A Process Quality Control Method Combining FMEA Analysis and Multivariate Control Charts

Abstract

The present invention discloses a process quality control method combining FMEA analysis and multivariate control charts, including: planning and preparing for PFMEA; identifying special quality indicators of process parameter diagrams; determining control limits and abnormal patterns through failure analysis and risk analysis in PFMEA, so as to accurately identify abnormal fluctuations in the product processing process; decoupling and correlation analysis of multivariate quality indicators; constructing multivariate SPC control charts; identifying and tracing the abnormal patterns of multivariate SPC control charts; optimizing PFMEA and adjusting the sampling frequency and control limits in combination with SPC control charts. The present invention solves technical problems such as it is difficult to control the product quality of large parts through frequent inspections, the abnormal patterns identified by univariate SPC control charts are inaccurate, the SPC control charts cannot be formulated according to the actual production process, and the SPC technology cannot effectively improve the production process.

Description

Technical Field

[0001] The present invention relates to the technical field of product processing quality control, and particularly relates to a process quality control method combining FMEA analysis and multivariate control charts. Background Art

[0002] With the rapid development of the world economy, quality management has played an increasingly important role in the manufacturing industry. At present, for small-batch and multi-variety parts, the processing quality of a single process or a single piece of equipment cannot be effectively controlled in a timely manner. Therefore, its quality management is different from the quality management strategies implemented in general manufacturing. It is inconvenient to monitor and control the accuracy of large processing machine tools, high-frequency sampling inspection cannot be carried out for the precision quality inspection of large parts, and trial processing with large castings is likely to increase the manufacturing cost. Therefore, the process capability assessment and real-time processing status monitoring for such manufacturing processes are of great significance for controlling the quality cost of enterprises.

[0003] Failure Mode and Effect Analysis (FMEA) and Statistical Process Control (SPC) technologies are standardized process quality analysis tools in the IATF 16949 quality management system. In the implementation and verification of many foreign enterprises, SPC has been regarded as an important and effective monitoring means. By using SPC technology, key process parameters, daily equipment status, and product production quality during the production process can be monitored. However, before using SPC technology for analysis, subjective judgment methods are often used for its monitoring objects, control levels, and sample spaces. In addition, the single-variable SPC control chart also has problems such as inaccurate identification of abnormal patterns, inability to formulate SPC control charts according to the actual production process, and inability of SPC technology to effectively improve the production process. The corresponding analysis stage in Process Failure Mode and Effect Analysis (PFMEA) can provide a reference more in line with actual production for the formulation of SPC. Regularly evaluate and review FMEA and SPC simultaneously to ensure that the two coordinate with each other and work together to improve process quality and reliability. Summary of the Invention

[0004] In order to overcome the above deficiencies in technology, the present invention provides a process quality control method combining FMEA analysis and multivariate control charts. This method solves technical problems such as the difficulty of controlling the product quality of large parts through frequent inspections, inaccurate identification of abnormal patterns by the single-variable SPC control chart, inability to formulate SPC control charts according to the actual production process, and inability of SPC technology to effectively improve the production process.

[0005] Term Explanation:

[0006] 1. FMEA: Failure Mode and Effect Analysis, failure mode and effect analysis.

[0007] 2. DFMEA: Design Failure Mode and Effect Analysis, which is Design Failure Mode and Effects Analysis.

[0008] 3. PFMEA: Process Failure Mode and Effect Analysis, which is Process Failure Mode and Effects Analysis.

[0009] 4. SPC: Statistical Process Control, which is Statistical Process Control.

[0010] 5. BOM: Bill of Material, which is Bill of Materials.

[0011] The technical solution adopted by the present invention to overcome its technical problems is as follows:

[0012] A process quality control method combining FMEA analysis and multivariate control charts, comprising the following steps:

[0013] S1. The manufacturing enterprise plans and prepares the PFMEA based on internal and external requirements and technical standards, and at least in combination with the illustrated technical documents, DFMEA, and bill of materials;

[0014] S2. Identification of special quality indicators for process parameter diagrams: The process parameter diagram for part processing is formulated through the structural analysis and functional analysis of PFMEA, and then the special quality indicators in the process parameter diagram are identified as the key objects for SPC monitoring, and the major matters and special process characteristics to be recorded during the product processing are determined;

[0015] S3. Determine the control limits and abnormal patterns through failure analysis and risk analysis in PFMEA, so as to accurately identify the abnormal fluctuations during the product processing;

[0016] S4. Decoupling and correlation analysis of multivariate quality indicators: For the special quality indicators selected in step S2 that need to be key monitored, from the perspective of data analysis, the correlation analysis method and principal component analysis method are used to analyze the correlation of the features;

[0017] S5. Construct a multivariate SPC control chart: Use the measurement data of different special quality indicators determined in steps S2 and S4 for visual analysis, perform data transformation on the measurement data of different quality characteristics, so that they are displayed in the SPC control chart with the same center limit and control limits, and mark the abnormal fluctuations of major matters during the product processing at the corresponding time nodes;

[0018] S6. Abnormal pattern recognition and traceability of multivariate SPC control charts: For the multivariate SPC control charts obtained in step S5, the quality data is abnormally recognized through the eight typical out-of-control criteria of SPC control charts and the combined judgment among multiple special quality indicators. Combining the abnormal patterns determined in step S3 and the abnormal fluctuations of major events marked in step S5, the faults in the product processing process are judged.

