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What are Process Control Charts?
What are Statistical Process Control Charts?
The statistical process control chart also known as “Shewhart chart” after its inventor is a graph used to study how a process changes over time. A control chart has a central line for the average or target, an upper line for the upper control limit, and a lower line for the lower control limit. These boundary lines are determined statistically from historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).
Regular monitoring of a process control chart can save unnecessary inspection and adjustments allowing for proactive response rather than a reactive response when it may be “too late” or “costly” and it is for this reason they are also known as “process-behavior charts”.
What are the different types of sources of variation?
A process is said to be in a “state of statistical control” when only common cause variation exists and when the statistical properties do not vary over time. There are two types of sources of variation, “common” and “special”.
Common cause variation is natural and inherent variation within the process and occurs with every data point (or part being measured). Before assessing the process capability, the variation must exhibit common cause variation. It is important to have a meaningful process capability that won't be subject to outliers and variation from an unstable process.
Special cause variation is usually identified by points lying outside the upper control limit (UCL) and lower control limit (LCL). However, there are also cases where the data points may lie within the control limits and still represent special cause variation, such as trends and other typical influenced variation.
In either case it is assignable or not necessarily affecting every part. Sometimes found to be results of a machine change, operator change, or major underlying condition change. If you're tracking the miles per gallon of vehicles and you switch surfaces from asphalt to dirt to concrete there will be special cause variation.
What are the different types of Control charts and how to select the right one?
The type and selection of control depends on the type of data that is being collected. Data can be generally classified in to two categories, “continuous” and “attribute”.
Continuous Data theoretically has an infinite number of measurements depending on the resolution of the measurement system. There are no limits to the gaps between the measurements. It is data that can be expressed on an infinitely divisible scale. The continuous data generally represent things that are measured, NOT counted. Examples of continuous data are Temperature, Height, Money, and Weight.
Attribute Data have a finite number of measurements and can be based on counts or used to represent the presence or lack of a certain characteristic. It is data that can be sorted into distinct, countable, and in completely separate categories. The count value cannot be divided further on an infinite scale with meaning. Examples of continuous data are pass / fail, rating 1-10, number of defects per part or in a batch of parts. Attribute data is also known as “discrete” data.
Control charts for continuous data are used in pairs. The top chart monitors the average, or the centering of the distribution of data from the process. The bottom chart monitors the range or standard deviation of the distribution. If your data were shots in target practice, the average is where the shots are clustering, the range is the width of cluster and standard deviation is the best fit deviation from the average within the cluster. Control charts for attribute data are always used singly.
This table below tabulates the most common control charts based on data types
Control Chart Types | Usage Description |
Continuous Data | |
Xbar or Xbar -MR | This type of chart demonstrates the centering of the data collected from a process, either a single sample or averages of a set of samples. |
Xbar-Sigma or Xbar-Range | This type of chart demonstrates the centering and variability of the data collected from a process. Depending on the sample-size its averages with a range chart (small sample size) or averages with a sigma chart (large sample size). |
Xbar-Range-Sigma (3D) | This type of chart demonstrates the centering and variability of the data collected from a process and charts all three characteristics of the distribution of the data. |
Attribute or Discrete Data | |
p-chart | This is used to show if the proportion defective within a process changes over the sampling period (the p indicates the portion of successes). In this chart the sample size can vary over time |
np-chart | This is used to show the number of defective within a process changes over the sampling period. In this chart the sample size remains constant over time This chart shows the number non-conformances recorded onto the control rather than the fraction. |
c-chart | This is used where there can be a number of defects per sample unit and the number of samples per sampling period remains constant. |
u-chart | This is used where there can be a number of defects per sample unit and the number of samples per sampling period may vary. |
The flow-chart below provides a quick cheat-sheet to determine which control chart to pick based on data type.
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Abeer Singhal
Abeer has over a decade of experience in semiconductor processing and advanced manufacturing. He is co-inventor on U.S. patents in the field of semiconductor manufacturing and has extensive experience implementing advanced manufacturing systems for high volume and complexity products. Prior to his role at Sentient, Abeer has worked as an equipment technician, process engineer, advanced process control systems engineer, and manufacturing systems architect. He has a degree in Microelectronic Engineering.