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Statistical methods in monitoring process capability and performance - Part 6: Process capability statistics for characteristics following a multivariate normal distribution
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Basic data Standard ID | GB/T 40681.6-2021 (GB/T40681.6-2021) | Description (Translated English) | Statistical methods in monitoring process capability and performance - Part 6: Process capability statistics for characteristics following a multivariate normal distribution | Sector / Industry | National Standard (Recommended) | Classification of Chinese Standard | A41 | Word Count Estimation | 34,394 | Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration |
GB/T 40681.6-2021: Statistical methods in monitoring process capability and performance - Part 6: Process capability statistics for characteristics following a multivariate normal distribution
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Statistical methods in monitoring process capability and performance - Part 6.Process capability statistics for characteristics following a multivariate normal distribution
ICS 03.120.30
A 41
National Standards of People's Republic of China
Statistical methods for production process capability and performance monitoring
Part 6.Multivariate Normal Process Capability Analysis
(ISO 22514-6.2013,Statisticalmethodsinprocessmanagement-
Released on 2021-10-11 and implemented on 2022-05-01
State Administration for Market Regulation
Issued by the National Standardization Management Committee
Table of contents
Foreword Ⅲ
Introduction Ⅳ
1 Scope 1
2 Normative references 1
3 Terms and definitions 1
4 Abbreviations 3
5 Process analysis 3
6 Evaluation of Multi-Process Capability and Performance 3
7 Calculation of process capability and process performance 4
7.1 Description of Type I and Type II Indices 4
7.2 Index setting and symbolic representation 4
7.3 Type Ⅰc and Type Ⅱc process capability index 6
7.4 Type Ⅱa and Type Ⅱb Process Capability Index 8
8 Example 9
8.1 Two-dimensional position tolerance 9
8.2 Location and size of slots 13
Appendix A (informative appendix) Formula derivation 17
Appendix B (informative appendix) Examples of shaft imbalance 20
Appendix C (informative appendix) Examples of hole positions 24
Appendix D (informative appendix) Transformation function construction 28
References 29
Foreword
The plan of GB/T 40681 "Statistical Methods for Monitoring Production Process Capability and Performance" is divided into the following 8 parts.
---Part 1.General principles and concepts;
---Part 2.Process capability and performance of time-dependent process models;
---Part 3.Research on machine performance of discrete product measurement data;
---Part 4.Process capability estimation and performance measurement;
---Part 5.Process capability and performance estimation of counting characteristics;
---Part 6.Multivariate normal process capability analysis;
---Part 7.Measuring process capability;
---Part 8.Equipment performance analysis of multi-state production process.
This part is Part 6 of GB/T 40681.
This section was drafted in accordance with the rules given in GB/T 1.1-2009.
This part uses the redrafting method to modify and adopt ISO 22514-6.2013 "Statistical Methods Capability and Performance in Process Management No. 6
Part. Analysis of Multivariate Normal Process Capability.
The technical differences between this part and ISO 22514-6.2013 and the reasons are as follows.
---Modified the description of the scope of application;
--- The term 3.4 "process capability" is changed to the definition of the corresponding term in GB/T 3358.2-2009 and Note 2 is deleted to maintain the current
Consistent with national standards;
---Change the term 3.6 "process performance" to the definition of the corresponding term in GB/T 3358.2-2009 and delete the note to the original definition to ensure
Be consistent with current national standards;
--- Modify the F distribution in 7.2.2 to the chi-square distribution, which is more accurate;
--- Modify the estimated formula of D in 7.4.2 to D^= 1
n-1
(x--μ0)TS-1(x
--μ0).
This section has made the following editorial changes.
---Revised the standard name to "Statistical Methods for Production Process Capability and Performance Monitoring Part 6.Multivariate Normal Process Capability Analysis";
--- Correct the meaning of xt1, xt2,.., xtd in 7.4.2 to "the length of the semi-ellipse axis";
--- Correct {x|(x-μ)T£ -1(x-μ)=c2} in A.1 of Appendix A to {x|(x-μ)TΣ-1(x-μ)= c2};
---Add the equal sign "=" after the covariance matrix ΣA and ΣB in Appendix C;
---Modified the right picture of Figure C.1 in Appendix C;
---Revised references.
This part is proposed and managed by the National Standardization Technical Committee for the Application of Statistical Methods (SAC/TC21).
Drafting organizations of this section. Beijing University of Technology, China National Institute of Standardization, Inner Mongolia Mengniu Dairy (Group) Co., Ltd., Shanxi Hu
Chemical Group Jinxing Chemical Co., Ltd., Huzhou Rongke Building Material Technology Co., Ltd., Huzhou Mingfeng Enterprise Management Consulting Co., Ltd., China Machinery Productivity
Promotion Center, Zhongtong Bus Holding Co., Ltd., Liaocheng Zhuoqun Auto Parts Co., Ltd.