[0019] S7. PFMEA optimization combined with SPC control chart to adjust sampling frequency and control limits: After evaluating the implementation effects of preventive measures and detection measures in the PFMEA optimization stage, the sampling frequency and the control limit coefficients of the SPC control chart are adjusted by combining the analysis of the SPC control chart.

[0020] Furthermore, in step S1, the internal and external requirements include external customer requirements and internal customer requirements. Among them, the external customer requirements at least include consumer requirements, and the internal customer requirements at least include downstream process requirements; the technical standards at least include regulatory requirements, technical requirements, and specification standards; the illustrated technical documents at least include drawings, charts, and design drawings.

[0021] Furthermore, step S2 specifically includes:

[0022] S21. Develop a process flow chart for part processing through the structural analysis of PFMEA, which is at least used to describe the structure of the overall process and sub-processes in the manufacturing process, and reflect the connection between each step and element in the product processing process;

[0023] S22. Develop a process parameter diagram for part processing through the functional analysis of PFMEA and using the process flow chart obtained in step S21. The process parameter diagram at least includes parameters that affect the input and output to achieve the process function, and is used to display the environment where the entire product processing process is located;

[0024] S23. Determine the special quality indicators to be monitored, the major events to be recorded, and the special process characteristics in the product processing process by using at least the important influencing factors, product requirements, and process control elements listed in the process parameter diagram of PFMEA.

[0025] Furthermore, step S3 specifically includes:

[0026] S31. Determine the potential failure modes, failure effects, and failure causes of each process function through the failure analysis in PFMEA, so as to establish a failure chain;

[0027] S32. For different failure causes, make corresponding changes to the abnormal patterns of the SPC control charts of the corresponding product quality characteristics;

[0028] S33. Based on the results of failure analysis, determine preventive measures and detection measures through risk analysis in PFMEA, and conduct severity, frequency, and detectability ratings for each failure chain respectively to determine the measure priority of the failure chain. Among them, preventive measures refer to measures in process design to eliminate the failure cause or the occurrence of the failure mode, and preventive control measures are verified during the process verification before sample parts, equipment acceptance, and formal production. Detection measures refer to the methods, whether automatic or manual, to detect the existence of failure causes and failure modes during or after the production process of the product.

[0029] S34. Select the special quality indicators that need to be monitored with key emphasis according to the measure priority, as well as the control limits and abnormal patterns corresponding to the special quality indicators, and determine the preliminary sampling frequency for product quality inspection.

[0030] Further, step S31 specifically includes:

[0031] S311. Based on the actual product processing process, combined with DFMEA and process parameter diagrams, list the potential failure modes of the specified operations during the product production process.

[0032] S312. Determine the failure effects of this failure mode at least through the processing flow chart and processing process cards.

[0033] S313. Search for the key process influencing factors in this failure mode in the process parameter diagram and determine the failure cause of this failure mode.

[0034] S314. Based on the analysis in steps S311 - S313, establish a failure chain.

[0035] Further, in step S33, the rating of measure priority uses three indicators of SOD. Among them, S represents the severity of the failure effect, O represents the occurrence frequency of the failure cause and the failure mode, and D represents the difficulty of detecting the failure cause and the failure mode.

[0036] Further, step S4 specifically includes:

[0037] After failure analysis and risk analysis, several special quality indicators that need to be monitored with key emphasis are selected. There is an overlap in the processing processes among the special quality indicators, so there is a coupling phenomenon among the measurement data of the special quality indicators in the time series. The correlation analysis method is used to measure and screen the quality characteristics higher than the preset correlation threshold. The quality characteristics higher than the preset correlation threshold are monitored by using one of the quality characteristics as a representative. The principal component analysis method is used to transform the related characteristics into uncorrelated new characteristics.

[0038] Further, step S5 specifically includes:

[0039] S51. Collect the detection data of different special quality indicators respectively, delete the outliers in the data, and fill in the missing values.

[0040] S52. Normalize the data of different special quality indicators, calculate the offset rate of the measurement error relative to the tolerance, and replace the detection data to draw an individual control chart. Calculate the moving range of the detection data and calculate the average moving range as the data of the moving range control chart.

