What is the main purpose of control charts

The main purpose of the control chart is to analyze and judge whether the process is in a stable state; by observing the distribution of product quality characteristic values ​​on the control chart, you can analyze and judge whether an abnormality has occurred in the production process. Once an abnormality is discovered, Take necessary measures in time to eliminate them and restore the production process to a stable state; you can also use control charts to bring the production process to a state of statistical control.

The operating environment of this tutorial: Windows 7 system, Dell G3 computer.

Control Chart, also called a control chart, is a chart designed using statistical methods to measure, record, and evaluate process quality characteristics to monitor whether the process is in a controlled state.

Control chart is a chart with control limits used to analyze and judge whether the process is in a stable state. It is a functional chart that can distinguish between normal fluctuations and abnormal fluctuations. It is an important statistical tool in on-site quality management. .

Control chart explanation

• 1. A tool for graphical feedback process in real time.

• 2. The purpose of the design is to tell the operator when to do or not do something.

• 3. Show the personality/performance of the process in chronological order.

• 4. Designed to distinguish signal from noise.

• 5. Detect changes in the mean and/or standard deviation.

• 6. Used to determine whether a process is stable (predictable) or out of control (unpredictable).

Control Chart Misconception

• 1. It is not a substitute tool for capability analysis.

• 2. It is difficult to use in the process of incoming material inspection (no time series).

• 3. Control charts are not efficient comparative analysis tools.

• 4. Should not be confused with operational chart or pre-control chart.

Control charts apply "bounds" to distinguish whether there are significant changes in the process or the presence of abnormal events. Since the setting of control limits is based on data, control limits cannot be determined until a certain amount of representative data is collected. If control limits are used incorrectly, not only will it cause confusion to users, but they will also be counterproductive to measures to achieve process improvement through chart monitoring.

There are three straight lines parallel to the horizontal axis on the control chart: central line (CL, Central Line), upper control limit (UCL, Upper Control Limit) and lower control limit (LCL, Lower Control Limit), and There is a plot sequence of sample statistic values ​​drawn in chronological order.

UCL, CL, and LCL are collectively called control limits (Control Limit). Usually the control limit is set at a position of ±3 standard deviations. The center line is the mean value of the controlled statistic, and the upper and lower control limits are several standard deviations away from the center line. If the plot points in the control chart fall outside the UCL and LCL or the arrangement of the plot points between the UCL and LCL is not random, it indicates that the process is abnormal.

The purpose of using control charts

By observing the distribution of product quality characteristic values ​​on the control chart, analyze and judge whether abnormalities have occurred in the production process. Once abnormalities are found, Necessary measures must be taken promptly to eliminate them and restore the production process to a stable state. Control charts can also be applied to bring the production process to a state of statistical control. The distribution of product quality characteristic values ​​is a statistical distribution.

Classification of control charts

According to the different purposes of using control charts, control charts can be divided into: control charts for analysis and control charts for control.

According to different types of statistical data, control charts can be divided into: measurement control charts and counting control charts (including piece counting control charts and point counting control charts). They are suitable for different production processes. Each category can be subdivided into specific control charts, initially consisting of seven basic charts.

Metric control charts include:

• * IX-MR (single value moving range chart)

• * Xbar -R (mean range chart)

• * Xbar-s (mean standard deviation chart)

Count control charts include:

• * P (Number of defective items for variable sample size)

• * Np (Number of defective items for fixed sample size)

• * u (number of defects per unit for variable sample size)

• * c (number of defects for fixed sample size)

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