The main drafters of this section. Xie Tianfa, Liu Han, Zhao Jing, Ding Wenxing, Yang Zhigang, Tian Guiyan, Li Bingyun, Ding Lihui, Qian Xinhui, Gu Feng,
Guo Qian, Wu Guangyu, Zhang Yanyan.
Introduction
Due to the complexity of production methods and the increased quality requirements for products and production processes, in many cases, univariate processes are used.
Process analysis is not enough to meet the requirements of process management. Therefore, it is particularly necessary to carry out process analysis based on the characteristics of multiple products. For example, in production
During the process, the process capability is developed for the geometric tolerances, dynamic changes (such as imbalance), material correlation characteristics or other process characteristics that can be collected
analyze.
The purpose of this section is to give different calculation methods for process performance and process capability index in the case of multiple processes or multiple product characteristics.
Definition.
Drawing on the methods in ISO 22514-2, this section provides the calculation formulas for the multivariate process performance and process capability index as a corresponding list.
The generalization of the variable index, these formulas take the process dispersion and process position into consideration at the same time. The index in this section is actually one-dimensional
Based on the classic Cp and Cpk indices. Appendix A gives a detailed explanation of the expansion from the univariate case to the multiple case.
The possible application examples of this section are the two-dimensional or three-dimensional position of the product, the imbalance (see Appendix B) or some related quantities of the chemical product.
The dispersion of the measurement result depends on the dispersion of the product realization process and the accuracy of the measurement process. It is assumed in this section that the
The capability of the measurement system has been verified before the product realizes the process capability analysis.
The calculation methods described in this section should be used to guide clear decisions, especially in the following situations.
---The limit of the process capability index is set for the continuous multi-product characteristics, which can be used as a part of the contract between the customer and the supplier;
---Comparison of production methods or supplier process capabilities;
---Production process license;
---In the event of complaints or harmful incidents, carry out problem analysis and make decisions.
Note. The product realization process includes the manufacturing process, product assembly process and service process.
Statistical methods for production process capability and performance monitoring
Part 6.Multivariate Normal Process Capability Analysis
1 Scope
This part of GB/T 40681 provides for the calculation process or product when it is necessary to consider a family of interrelated single variables.
Statistical methods for the performance and capabilities of product characteristics.
This section is applicable to the process capability and performance estimation of the multivariate normal process in the common production process.
Note. For the sake of simplicity, the methods provided in this section are mostly described with the bivariate normal process as an example.
For the different methods provided, this section does not evaluate the possible applications of each method in different situations. The user can be based on
Choose a more appropriate method for the actual situation.
2 Normative references
The following documents are indispensable for the application of this document. For dated reference documents, only the dated version is applicable to this
document. For undated reference documents, the latest version (including all amendments) is applicable to this document.
ISO 22514-1 Statistical Methods in Process Management Capability and Performance Part 1.General Principles and Concepts (Statistical
methodsinprocessmanagement-Capabilityandperformance-Part 1.Generalprinciplesandcon-
cepts)
ISO 22514-2 Statistical Method Capability and Performance in Process Management Part 2.Process Capability and Performance of Time-Dependent Process Models
Performance (Statisticalmethodsinprocessmanagement-Capabilityandperformance-Part 2.Processcapa-
bilityandperformanceoftime-dependentprocessmodels)
3 Terms and definitions
The following terms and definitions defined by ISO 22514-1 and ISO 22514-2 apply to this document.
3.1
Quantity
The size of a phenomenon, object or substance can be represented by a number and a reference object.
[JJF1001-2018, definition 3.1]
3.2
Multivariatequantity
Multiple distinguishable feature sets.
Note 1.The set can be represented by a d-dimensional array, that is, an ordered set composed of d elements.
Note 2.If the single variable in the set is represented by xi, where i=1,2,,d, the multivariate is represented by the vector x, and x=(x1,x2,,xd)T. Therefore, more
The variable can be regarded as the feature vector of the product. The value of the multivariate is represented by a point in the d-dimensional feature space.
Note 3.The elements in the vector are selected according to specific technical reasons.
Note 4.All single variables that make up the vector need to be measurable in the same product or object.
Note 5.If you use statistical methods to describe multiple variables, then the vector is regarded as a d-ary random vector.
Example 1.Combining x1=color, x2=quality and x3=number of failures into a vector, a single statistic can be used to evaluate process capability. This vector x
The dimension of is d=3.
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