[0041] S53. Plot the error offsets of different special quality indicators in the same SPC individual control chart and moving range control chart. The center limit of the individual control chart is taken as 0, the upper control limit is taken as +A2, and the lower control limit is taken as -A2. The center limit of the moving range control chart is taken as 1, the upper control limit is D3, and the lower control limit is 0. Here, both A2 and D3 are control limit coefficients.

[0042] Further, step S6 specifically includes:

[0043] S61. Combine the abnormal patterns determined in step S3 and the eight typical out-of-control criteria of the SPC control chart to comprehensively judge the abnormal patterns of the multivariate SPC control chart.

[0044] S62. If an abnormal pattern appears in a single special quality indicator, directly judge it with the abnormal pattern of the traditional SPC control chart. If abnormal patterns appear in multiple quality indicators simultaneously, then judge using the processing procedure relationship.

[0045] S63. After identifying the abnormal pattern, combine the major event record form determined in step S2, and then the potential failure mode or the existing failure cause and failure mode in the product processing process can be traced.

[0046] Further, step S7 specifically includes:

[0047] S71. Reduce the severity level of the failure impact and the frequency of the failure cause by changing the product design plan or the product processing process, and improve the ability to detect the failure cause and failure mode by using error-proof detection.

[0048] S72. Re-evaluate the severity, frequency, and detectability of the current failure mode, and formulate a new measure priority.

[0049] S73. When the severity and frequency of this failure mode are reduced, reduce the quality inspection sampling frequency, and at the same time reduce the upper and lower control limits of the SPC control chart.

[0050] The beneficial effects of the present invention are:

[0051] The present invention generates a multivariate SPC control chart based on PFMEA analysis, and combines PFMEA and SPC for process quality control. Through the analysis tool of PFMEA, the key characteristics with high severity and high risk priority rating are used as special quality indicators that SPC needs to focus on monitoring, and provide reasonable theoretical support for the determination of the control limits and sample space of SPC, making the implementation of SPC technology more objective and realizing the rapid and accurate identification of abnormal processing states; through the joint diagnosis of the multivariate SPC control chart, specific faults such as abnormal patterns occurring simultaneously in multiple quality indicators can be more accurately identified; by regularly reviewing the PFMEA output and SPC data, the process and control strategies are further optimized according to the actual situation to achieve continuous improvement. Description of the Drawings

[0052] Figure 1 It is a block diagram of the module relationships involved in the embodiments of the present invention.

[0053] Figure 2 It is a schematic flowchart of the process quality control method combining FMEA analysis and multivariate control charts described in the embodiments of the present invention.

[0054] Figure 3 It is a process flow chart of the machining process of a certain header board part constructed in the embodiments of the present invention.

[0055] Figure 4 It is a process parameter diagram of a certain header board part constructed in the embodiments of the present invention.

[0056] Figure 5 It is a schematic diagram of the failure chain of a partial machining process of a certain header board part described in the embodiments of the present invention.

[0057] Figure 6 It is a schematic diagram of the control chart partition described in the embodiments of the present invention.

[0058] Figure 7 It is an individual value control chart taking L2 and L4 as examples in the multivariate SPC control chart constructed in the embodiments of the present invention.

[0059] Figure 8 It is a moving range control chart taking L2 and L4 as examples in the multivariate SPC control chart constructed in the embodiments of the present invention. Detailed Embodiments

[0060] To facilitate better understanding of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0061] The present invention discloses a process quality control method combining FMEA analysis and multivariate control charts, including the following steps:

[0062] S1. The manufacturing enterprise plans and prepares the PFMEA according to internal and external requirements and technical standards, and at least in combination with the illustrated technical documents, DFMEA, and bill of materials.

[0063] S2. Identification of special quality indicators in the process parameter diagram: Develop the process parameter diagram for part processing through the structural analysis and functional analysis of the PFMEA, and then identify the special quality indicators in the process parameter diagram as the key objects for SPC monitoring, and determine the major matters and special process characteristics that need to be recorded during the product processing.

[0064] S3. Determine the control limits and abnormal patterns through the failure analysis and risk analysis in the PFMEA, so as to accurately identify the abnormal fluctuations in the product processing process.

[0065] S4. Decoupling and correlation analysis of multi - variable quality indicators: For the special quality indicators selected in step S2 that need to be key - monitored, from the perspective of data analysis, use the correlation analysis method and principal component analysis method to analyze the correlation of the features.

[0066] S5. Construct a multi - variable SPC control chart: Use the measurement data of different special quality indicators determined in step S2 and step S4 for visual analysis, perform data transformation on the measurement data of different quality characteristics, so that they are displayed in the SPC control chart with the same central limit and control limits, and mark the abnormal fluctuations of major matters in the product processing process at the corresponding time nodes.

[0067] S6. Abnormal pattern recognition and traceability of the multi - variable SPC control chart: For the multi - variable SPC control chart obtained in step S5, perform abnormal recognition on the quality data through the eight typical out - of - control criteria of the SPC control chart and the joint judgment between multiple special quality indicators, and combine the abnormal pattern determined in step S3 and the abnormal fluctuations of major matters marked in step S5 to judge the faults in the product processing process.

