GB/T 17989.8-2022Statistical method of quality control in production process - Control charts - Part 8: Charting techniques for short runs and small mixed batches (English PDF)

foreword

This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for Standardization Work Part 1.Structure and Drafting Rules of Standardization Documents"

drafted.

This document is part 8 of GB/T 17989.GB/T 17989 has released the following parts.

--- Control Charts Part 1.General Guidelines;

--- Control Charts Part 2.General Control Charts;

--- Control Charts Part 3.Acceptance Control Charts;

--- Control Charts Part 4.Cumulative and Control Charts;

--- Statistical method control charts for quality control in production processes - Part 5.Special control charts;

--- Statistical method control chart for quality control of production process Part 6.Exponentially weighted moving average control chart;

--- Statistical method control chart for quality control in production process Part 7.Multivariate control chart;

--- Statistical method control chart for quality control in production process Part 8.Control method for short cycle and small batch;

--- Statistical methods for quality control of production process control charts Part 9.Stationary process control charts.

This document is revised and adopted ISO 7870-8.2017 "Control Charts - Part 8.Control Methods for Short Cycle Small Batches".

The technical differences between this document and ISO 7870-8.2017 and their reasons are as follows.

--- Scope paragraph 1, line 2, 4.2b), 4.2c), 6.2.1a), 6.2.2a), 6.3.1a), 6.3.2a), 6.4.1a), 6.4.2a),

"Sample size" in 6.5.1a) and 6.5.2a) is changed to "subgroup size" to keep the terminology consistent with this series of standards;

--- Add the explanation of different types of control charts in the explanation of the symbols "LCL" "UCL" in 3.2;

--- Change the "precision" in Figure 1 and Figure 2 to "process spread", which is in line with the commonly used Chinese usage of this meaning;

--- Change the "subgroup serial number" in the description of the index serial number in Figure 6 to "measured value serial number", and make it clear that the serial number refers;

--- Redraw Figure 7, and the positions of some points in the original image are not accurate.

The following editorial changes have been made to this document.

--- Change the standard name to "Production Process Quality Control Statistical Methods Control Chart Part 8.Control Methods for Short Cycle Small Batches"

Law";

--- Modify the "X" in the description of the index number in Figure 7 to "percentage";

--- Modify "variation pattern of characteristics" in 6.2.2f), 6.4.2f) to "fluctuation of characteristics";

--- Modify the "individual value" and "range" in the header row of Table 7 and the header row of Table 11 to "Individual Value Control Chart" and "Moving Range Control Chart" respectively;

--- Put "Rexp=(1.128 x Expected Standard" in footnote a of Table 7, footnote a of Table 9, footnote a of Table 11 and footnote a of Table 13

difference) when the moving range is 2" is modified to "Rexp=(1.128xexpected standard deviation), when considering the moving range of adjacent data

Time";

--- Modify "based on the moving range of 2" in the second row of paragraph 2 of 6.2.4 to "based on the moving range of adjacent data";

--- Modify the "subgroup number" in the index number description X of Figure 9, Figure 10, Figure 11, and Figure 12 to "measurement value number";

--- Modify "Moving Average" and "Moving Range" in the header of Table 9 and Table 13 to "Moving Average Control Chart" and "Moving Range Control Chart";

--- In 6.3.3b), "calculate and draw the point corresponding to every two adjacent (XT) values" is modified to "calculate and draw every two

The point corresponding to the moving average of the adjacent (XT) values";

--- Amend "Table 7" in the second sentence of paragraph 1 of 6.3.4 to "Table 8";

--- Modify the title of Figure 10 "Moving Average and Moving Range Control Chart for Variable Targets" to "Moving Average Control for Variable Targets"

picture";

--- Change "Table 10" in the Note to Table 11 to "Table 11";

--- Modify "single sample" at the beginning of paragraph 2 of 6.4.4 to "single observation";

--- Change "Table 12" in 6.5.3f) to "Table 13";

--- Modify "Single Value" in Figure 12 Legend Y to "Moving Average";

--- Modify "Xvariable" in Figure A.1 to "X", and "range" to "moving range";

--- In Figure A.3, "mean Xmoving" is changed to "single value X", and "range" is changed to "moving range";

---Add "target" to the header space part of the fifth line from the bottom of Figure A.3;

--- Delete "Xmoving" in the header of the second last line of Figure A.3;

--- In Figure A.2 and Figure A.4, "Average" is modified to "Moving Average", and "Range" is modified to "Moving Range".

Please note that some content of this document may be patented. The issuing agency of this document assumes no responsibility for identifying patents.

Introduction

Charting is a commonly used statistical tool in process control to monitor deviations in the process and keep the process stable. GB/T 17989 Control Chart

The series of standards is divided into the following 9 parts.

--- Control Charts Part 1.General Guidelines. The purpose is to give the basic terms, principles and classification of control charts, and to select control charts

guide.

--- Control charts Part 2.General control charts. The purpose is to establish guidelines for process control using conventional control charts.

--- Control charts Part 3.Acceptance control charts. The purpose is to establish guidelines for the use of acceptance control charts for process control, and to specify

General procedures for determining subgroup sample sizes, action limits, and decision criteria are described.

