantities of defective products and reduces the defect rate. It not only enhances the accuracy of product quality control but also creates a virtuous cycle in improving the yield rate. Through continuous data analysis and feedback, the production line can continuously optimize itself, achieving continuous improvement in product quality.

[0078] Example 2:

[0079] This invention provides an SPC quality modeling apparatus, characterized in that it includes:

[0080] The data acquisition module is configured to acquire sampled values ​​of target parameters within a specified time range and the upper and lower limits of the target parameters' specifications under the target process.

[0081] The indicator calculation module is configured to calculate the capability indicators of the target parameters within a specified time range based on the sampled values ​​and upper and lower limits of the specifications.

[0082] The chart drawing module is configured to use Element UI to display table headers based on capability indicators and Echart to draw control charts reflecting the stability and capability of the production process.

[0083] The SPC quality modeling device provided by this invention aims to monitor key indicators in the production process in real time, analyze data using statistical methods, and promptly identify production deviations or potential quality problems, thereby achieving precise control over the production process and improving the production quality and process stability of lithium batteries.

[0084] Example 3:

[0085] Based on the SPC quality modeling method provided in Embodiment 1, this embodiment of the invention provides an electronic device, including a processor and a storage medium;

[0086] Storage media are used to store instructions;

[0087] The processor is used to perform operations according to instructions to execute the steps according to the method described above.

[0088] Example 4:

[0089] Based on the SPC quality modeling method provided in Embodiment 1, this embodiment of the invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above method.

[0090] Example 5:

[0091] Based on the SPC quality modeling method provided in Embodiment 1, this embodiment of the invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above method.

[0092] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of SPC quality modeling, characterized by, The method comprises: acquiring sampling values of a target parameter in a target process within a specified time range and upper and lower limits of the target parameter; calculating a capability index of the target parameter within the specified time range according to the sampling values and the upper and lower limits of the target parameter; displaying a table header using Element UI and drawing a control chart reflecting production process stability and capability according to the capability index.

2. The SPC quality modeling method of claim 1, wherein, The acquisition of the sampling values of the target parameter comprises sampling inspection and full inspection.

3. The SPC quality modeling method of claim 1, wherein, The capability index comprises mean, range, standard deviation, UCL, LCL, CP, CPL, CPU, and CK.

4. The SPC quality modeling method of claim 3, wherein, The UCL and LCL are dynamically adjusted based on the control chart coefficients when calculated based on the mean-range chart. The UCL and LCL are calculated based on the range chart, and the control chart coefficient is dynamically adjusted; wherein is the control chart coefficient after dynamic adjustment, is the sensitivity coefficient, , is the coefficient of variation of the range , , is the standard deviation and mean of the range .

5. The SPC quality modeling method of claim 1, wherein, In the table header and the control chart, data points with abnormal capability indexes are marked in red.

6. The SPC quality modeling method of claim 1, wherein, If the number of target parameters with abnormal capability indexes is greater than a threshold, a quality warning is given.

7. An SPC quality modeling apparatus characterized by comprising: The method comprises: a data acquisition module configured to acquire sampling values of a target parameter in a target process within a specified time range and upper and lower limits of the target parameter; an index calculation module configured to calculate a capability index of the target parameter within the specified time range according to the sampling values and the upper and lower limits of the target parameter; a chart drawing module configured to display a table header using Element UI and draw a control chart reflecting production process stability and capability according to the capability index.

8. An electronic device, comprising: The method comprises a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the method according to any one of claims 1-6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method according to any one of claims 1-6.

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