[0068] S7. Optimize the PFMEA and adjust the sampling frequency and control limits in combination with the SPC control chart: After evaluating the implementation effects of the preventive measures and detection measures in the PFMEA optimization stage, adjust the sampling frequency and the control limit coefficient of the SPC control chart in combination with the analysis of the SPC control chart.

[0069] To better understand the above - mentioned technical solution, the exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. These are only the exemplary embodiments of the present invention. However, it should be understood that the present invention can be implemented in various forms and is not limited to the embodiments described here. These embodiments are to enable those skilled in the art to understand the present invention more clearly and thoroughly.

[0070] A process quality control method combining FMEA analysis and multi - variable control charts described in this embodiment is asFigure 1 and Figure 2 as shown, including the following steps:

[0071] S1. The manufacturing enterprise plans and prepares the PFMEA based on internal and external requirements and technical standards, and at least in combination with the illustrated technical documents, DFMEA, and bill of materials (BOM), mainly to determine the scope to be analyzed, determine the team responsibilities, and collect relevant information, aiming to lay a foundation for subsequent failure mode analysis.

[0072] Specifically, the internal and external requirements include external customer requirements and internal customer requirements. Among them, the external customer requirements at least include consumer requirements, and the internal customer requirements at least include downstream process requirements. The technical standards at least include regulatory requirements, technical requirements, and specification standards. The illustrated technical documents at least include drawings, charts, and design drawings.

[0073] S2. Identification of special quality indicators in the process parameter diagram: The process parameter diagram for part processing is formulated through the structural analysis and functional analysis of PFMEA, and then the special quality indicators in the process parameter diagram are identified as the key objects for SPC monitoring, and the major matters and special process characteristics to be recorded during the product processing are determined.

[0074] Specifically, step S2 includes:

[0075] S21. Formulate the process flow chart for part processing through the structural analysis of PFMEA. As Figure 3 shown, it is the process flow chart for a certain header plate part, which is at least used to describe the overall process and sub-process structure during manufacturing, and reflects the connection between each step and element in the product processing process. The process flow chart, as an auxiliary tool, not only helps to define the process, but also provides a visual basis for the functional analysis of PFMEA.

[0076] S22. Formulate the process parameter diagram for part processing through the functional analysis of PFMEA and using the processing flow chart obtained in step S21. As Figure 4 shown, it is the process parameter diagram for a certain header plate part. Specifically, the functional analysis focuses on factors such as manufacturing environment, cycle, occupational health and safety, and environmental impact, aiming to identify the special quality indicators of the product and process. These special quality indicators are the key points of control to ensure meeting safety, regulatory compliance, as well as the functions, performance, and technical requirements concerned by customers, and at the same time optimize costs to improve customer satisfaction. The process parameter diagram at least includes the parameters that affect the input and output to achieve the process function, and is used to display the environment where the entire product processing process is located.

[0077] S23. Determine the special quality indicators to be monitored, the major events to be recorded, and the special process characteristics during the product processing by using at least the important influencing factors, product requirements, and process control elements listed in the process parameter diagram of PFMEA, provide data reference for the monitoring of special quality indicators, major events to be recorded, and special process characteristics by SPC, and provide a basis for subsequent failure traceability.

[0078] S3. Determine the control limits and abnormal patterns through failure analysis and risk analysis in PFMEA, so as to accurately identify the abnormal fluctuations during the product processing.

[0079] Specifically, step S3 includes:

[0080] S31. Determine the potential failure modes, failure effects, and failure causes of each process function through failure analysis in PFMEA, so as to establish a failure chain, with the aim of providing a basis for risk analysis.

[0081] In this embodiment, step S31 specifically includes:

[0082] S311. According to the actual product processing process, combined with DFMEA and the process parameter diagram, list the potential failure modes of the operations specified in the product production process.

[0083] S312. Determine the failure effects of this failure mode at least through the processing flow chart and processing process card.

[0084] S313. Search for the key process influencing factors in this failure mode in the process parameter diagram and determine the failure cause of this failure mode.

[0085] S314. Establish a failure chain based on the analysis of steps S311 - S313.

[0086] S32. Make corresponding changes to the abnormal patterns of the SPC control chart of the corresponding product quality characteristics for different failure causes.

