Integrated control scheme for optimal single-level continuous sampling inspection and process capability index

Abstract

The invention discloses an integrated control scheme for an optimal single-level continuous sampling inspection and process capability index, and the scheme comprises the steps: firstly setting AOQL, AFIL, alpha and beta, and guaranteeing the stability of production data through normality inspection; and then, calculating a process qualified rate and a lower confidence limit thereof, and judging whether production halt adjustment is needed or further inspection is executed according to the qualified rate. Then, related quality indicators are calculated, and sampling inspection parameters are determined. The integrated control scheme is executed according to a preset inspection rule. And finally, through continuous monitoring and adjustment, it is ensured that the production quality meets the requirement. The method is suitable for process quality control with limited inspection capability. The integrated control scheme aims at reducing two risks while meeting the quality and cost requirements, is suitable for the process with the tolerance requirement on the production line, and realizes the comprehensive control of the quality, the cost and the risk.

Description

Technical Field

[0001] The present invention belongs to the field of product manufacturing process control, and is specifically an optimal single level continuous sampling test (OCSP-1) and process capability index (C pm ) integrated control scheme. The present invention aims at the situation that both the first and second risks of the Dodge type CSP scheme are high, and uses the process capability index C pm Estimator Distribution function, establish an inspection strategy that controls both types of risks at a given level. The integrated control scheme separates risk control from the CSP-1 scheme, and uses the metrological characteristics of inspection data to control both the first and second types of risks at a given level through the risk control scheme while meeting quality requirements. It can be used for process quality control of production processes with limited inspection capabilities on production lines with upper and lower tolerance limits. Background Art

[0002] As an indispensable quality control method in modern industrial production, continuous sampling inspection is mainly to identify and remove unqualified products on the production line in real time and efficiently to ensure the overall quality level of subsequent products. This method is particularly suitable for high-speed and continuous production environments, such as automated assembly line operations, which can significantly reduce batch waiting time, reduce storage costs, and improve production efficiency.

[0003] However, the traditional continuous sampling plan (CSP) often faces many challenges in practical application. On the one hand, the fixed sampling plan is difficult to flexibly respond to the fluctuations that may occur in the production process, resulting in increased lag and uncertainty in quality control; on the other hand, when setting up the CSP plan, it is often difficult to take into account both the first and second types of risks at the same time, making it difficult to achieve the optimal quality control effect.

[0004] In addition, with the advent of the era of intelligent manufacturing, the production process has higher and higher requirements for the accuracy and real-time performance of quality control. Traditional CSP solutions have been unable to meet these new requirements in terms of process capability assessment, risk control and economic optimization. Therefore, it is particularly important to develop an integrated control solution that can dynamically adapt to changes in the production process, accurately control risks and optimize resource allocation.

[0005] In view of the above background, the present invention proposes a method combining OCSP-1 and C pm The integrated control scheme is introduced by introducing the process capability index C pmAs the core indicator of quality control, combined with the optimal single-level continuous sampling inspection strategy, real-time monitoring and precise control of the production process quality are achieved. At the same time, by building a risk control model, the first and second risks are controlled within an acceptable range, ensuring the stability of the production process and the consistency of product quality. In addition, the plan also takes into account economic optimization factors, striving to meet the limitations of inspection capabilities while ensuring quality and improve overall economic benefits. Summary of the invention

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] 1. An optimal single-level continuous sampling test OCSP-1 and process capability index C pm The integrated control scheme includes the following steps:

[0008] (1) Given the current average outgoing quality limit AOQL and the long-term average inspection number limit AFI L , the first type of risk value α and the second type of risk value β;

[0009] (2) Use the latest n (initial sample size n = 100) production data to perform a normality test on the production process (calculate the p-value). If the p-value ≥ 0.05, proceed to step (3); otherwise, stop production and make adjustments;

[0010] (3) Calculate the process yield estimate using 100 test data and its lower confidence limit Calculate using maximum likelihood estimation

[0011]

[0012] in, USL is the upper tolerance limit, LSL is the lower tolerance limit, is the maximum likelihood estimate of the process pass rate, and Ф(·) is the cumulative distribution function of the normal distribution. Estimate the process standard deviation σ and process mean μ respectively, n is the number of samples (n=100 in this case), x i (i=1,...,n) is the sample observation value.