--- Control Charts Part 4.Cumulative and Control Charts. The purpose is to establish the application of cumulative and techniques for process monitoring, control and review

Statistical methods for sex analysis.

--- Statistical methods for quality control of production process control charts Part 5.Special control charts. The purpose is to establish the understanding and application of

A guide to statistical process control with special control charts.

--- Production process quality control statistical methods control charts Part 6.Exponentially weighted moving average control charts. The purpose is to establish

A guide to understanding and applying exponentially weighted moving average (EWMA) charts for statistical process control.

--- Statistical methods for quality control of production process control charts Part 7.Multivariate control charts. The purpose is to establish the construction and application of multiple

A guide to statistical process control with meta-control charts and establishes routine methods for using and understanding multi-variable control charts for measurement data.

--- Statistical methods of production process quality control control chart Part 8.Control methods for short cycle and small batches. The purpose is to establish

When the subgroup size is 1, the conventional metrology control chart is used to detect the method of short cycle and small batch production process.

--- Statistical methods for quality control of production process control charts Part 9.Stationary process control charts. The purpose is to establish the construction and application of

A guide to controlling stationary processes with control charts.

It is generally recommended to collect at least 25 subgroups as the basis for constructing conventional quantitative control charts, so that some constructive analysis can be carried out.

analysis. This is the application of statistical process control to plot a control chart for a single product characteristic (such as diameter) or a process parameter (such as temperature) in mass production

the best way. However, many potential applications of statistical process control are problematic.

In the commercial society, high-efficiency systems have increasing requirements for multi-functionality and flexibility to support just-in-time production inventory management, and with the help of smaller

Batches and shorter cycle times enable more diverse product assortments. This is followed by ever-increasing resets, conversions, and mold changes, which

The effective application of metering process control brings new challenges. These challenges are occurring at a critical time—the pressure for continuous performance improvement

It's never been this big.

The process can accommodate the production of many parts, preferably similar in shape but with different nominal dimensions, and the parts are set with a variety of characteristics, including

Different nominal values, different measurement units, and different tolerances. For example, bolt manufacturers produce sizes (diameter and length) in short cycles

Different bolts, pipe extruders produce pipes with different outer diameters, inner diameters and wall thicknesses. Conventional practice is to set each feature for each part

Count a control chart. Such a cumbersome, product-centric approach will only generate a huge number of line graphs, each with too much data.

Sparse, neither for process control nor for quality improvement.

In the same way as other challenges are met, such as. Lean thinking and the introduction of rapid die change technology in production, statistical process control also requires

Responding with more convenient methods is both a problem and a challenge.

The problem arises because. In the corporate world, production cycles are often too short to generate enough data to apply conventional ideas.

Defined control chart. This type of problem can arise in two ways. first, the production batch is too small; second, the run time is too short, such as a high-speed press

The bed may only be running for a short time. Either way, it was not possible to get enough subgroups for the control chart to monitor effectively.

The opportunity arises because. Many current applications of statistical process control are actually statistical product control, in other words, implementing statistical processes

Control is often product-centric rather than process-centric. Different products produced by the same or similar processes are considered distinct

entity. Therefore, sources of process fluctuations are ignored when analyzing product-oriented control charts. Due to the short cycle and small batches, the product letter

Because of the scarcity of information, the focus is on the common element, that is, the process. Short-cycle statistical process control provides a series of product-related

method for converting short-term tasks into long-term processes. For example. a workshop that does not make too many identical parts, but has many

The process runs continuously. They keep turning more shafts and drilling more holes. Processes such as drilling, turning, grinding, etc. or corresponding equipment

Grouping facilities (e.g. machine tools) in preparation for the application of short-cycle statistical process control.

This document presents some basic statistical concepts, terminology and notation, but is as refined as possible. Choose a description that is as close to the actual work as possible

rather than statistical terms. Its purpose is to make this document accessible to a broad range of potential users, as well as to facilitate broad communication and methodological understanding.

comprehend.

Before reading this document, readers who are not familiar with control charts are recommended to read GB/T 17989.1 and GB/T 17989.2.

Statistical Methods for Quality Control in Production Processes

Control Charts Part 8.Short Cycle Small Batches

Control Method

1 Scope

This document describes the application of conventional metrology control charts to monitor short cycle and small batch production processes where the subgroup size is limited to 1.

program method. It provides a set of tools to help understand the sources of volatility in these processes in order to better manage them.

This document applies to the process control of metrological quality characteristics with a subgroup size of 1.The control charts involved here are process-directed

oriented rather than product oriented. Users can point, monitor and control similar characteristics of different products on the same control chart, or the same product

different characteristics.

Note 1.The definitions of the terms short cycle and small batch are not perfect. Short cycle times and small batches in this document refer to the production of that product before another product is subsequently produced.

Only produced in small quantities.

Note 2.When the subgroup size is greater than 1, other standards apply.

2 Normative references

The contents of the following documents constitute essential provisions of this document through normative references in the text. Among them, dated citations

documents, only the version corresponding to that date applies to this document; for undated references, the latest edition (including all amendments) applies to

this document.

GB/T 3358.2-2009 Statistical vocabulary and symbols - Part 2.Applied statistics (ISO 3534-2.2006, IDT)

3 Terms and definitions, symbols