An integrated control scheme of optimal four-parameter continuous sampling inspection and first generation process capability index

Abstract

This invention discloses an integrated control scheme for optimal four-parameter continuous sampling inspection and first-generation process capability index. The specific steps are: (1) Given; (2) Determine the limit process and calculate; (3) Determine the process capability of the boundary process; (4) Calculate the integrated control scheme parameters (); (5) Execute the integrated scheme; (6) Collect data for normality test. If the normality is ≥ 0, proceed to (7); otherwise, stop production for maintenance; (7) Record data and calculate; (8) Compare with : if ≥ 0, continue to step (9); otherwise, stop production for maintenance; (9) Compare with : if ≥ 0, proceed to (10) without inspection; otherwise, return to (5); (10) Observe whether the system variables change. If they change, return to (5); otherwise, do not inspect. This invention is used for production lines with limited inspection capabilities. While meeting quality and cost requirements, it reduces the first and second types of risks. It can be used for quality control of production processes with tolerance upper and lower limits and limited inspection capabilities.

Description

Technical Field

[0001] This invention relates to the field of product manufacturing process control, and in particular to an optimal four-parameter continuous sampling inspection (Optimal Continuous Sampling Plan-3, OCSP-3) and a first-generation process capability index (…). This integrated quality control solution deeply integrates dynamic sampling inspection mechanisms with process capability quantitative assessment to construct a real-time monitoring and closed-loop control system for the production process, aiming to achieve continuous and stable control of product quality and lean optimization of the production process.

[0002] The core of this solution lies in integrating the dynamic testing characteristics of OCSP-3 with... The process capability index provides quantitative control functionality. As a continuous sampling inspection method, OCSP-3 has the ability to adaptively adjust control parameters based on real-time production process data. It can dynamically adjust sampling frequency and inspection locations, thereby enabling more targeted inspections in critical processes, fluctuating stages, or areas prone to anomalies. This dynamic mechanism not only strengthens the early identification and rapid response capabilities to quality problems but also makes the allocation of inspection resources more scientific and rational. Meanwhile, the process capability index... As a key quantitative indicator for measuring process stability and consistency, it has been introduced as an important basis for evaluating production status and adjusting inspection strategies. This is achieved through real-time calculation... By incorporating these values ​​into the decision-making closed loop, the solution achieves precise matching and synergistic optimization between "process accuracy" and "inspection intensity".

[0003] This integrated control scheme combines the dynamic verification logic of OCSP-3 with... Risk quantification capability, while ensuring the average detection quality limit ( ) and long-term average test number ( Under the premise of meeting preset requirements, the system controls two types of risks in the production process (producer risk) Consumer risk This leads to the establishment of a closed-loop management and control system. This system not only focuses on the compliance of the final product, but also delves into the process itself. Through continuous assessment of process capabilities and corresponding adjustments to inspection strategies, it promotes continuous improvement and preventative control in the production process.

[0004] This invention provides a quality control method that achieves collaborative optimization under multiple constraints, making it particularly suitable for high-precision manufacturing scenarios with extremely high requirements for quality consistency and process stability, such as semiconductor packaging, medical device processing, and aerospace. This solution can effectively ensure product quality, reduce the risk of missed inspections and misjudgments, while rationally allocating inspection resources, reducing quality control costs, and helping enterprises achieve visualization, controllability, and sustainable improvement of the production process, thereby enhancing overall operational efficiency and market competitiveness. Background Technology

[0005] In high-speed, continuous modern industrial production systems, continuous sampling inspection is one of the core technical means to ensure online quality. Its characteristic lies in its ability to identify and intercept non-conforming products in real time without interrupting production, thus maintaining product quality stability. Traditional sampling schemes, represented by Continuous Sampling Plans (CSP), generally employ preset static parameters (such as sampling frequency and transformation rules) to achieve basic inspection functions. However, their fixed structure makes it difficult to dynamically adapt to fluctuations in the production process. This static execution method has the following two shortcomings in practical applications: First, when process capability fluctuates or declines, traditional schemes struggle to detect risks and adjust inspection strategies in a timely manner, potentially leading to the outflow of non-conforming products. Second, when process capability is stable or at a relatively optimal level, the scheme maintains a fixed inspection intensity, making it difficult to reduce the resource consumption caused by redundant inspections and affecting inspection economics.