[0013] Lower confidence limit for the estimate of the process yield The calculation method is:

[0014]

[0015] in: is the value of the alpha quantile of the chi-square distribution with n-1 degrees of freedom.

[0016] (4) Compare with the effective working range (1-10AOQL, 1-AOQL] and make a judgment. Exempt from inspection; if Then stop production and make adjustments; if Execute OCSP-1 and go to step (5);

[0017] (5) Calculate p IQL , C pm-AOQL , C pm-IQL :

[0018] When p L =p IQL When AOQ = AOQL and AFI = AFI L ,according to Then p IQL for:

[0019]

[0020] According to p IQL The value of C when the process capability is equal to the quality requirement can be obtained pm-AOQL Process capability index C for value and limit process pm-IQL :

[0021]

[0022] Where p is the process defective rate, p = 1-p c , 0<p<1;

[0023] (6) Calculate the number of consecutive qualified products i of the optimal single-level continuous sampling inspection plan OCSP-1 o And the test score f in the score test phase o , determine the sample size n of the integrated control solution and the critical value C of the process capability index pm0 ;

[0024] Solve for the quality control parameter i of the integrated control scheme o and f o The formula is:

[0025]

[0026] Among them, 0<f<1, i>0, and i can only be a positive integer.

[0027] Risk control parameters n and C of the integrated control scheme pm0 is the solution of the following system of equations:

[0028]

[0029] Among them, ξ=(μ-T) / σ, ξ=0, b1=3C pm-AOQL , b2=3C pm-IQL , Ф(·) is the probability density function of the standard normal distribution N(0,1);

[0030] (7) Implementation of integrated control scheme (i o ,f o ,n,C pm0 );

[0031] (8) Keep record of n latest data and calculate and like and Continue to step (7); if and Return to step (2); if the former conditions are not met, stop production and make adjustments.

[0032] The calculation formula is:

[0033]

[0034] Where T is the process target value.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] 1. This type of process capability index estimator The risk control scheme can control both types of risks at a given level at the same time, thereby meeting the risk control needs of process status judgment under quality and cost constraints, and making up for the defect of the CSP-1 quality control scheme that both types of risks are very high and cannot be controlled at the same time.

[0037] 2. This method separates the process status judgment risk from the CSP-1 scheme, which is an innovation in risk control methods. The risk control scheme based on process capability index realizes the simultaneous control and quantitative control of two types of risks, which improves the effect of process risk control.

[0038] 3. By dynamically adjusting the sampling inspection strategy and risk control parameters, the present invention can flexibly respond to various uncertainties and fluctuations in the production process, ensuring that the production process is always under control. This high degree of flexibility and stability is of great significance in improving product quality, reducing defective product rates, and reducing downtime.

[0039] 4. While reducing production costs and improving product quality, the present invention also brings significant economic benefits. By optimizing the sampling inspection strategy, reducing unnecessary inspection times and storage costs, the present invention saves a lot of human, material and financial resources for the enterprise. The saving of these resources directly translates into an increase in corporate profits and an improvement in market competitiveness, laying a solid foundation for the sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the appended Figure 1 , combined with examples, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of invention protection.

[0042] See also Figure 1 As shown, a technical solution provided by the present invention is:

[0043] An optimal single-level continuous sampling test (OCSP-1) and process capability index (C pm ) integrated control solution

[0044] When using, follow these steps:

[0045] (1) Given the current average inspection quality limit AOQL = 0.00143 and the long-term average inspection number limit AFI L =0.7, the first risk value α = 0.05 and the second risk value β = 0.05;

[0046] (2) 100 cylinder head inner diameter fine grinding data were collected and used to perform a normality test. The p-value was calculated to be 0.527 ≥ 0.05, indicating that the fine grinding process had a high normal fit and the process approximately obeyed the normal distribution.