[0087] S33. Based on the results of failure analysis, preventive measures and detection measures are determined through risk analysis in PFMEA, and severity, frequency, and detectability ratings are conducted for each failure chain respectively to determine the measure priority of the failure chain. The measure priority rating uses three indicators of SOD. Among them, S represents the severity of the failure impact, O represents the occurrence frequency of the failure cause and failure mode, and D represents the difficulty of detecting the failure cause and failure mode. Using the process failure mode and special quality indicators, the monitoring object of SPC can be determined by referring to the detectability D rating; using the failure cause and special process characteristics, the abnormal mode of SPC can be determined by referring to the frequency O rating; using the failure impact, severity S rating, and frequency O rating, the sample space of SPC, that is, the sampling frequency, can be determined. Specifically, preventive measures refer to the measures to eliminate the occurrence of failure causes or failure modes in process design, and preventive control measures are verified during the process verification before sample parts, equipment acceptance, and formal production. Detection measures refer to the methods, whether automatic or manual, to detect the existence of failure causes and failure modes during or after the production process of products.

[0088] In this embodiment, taking a certain machining process of a certain head plate part as an example for failure analysis, a failure chain of the machining process is constructed, and part of the failure chain is as Figure 5 shown; the partial failure risk ratings of a certain machining process of the head plate part are shown in Table 1.

[0089] Table 1 Partial Failure Risk Rating Table of a Certain Machining Process of a Certain Head Plate Part

[0090]

[0091] The measure priorities in Table 1 are divided into three priority levels: high, medium, and low, represented by H, M, and L respectively. The PFMEA team needs to formulate different measure requirements for different measure priorities. Select the key product characteristics corresponding to the failure modes with high failure severity rating and high frequency as the monitoring object space of multivariate SPC, and design relevant algorithms to process the SOD rating.

[0092] After selecting the corresponding failure mode, determine the failure cause, and formulate the abnormal mode type of the multivariate SPC control chart for the failure cause. The corresponding standard between the abnormal mode of the multivariate SPC control chart and the failure cause is shown in Table 2. In Table 2, represents the moving range within the group, represents the mean within the group, UCL represents the upper specification limit of the SPC control chart, LSL represents the lower specification limit of the SPC control chart, and the upper and lower control limits are located at three standard deviation distances above and below the center line respectively. The control chart is divided into six zones, as Figure 6As shown, the width of each zone is one standard deviation σ. The six zones are labeled A, B, C, C, B, A from top to bottom. Two zones with the same label are symmetric with respect to the center line. The details of the specific failure causes need to be adjusted and recorded according to the on-site production process.

[0093] Table 2 Corresponding Standards between Abnormal Patterns and Failure Causes of Multivariate SPC Control Charts

[0094]

[0095] S34. Select the special quality indicators that need to be monitored key points according to the measure priority, as well as the control limits and abnormal patterns corresponding to the special quality indicators, and determine the preliminary sampling frequency of product quality inspection.

[0096] S4. Decoupling and Correlation Analysis of Multivariate Quality Indicators: For the special quality indicators that need to be monitored key points selected in step S2, from the perspective of data analysis, use the correlation analysis method and the principal component analysis method to analyze the correlation of the characteristics.

[0097] Specifically, step S4 includes:

[0098] After failure analysis and risk analysis, several special quality indicators that need to be monitored key points are selected. There is an overlap in the processing processes between the special quality indicators, and there is a coupling phenomenon in the measurement data of the special quality indicators in the time series; use the correlation analysis method to measure and screen the quality characteristics higher than the preset correlation threshold, and the quality characteristics higher than the preset correlation threshold are monitored through one of the quality characteristics as a representative; use the principal component analysis method to convert the related characteristics into uncorrelated new characteristics.

[0099] In the case of this embodiment, after the analysis of step S3 above, a total of eight quality characteristics are selected, including five dimensional characteristics and three geometric characteristics. After the preliminary analysis of the data characteristics, the three geometric characteristics are relatively independent, so these three geometric characteristics all need to be monitored using multivariate SPC control charts; in this embodiment, it is preferably to use the Pearson coefficient and the Spearman coefficient to perform correlation analysis on the five dimensional characteristics, and select two of them for monitoring.

[0100] The calculation formula of the Pearson coefficient is as follows:

[0101] ρ P[X,Y] = cov(X,Y) σ X × σ Y = E((X - μ X )(Y- μ Y )) σ X × σ Y = E XY -E(X)E(Y) E X 2 - E 2 (X) × E Y 2 - E 2 (Y) (1)

[0102] In formula (1), 、 respectively represent two groups of variables for correlation analysis ρ P[X,Y] represents a variable and variable 's Pearson coefficient represents the expected value of a certain group of variables and respectively represent variable and variable 's standard deviation represents variable and variable 's covariance and respectively represent variable and variable 's mean

[0103] The calculation formula of the Spearman coefficient is as follows

[0104] (2)

[0105] In formula (2), represents the Spearman coefficient of two groups of variables represents the rank difference of the th data pair represents the total number of observation samples

[0106] Calculate the comprehensive correlation coefficient through the Pearson coefficient and the Spearman coefficient. The calculation formula of the comprehensive correlation coefficient is as follows

[0107] ρ = λ × ρ P[X,Y] + 1 - λ × ρ S (3)

[0108] In formula (3), represents the comprehensive correlation coefficient between variable and variable represents the weight coefficient between the Pearson coefficient and the Spearman coefficient