[0006] With the deepening development of the intelligent manufacturing paradigm, the manufacturing industry has placed higher demands on quality control, requiring not only real-time performance and high throughput, but also emphasizing the precision, adaptability, and systematic nature of control. The quality competitiveness of modern precision manufacturing (such as semiconductors, high-end medical devices, and aerospace) increasingly depends on the stability and controllability of the processes themselves. Therefore, the goal of quality control has evolved from simply determining product compliance to achieving dynamic and holistic synergistic optimization among quality risks, inspection costs, and process efficiency.

[0007] This invention proposes an innovative integrated quality control scheme that combines optimal four-parameter continuous sampling inspection (OCSP-3) with the first-generation process capability index (…). This solution achieves deep coupling with OCSP-3 to enable adaptive inspection of the production process. Using OCSP-3 as the dynamic inspection framework, it leverages its intelligent switching mechanism between "full inspection" and "fractional sampling" to ensure the average outgoing quality limit. While meeting the requirements, a balance must be struck between quality assurance and inspection costs. This is achieved by introducing a process capability index. As a quantitative evaluation indicator of process status, OCSP-3's key parameters (such as sampling rate and conversion threshold) are monitored in real time and dynamically adjusted to achieve precise matching and coordinated response.

[0008] Furthermore, this solution is based on OCSP-3 and the process capability index. Based on the statistical characteristics, an integrated control mechanism encompassing quality, cost, and risk was constructed. In this mechanism, the first type of risk ( Risk, namely the producer risk of mistakenly rejecting defective products) and the second type of risk ( The risk (i.e., the risk to consumers who mistakenly receive substandard products) is also included in the scope of systematic management. This ensures that excessive inspection is avoided when process quality is good, and that inspection is strengthened when process quality is poor, in order to prevent misjudgment.

[0009] In summary, this invention overcomes the problem of insufficient response to process fluctuations in traditional continuous sampling inspection, and makes up for the deficiency that relying solely on process capability index for monitoring cannot directly drive inspection decisions. It forms a quality control scheme suitable for precision manufacturing industry with multi-objective collaborative optimization and adaptive and closed-loop feedback capabilities. Summary of the Invention

[0010] This invention aims to overcome the shortcomings of existing technologies and solve two major technical problems of traditional continuous sampling inspection schemes in high-speed, continuous production processes: First, traditional schemes, represented by the CSP series, rely on static parameters and are difficult to dynamically respond to fluctuations in the production process. When process capability drifts, they cannot adjust the inspection intensity in a timely manner, easily leading to the outflow of non-conforming products. Second, when process capability is stable, traditional schemes still maintain a fixed inspection intensity, resulting in a waste of inspection resources and poor overall inspection economy. In addition, existing technologies have also failed to effectively combine the process capability index, a quantitative assessment method, with the dynamic sampling inspection mechanism to achieve a systematic closed-loop control of producer risk and consumer risk.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] An optimal four-parameter continuous sampling inspection (OCSP-3) and process capability index ( The integrated control scheme includes the following steps:

[0013] (1) Determine process constraint variables

[0014] Given the average outgoing quality limit, ), the long-term average inspection limit (Average Fraction Inspection Limit) Category 1 risk (producer risk) ) and second-class risks (consumer risks, ).

[0015] (2) Determine the limit process

[0016] A process in which only by using maximum inspection capacity can acceptable quality be obtained is called a limiting process, and its nonconforming rate is denoted as . .

[0017] Under the limit process and Established at the same time, among which... It is the average outgoing quality. It is the long-term average number of inspections. , and The relationship is , This represents the process defect rate. Therefore, the following relationship holds true under the limit process:

[0018]

[0019] (3) Determine the process capability of the boundary process

[0020] For an unbiased process, the process capability index and The relationship between them is , It is the inverse function of the cumulative distribution function of the standard normal distribution. When hour,

[0021]

[0022] Recorded as .

[0023] when hour,

[0024]

[0025] Recorded as .