[0047] The inner diameter of the lower cylinder head is finely ground 100 Data unit: mm

[0048]

[0049]

[0050] (3) Calculate the process yield estimate using 100 test data and its lower confidence limit

[0051]

[0052] (4) Execute OCSP-1 and go to step (5);

[0053] (5) Calculate p IQL , C pm-AOQL , C pm-IQL :

[0054] When p L =p IQL When AOQ = AOQL and AFI = AFI L ,according to Then p IQL for:

[0055] p IQL =0.004767

[0056] According to p IQL The value of C can be obtained to obtain the process capability equal to the quality requirement pm-AOQL Process capability index C for value and limit process pm-IQL :

[0057] C pm-AOQL =1.062842

[0058] C pm-IQL =0.940798

[0059] (6) Calculate the number of consecutive qualified products i of the optimal single-level continuous sampling inspection plan OCSP-1 o And the test score f in the score test phase o , determine the sample size n and the critical value C of the process capability index of the integrated control solution pm0 ;

[0060] i o =1030

[0061] f o =0.054067

[0062] n = 365

[0063] C pm0 =1.0021

[0064] (7) Implementation of integrated control scheme (i o ,f o ,n,C pm0 )=(1030,0.054067,365,1.0021);

[0065] (8) Keep records of the latest 365 data and calculate and like and Continue to step (7); if and Return to step (2); if the former conditions are not met, stop production and make adjustments.

[0066] The above shows and describes 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 by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An optimal single-level continuous sampling test (OCSP-1) and process capability index (C pm ) includes the following steps: (1) Given the current average outgoing quality limit AOQL and the long-term average inspection number limit AFI L , the first type of risk value α and the second type of risk value β; (2) Use the latest n (initial sample size n = 100) production data to perform a normality test on the production process (calculate the p-value). If the p-value ≥ 0.05, proceed to step (3); otherwise, stop production and make adjustments; (3) Calculate the process yield estimate using 100 test data and its lower confidence limit Process qualified product rate estimate p^ c The calculation formula is: in, USL is the upper tolerance limit, LSL is the lower tolerance limit, is the maximum likelihood estimate of the process qualification rate, Ф(·) is the cumulative distribution function of the normal distribution; the sample standard deviation S and sample mean Estimate the process standard deviation σ and process mean μ respectively, n is the number of samples (n=100 in this case), x i (i=1,...,n) is the sample observation value; Lower confidence limit for process yield estimate The calculation formula is: in: is the value of the alpha quantile of the chi-square distribution with n-1 degrees of freedom; (4) Compare with the effective working range (1-10AOQL, 1-AOQL] and make a judgment. Exempt from inspection; if Then stop production and make adjustments; if Execute OCSP-1 and go to step (5); (5) Calculate p IQL , C pm-AOQL , C pm-IQL : When p L =p IQL When AOQ = AOQL and AFI = AFI L ,according to Then p IQL for: According to p IQL The value of C can be obtained to obtain the process capability equal to the quality requirement pm-AOQL Process capability index C for value and limit process pm-IQL : Where p is the process defective rate, p = 1-p c , 0<p<1; (6) Calculate the number of consecutive qualified products i of the optimal single-level continuous sampling inspection plan OCSP-1 o And the test score f in the score test phase o , determine the sample size n of the integrated control solution and the critical value C of the process capability index pm0 ; Solve for the quality control parameter i of the integrated control scheme o and f o The formula is: Among them, 0<f<1, i>0, i can only be a positive integer; Risk control parameters n and C of the integrated control scheme pm0 is the solution of the following system of equations: Among them, ξ=(μ-T) / σ, ξ=0, b1=3C pm-AOQL , b2=3C pm-IQL , Ф(·) is the probability density function of the standard normal distribution N(0,1); (7) Implementation of integrated control scheme (i o ,f o ,n,C pm0 ); (8) Keep record of n latest data and calculate and like and Continue to step (7); if and Return to step (2); if the former conditions are not met, stop production and make adjustments; The calculation formula is: Where T is the process target value.

2. The optimal single-level continuous sampling test OCSP-1 and process capability index C according to claim 1 pm Integrated control solutions, It can be used for process quality control of production processes with upper and lower tolerance requirements and limited inspection capabilities on the production line.

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