[0109] In the case of this embodiment, since the distribution of quality characteristics does not strictly conform to the normal distribution, the Pearson coefficient and the Spearman coefficient are used in combination to evaluate the correlation degree between quality characteristics. The correlation scores of the five quality indicators are shown in Table 3. L1~L5 are the five quality indicators of this part, representing five position precisions. Select L2 and L4 together with three form and position characteristics to form the monitoring object space of the multivariate SPC control chart

[0110] ​Table 3 Correlation Coefficient Table of Each Quality Index

[0111]

[0112] S5. Construct a multivariate SPC control chart: Use the measurement data of different special quality indexes determined in steps S2 and S4 for visual analysis, perform data transformation on the measurement data of different quality characteristics, display them in an SPC control chart with the same central limit and control limits, and mark the abnormal fluctuations of major events in the product processing process at the corresponding time nodes.

[0113] Specifically, step S5 includes:

[0114] S51. Collect the detection data of different special quality indexes respectively, delete the abnormal values in the data, and fill in the missing values.

[0115] S52. Normalize the data of different special quality indexes, calculate the offset rate of the measurement error relative to the tolerance, and replace the detection data to draw an individual control chart. Calculate the moving range of the detection data and calculate the mean of the moving ranges as the data of the moving range control chart.

[0116] S53. Plot the error offsets of different special quality indexes in the same SPC individual control chart and moving range control chart. The central limit of the individual control chart is taken as 0, the upper control limit is taken as +A2, and the lower control limit is taken as -A2. The central limit of the moving range control chart is taken as 1, the upper control limit is D3, and the lower control limit is 0, where A2 and D3 are both control limit coefficients.

[0117] Since it is inconvenient to conduct large-scale sampling inspection in this embodiment, only the first piece, the last piece, and the pieces taken in the middle of the corresponding order are inspected in actual production. Therefore, an individual - moving range control chart can be selected, that is, an individual control chart and a moving range control chart.

[0118] First, select 30 groups of recent data for process capability assessment (note that data stratification selection and grouping should be reasonable), calculate the process capability index and process parameters to calculate the control limits of the control chart. Calculate the moving range of the data and unify it. After calculating the moving ranges of various data, calculate the mean of the moving ranges, divide all the ranges by the range mean of the corresponding characteristics, scale the range means of different characteristics to 1. In this embodiment, the preferred value of the upper limit D3 is 3.267. Calculate the mean of the data and unify the control limits. Subtract the mean of the corresponding characteristics from the data of different characteristics, and the mean line is unified as the 0 line; then divide the processed data by the corresponding range mean, and the upper control limit and the lower control limit are unified as ±2.660 lines, that is, A2 = 2.660, as Figure 7 and Figure 8 shown.

[0119] The specific calculation formulas are as follows:

[0120] (4)

[0121] (5)

[0122] (6)

[0123] In formulas (4)-(6), represents the moving range of the th value and the previous value, and represent the current value and the previous value respectively, represents the average moving range within the group, represents the center line of the moving range control chart, represents the upper control limit of the moving range control chart, represents the lower control limit of the moving range control chart, represents the average within the group, represents the center line of the individual value control chart, represents the upper control limit of the individual value control chart, represents the lower control limit of the individual value control chart.

[0124] S6. Identification and traceability of abnormal patterns in multivariate SPC control charts: For the multivariate SPC control chart obtained in step S5, the quality data is abnormally identified through the combined judgment of the eight typical out-of-control criteria of the SPC control chart and multiple special quality indicators, and the faults in the product processing process are judged by combining the abnormal patterns determined in step S3 and the abnormal fluctuations of the major events marked in step S5.

[0125] Specifically, step S6 includes:

[0126] S61. Combining the abnormal patterns determined in step S3 and the eight typical out-of-control criteria of the SPC control chart, comprehensively judge the abnormal patterns of the multivariate SPC control chart. Among them, the eight typical out-of-control criteria of the SPC control chart refer to the eight typical out-of-control criteria specified in GB / T 4091-2000, namely one point outside the limit, two-thirds on the same side outside B, four-fifths on the same side outside C, six points rising continuously, six points falling continuously, eight points without C, fourteen points rising and falling, and fifteen points within C.

[0127] In the case of this embodiment, eight typical out-of-control criteria are adopted, and six of them are selected according to the actual production situation, namely one point outside the control limit, two-thirds outside B on the same side, four-fifths outside C on the same side, six points rising continuously, six points falling continuously, and eight points without C (due to the limitations of the enterprise production situation and quality control costs, the other two criteria are not applicable to this case), which are used as the out-of-control criteria in the case of this embodiment. According to the enterprise production situation and the implementation mode of the present invention, the six out-of-control criteria are refined. Specifically, according to the different forms of the typical out-of-control criteria on both sides of the center line, the abnormal patterns of the specific corresponding data distribution and the corresponding failure causes are listed respectively, as shown in Table 2.