[0026] (4) Calculation Parameters of the integrated control scheme with OCSP-3

[0027] The OCSP-3 scheme has four parameters ( ), It is the number of consecutive qualified products in the continuous inspection phase. It is the test score in the fractional test phase, the intermediate sample test number. and intermediate continuous test number Note: In the optimal OCSP-3 scheme, and The scheme achieves optimal control.

[0028] The parameters of the integrated control scheme are ( ),in, It's the sample size. Key values ​​for process capability index. Quality control parameters. and Solve the following system of equations:

[0029]

[0030]

[0031] Among them, in formulas (4) and (5) .

[0032] Risk control parameters of integrated control scheme and Solve the following system of equations:

[0033]

[0034]

[0035] (5) Implement the integrated control scheme ( )

[0036] (6) Process capability assessment and data normality test

[0037] Collect the latest Perform a normality test on the production data. It is a normality fit index. If If the result is positive, proceed to step (7) after passing the normality test; otherwise, suspend production and perform process adjustments.

[0038] (7) Keep records The latest data, used for calculation

[0039] The natural estimate is:

[0040]

[0041] In the formula: For sample variance, , For the sample size, For sample observations, It is the sample mean. .

[0042] (8) and Comparison: If ≥ Continue with step (9); otherwise, stop production and perform equipment maintenance.

[0043] (9) and Comparison: If ≥ If the result is positive, the inspection is waived and the process proceeds to step (10); otherwise, the process returns to step (5).

[0044] (10) Observe whether there are any changes in system variables. If there are changes, return to step (5); if there are no changes, continue to skip the inspection.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. It achieves the organic integration of dynamic inspection and process capability assessment.

[0047] This invention combines the dynamic sampling mechanism of OCSP-3 with the process capability index. By combining quantitative functions, it overcomes the shortcomings of traditional testing methods, such as insensitivity to process fluctuations and the inability of a single capability index to directly guide testing decisions, and achieves a precise match between "process accuracy and testing intensity".

[0048] 2. A dual-risk closed-loop control scheme was constructed.

[0049] By establishing based The present invention employs a statistically distributed risk scheme that can simultaneously control both Type I and Type II risks, avoiding over-testing in high-quality processes and preventing false acceptance in low-quality processes, thereby improving the scientific rigor and reliability of quality decisions.

[0050] 3. Possesses good adaptability and real-time performance.

[0051] The solution can dynamically adjust the sampling frequency and inspection location based on real-time process data, enabling more precise inspections during critical processes and fluctuating stages. This significantly improves the early detection and response capabilities for quality issues and is suitable for high-precision, fast-paced modern manufacturing environments.

[0052] 4. Optimize inspection economy while ensuring quality.

[0053] Through integrated and optimized design, this solution meets the requirements. and While imposing constraints, it can effectively reduce the average number of inspections and unnecessary inspection costs, providing enterprises with a multi-constraint collaborative and closed-loop controllable quality management method, which helps to improve production efficiency and market competitiveness. Attached Figure Description

[0054] Figure 1 The flowchart of this invention Detailed Implementation

[0055] The following will be combined with the appendix of the present invention. Figure 1 The technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with examples. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the invention.

[0056] Please see Figure 1 As shown, the present invention provides a technical solution:

[0057] An optimal four-parameter continuous sampling test (OCSP-3) and a first-generation process capability index ( This is an integrated control scheme. When using it, follow these steps.

[0058] (1) Given the current average detection quality limit Long-term average test number limit Category 1 risk value and Category II risk values Intermediate sampling inspection number and intermediate continuous test number ;

[0059] (2) Under the limiting process, calculate the nonconforming rate of the limiting process. :

[0060]

[0061] (3) Determine the process capability of the boundary process, which is the process capability index when the process nonconforming rate equals the average detected quality limit. Process capability index of limiting process :

[0062] when hour,

[0063]

[0064] when hour,

[0065]

[0066] (4) Calculate the number of consecutive qualified products for the optimal four-parameter continuous sampling inspection plan OCSP-3. And the test score in the fractional test stage Determine the sample size of the integrated control scheme. Key values ​​of process capability index ;

[0067]

[0068]

[0069]

[0070]

[0071] (5) Implement the integrated control scheme ( ) = ( );

[0072] (6) Collect diameter data of 104 crankshaft eccentric parts of air conditioner compressors (Table 1) and calculate the normal fit value. Use the data to perform a normality test;

[0073] Calculated This indicates that the process has a good normality fit, and it can be considered that the process approximately follows a normal distribution.