[0128] S62. If an abnormal pattern appears in a single special quality index, the abnormal pattern of the traditional SPC control chart is directly used for judgment; if abnormal patterns appear in multiple quality indexes at the same time, the processing procedure relationship is used for judgment.

[0129] S63. After identifying the abnormal pattern, combined with the major event record form determined in step S2, the potential failure modes or the existing failure causes and failure modes in the product processing process can be traced back.

[0130] S7. Optimize PFMEA and adjust the sampling frequency and control limits in combination with the SPC control chart: After evaluating the implementation effects of the preventive measures and detection measures in the PFMEA optimization stage, adjust the sampling frequency and the control limit coefficient of the SPC control chart by combining the analysis of the SPC control chart.

[0131] Specifically, step S7 includes:

[0132] S71. Reduce the severity level of the failure impact and the frequency of the failure cause by changing the product design scheme or the product processing process, and improve the ability to detect the failure cause and failure mode by using error-proof detection.

[0133] S72. Re-evaluate the severity, frequency, and detectability of the current failure mode, and formulate a new measure priority.

[0134] S73. When the severity and frequency of the failure mode are reduced, reduce the quality inspection sampling frequency and at the same time reduce the upper and lower control limits of the SPC control chart.

[0135] In the case of this embodiment, three processing steps are selected for optimization measures in the processing process analysis, and the effects after the measure optimization are shown in Table 4.

[0136] Table 4 Effects after Measure Optimization

[0137]

[0138] In the case of this embodiment, through the joint fault tracing of multivariate SPC and PFMEA, the fault cause of the processing process was accurately located; corresponding measures were taken in a timely manner to reduce the occurrence frequency and failure impact of the processing process faults, ensuring production efficiency and improving the qualified rate of products; the sampling frequency in the quality control strategy of the processing process and the control limits of the SPC control chart were adjusted, shortening the quality control cycle and reducing the quality cost.

[0139] The above only describes the basic principles and preferred embodiments of the present invention. Those skilled in the art can make many changes and improvements based on the above description, and these changes and improvements should fall within the protection scope of the present invention.

Claims

1. A process quality control method combining FMEA analysis and multivariate control chart, characterized in that: The steps include: S1. The manufacturer shall plan and prepare PFMEA according to internal and external requirements and technical standards and at least in combination with the graphic technical documents, DFMEA and bill of materials; S2. Identification of special quality indicators in process parameter diagram: Develop process parameter diagram for parts processing through structural analysis and functional analysis of PFMEA, and then identify special quality indicators in process parameter diagram as the key objects of SPC monitoring, and determine the major issues and special process characteristics that need to be recorded in the product processing process; S3. Determine control limits and abnormal modes through failure analysis and risk analysis in PFMEA, so as to accurately identify abnormal fluctuations in product processing; S4. Decoupling and correlation analysis of multiple quality indicators: For the special quality indicators that need to be monitored in step S2, correlation analysis and principal component analysis are used to perform correlation analysis on the features from the perspective of data analysis; S5. Construct a multivariate SPC control chart: Use the different special quality indicator measurement data determined in step S2 and step S4 to perform visual analysis, perform data transformation on the different quality characteristic measurement data, so that they can be displayed in the SPC control chart with the same center limit and control limit, and mark the abnormal fluctuations of major events in the product processing process at the corresponding time nodes; S6. Identification and tracing of abnormal patterns of multivariate SPC control charts: For the multivariate SPC control chart obtained in step S5, identify abnormalities in quality data through eight typical abnormality judgment criteria of the SPC control chart and joint judgment between multiple special quality indicators, and judge the faults in the product processing process by combining the abnormal pattern determined in step S3 and the abnormal fluctuations of the major events marked in step S5; S7. PFMEA optimization combined with SPC control chart to adjust sampling frequency and control limits: After evaluating the implementation effect of preventive measures and detection measures in the PFMEA optimization stage, the sampling frequency and control limit coefficients of the SPC control chart are adjusted in combination with SPC control chart analysis.

2. The process quality control method combining FMEA analysis and multivariate control chart according to claim 1, characterized in that: In step S1, internal and external demands include external customer demands and internal customer demands, wherein external customer demands at least include consumer demands, and internal customer demands at least include downstream process demands; technical standards at least include regulatory requirements, technical requirements, and specification standards; and graphical technical documents at least include drawings, charts, and design drawings.

3. The process quality control method combining FMEA analysis and multivariate control chart according to claim 1 is characterized in that: Step S2 specifically includes: S21. Develop a parts processing flow chart through PFMEA structural analysis, which is used to describe at least the structure of the overall process and sub-processes in the manufacturing process, and reflect the connection between the various steps and elements in the product processing process; S22, formulating a process parameter diagram for part processing by using the process flow chart obtained in step S21 through functional analysis of PFMEA, wherein the process parameter diagram at least includes parameters that affect input and output process functions, and is used to display the environment in which the entire product processing process is located; S23. Use the process parameter diagram of PFMEA, which lists at least the important influencing factors, product requirements and process control elements, to determine the special quality indicators that need to be monitored during product processing, the major issues that need to be recorded and the special process characteristics.

4. The process quality control method combining FMEA analysis and multivariate control chart according to claim 1, characterized in that: Step S3 specifically includes: S31. Determine the potential failure modes, failure effects and failure causes of each process function through failure analysis in PFMEA, thereby establishing a failure chain; S32. According to different failure causes, make corresponding changes to the abnormal mode of the SPC control chart of the corresponding product quality characteristics; S33. According to the results of failure analysis, preventive measures and detection measures are determined through risk analysis in PFMEA, and severity, frequency and detection rating are performed for each failure chain to determine the priority of measures in the failure chain. Preventive measures refer to measures to eliminate the occurrence of failure causes or failure modes in process design, and preventive control measures are verified during the process verification before sample and equipment acceptance and formal production. Detection measures refer to the detection of failure causes and failure modes by automatic or manual methods during or after the product leaves the production process. S34. Select the special quality indicators that need to be monitored based on the priority of the measures, as well as the control limits and abnormal patterns of the corresponding special quality indicators, and determine the preliminary sampling frequency of product quality inspection.

5. The process quality control method combining FMEA analysis and multivariate control chart according to claim 4 is characterized in that: Step S31 specifically includes: S311. Based on the actual product processing, combined with DFMEA and process parameter diagrams, list the potential failure modes of the operations specified in the product production process; S312, determining the failure impact of the failure mode at least through a processing flow chart and a processing process card; S313, finding the key process influencing factors in the failure mode in the process parameter diagram, and determining the failure cause of the failure mode; S314. According to the analysis of step S311 to step S313, an invalidation chain is established.

6. The process quality control method combining FMEA analysis and multivariate control chart according to claim 4 is characterized in that: In step S33, the priority of measures is rated using the three indicators SOD, where S represents the severity of the failure impact, O represents the frequency of the failure cause and failure mode, and D represents the difficulty of detecting the failure cause and failure mode.

7. The process quality control method combining FMEA analysis and multivariate control chart according to claim 1, characterized in that: Step S4 specifically includes: After failure analysis and risk analysis, several special quality indicators that need to be monitored are selected. There is overlap in the processing processes between the special quality indicators, and there is coupling between the measurement data of the special quality indicators in the time series; the correlation analysis method is used to measure and screen quality features that are higher than the preset correlation threshold. The quality features that are higher than the preset correlation threshold are monitored through one of the quality features as a representative; the principal component analysis method is used to convert related characteristics into unrelated new characteristics.

8. The process quality control method combining FMEA analysis and multivariate control chart according to claim 1, characterized in that: Step S5 specifically includes: S51, respectively collecting test data of different special quality indicators, deleting abnormal values ​​in the data, and filling in missing values; S52, normalizing the data of different special quality indicators, calculating the deviation rate of the measurement error relative to the tolerance, and drawing a single value control chart instead of the test data, calculating the moving range of the test data and calculating the moving range mean as the data of the moving range control chart; S53. Plot the error offsets of different special quality indicators on the same SPC single value control chart and moving range control chart. The center limit of the single value control chart is 0, the upper control limit is +A2, and the lower control limit is -A2. The center limit of the moving range control chart is 1, the upper control limit is D3, and the lower control limit is 0. Among them, A2 and D3 are control limit coefficients.

9. The process quality control method combining FMEA analysis and multivariate control chart according to claim 1, characterized in that: Step S6 specifically includes: S61, combining the abnormal pattern determined in step S3 and eight typical abnormality judgment criteria of the SPC control chart, comprehensively judging the abnormal pattern of the multivariate SPC control chart; S62. If a single special quality indicator shows an abnormal pattern, the abnormal pattern judgment of the traditional SPC control chart is used directly; if multiple quality indicators show abnormal patterns at the same time, the relationship between the processing procedures is used for judgment; S63. After the abnormal mode is identified, combined with the major event record table determined in step S2, it is possible to trace back to the potential failure mode or the failure cause and failure mode that have already occurred in the product processing process.

10. The process quality control method combining FMEA analysis and multivariate control chart according to any one of claims 6 to 9, characterized in that: Step S7 specifically includes: S71. Reduce the severity level of failure effects and the frequency of failure causes by changing product design or product processing, and improve the ability to detect failure causes and failure modes by using error-proofing detection; S72. Re-evaluate the severity, frequency, and detectability of the current failure mode and set new action priorities; S73. When the severity and frequency of the failure mode decrease, reduce the frequency of quality inspection sampling and reduce the upper and lower control limits of the SPC control chart.

Images