[0074] Table 1. 104 diameter data for the eccentric part of the air conditioner compressor crankshaft (unit: mm)

[0075]

[0076] (7) Keep the latest 104 data points in the record and use them to calculate the estimate. :

[0077]

[0078] (8) and Comparison, based on calculations Then continue to step (9);

[0079] (9) According to the calculation We need to return to step (5);

[0080] (10) Observe whether the system variables have changed. If they have changed, return to step (5); if they have not changed, then skip the inspection.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An optimal four-parameter continuous sampling inspection method for OCSP-3 and the first-generation process capability index The integrated control scheme is characterized by, Includes the following steps: (1) Determine process constraint variables: Given the average detection quality limit Long-term average test number limit Category 1 risk Category II risk . (2) Determine the limiting process and solve for the nonconforming rate of the limiting process: A process in which only by using maximum inspection capacity can acceptable quality be obtained is called a limiting process, and its nonconforming rate is denoted as . . The following conditions must be met simultaneously during the limiting process: average detection quality equal Long-term average test number equal , and The relationship is , This represents the process defect rate. Therefore, the following relationship holds true under the limit process: (3) Determine the key values ​​of the process capability index for the boundary process: For an unbiased process, the process capability index and The relationship between them is , It is the inverse function of the cumulative distribution function of the standard normal distribution. When hour, Recorded as . when hour, Recorded as . (4) Calculation Parameters of the integrated control scheme with OCSP-3: The OCSP-3 scheme parameters include ( ),in It is the number of consecutive qualified products in the continuous inspection phase. This refers to the test score in the fractional testing phase, and the intermediate sample size is set. and intermediate continuous test number Note: In the optimal OCSP-3 scheme, and The scheme achieves optimal control. The parameters of the integrated control scheme are ( ),in, It's the sample size. Key values ​​for process capability index. Quality control parameters. and Solve the following system of equations: Among them, in formulas (4) and (5) . Risk control parameters of integrated control scheme and Solve the following system of equations: (5) Implement the integrated control scheme: The parameters obtained according to step (4) Implement OCSP-3 sampling inspection and release / stricter / exemption control. (6) Process capability assessment and data normality test: Collect the latest Perform a normality test on the production data. It is a normality fit index. If If the result is positive, the normality test is passed and the process proceeds to step (7); otherwise, production is suspended and process adjustments are made. (7) Calculate the process capability index estimate: Keep a record The latest data, used for calculation , The natural estimate is: In the formula: For sample variance, , It is the sample mean. , For the sample size, These are sample observations. (8) Compare with the key values ​​of the process capability index and make maintenance decisions: Will and Comparison: If Continue with step (9); otherwise, stop production and perform equipment maintenance. (9) Exemption from inspection and rollback update: and Comparison: If If the result is positive, the process is exempt from inspection and proceeds to step (10); otherwise, return to step (5) to redetermine the critical value of the process capability index and continuously monitor the process status. (10) Monitoring changes in system variables: Observe whether any system variables have changed. If they have changed, return to step (5); if they have not changed, continue with the exemption from inspection.

2. The integrated control scheme according to claim 1, characterized in that: The method can be used for quality control of production processes with upper and lower tolerance requirements and limited inspection capabilities on the production line, so as to achieve simultaneous control of quality, cost and two types of risks.

3. The integrated control scheme according to claim 1, characterized in that: The discrimination point The discrimination point is the nonconforming rate corresponding to an acceptable quality level. The rate of nonconforming products corresponding to the rejection quality level.

4. The integrated control method according to claim 1, characterized in that: The long-term average test result function is the long-term average test result or long-term average test proportion function obtained by the OCSP-3 test procedure under steady-state conditions, and is related to... Related.

5. The integrated control method according to claim 1, characterized in that: The release probability function OC function is obtained by establishing a state transition model for the OCSP-3 inspection process and performing steady-state analysis or recursive calculation.

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