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Median & Range Charts

- If the distribution is normal, then mean=median=mode
- If R is in control and distribution is normal, then alternatively you can plot the medians
- Disadvantage: Less sensitive to changes in the average
- Advantage: Simpler to use

11 step procedure for control charts (median&R)

- Select a process measurement
- Stabilize process and decrease obvious variability
- Check the gages (10:1, GRR)
- Make a sample plan. Choose an odd number 3, 5, 7 …
- Setup the charts and process log
- Setup the histogram
- Take the samples and chart the points
- Calculate the control limits and analyze for control
- Calculate the capability and analyze for capability
- Monitor the process
- Continuous improvement

Average & Standard Deviation Charts

- Same type of chart as the average and range chart
- Uses sample standard deviation instead of range
- Used for large sample sizes
- Need the use of a computer or calculator to make this practical

11 step procedure for control charts

- Select a process measurement
- Stabilize process and decrease obvious variability
- Check the gages (10:1, GRR)
- Make a sample plan
- Setup the charts and process log
- Setup the histogram
- Take the samples and chart the points
- Calculate the control limits and analyze for control
- Calculate the capability and analyze for capability
- Monitor the process
- Continuous improvement

For the standard deviation control chart:

For the mean control chart:

How to calculate control limitsIndustrial processes

In industry, there are many different types of processes. The main types, along with a brief description of each are listed below:

- Short run/small run - Could be very slow or very fast manufacturing cycles. Some short run processes produce a few intricate products over long time periods while other short runs produce large numbers of parts, but in short time periods.
- Mass production - Repetitive, long-running assembly line type processes that produce large numbers of individual products.
- Batch - Producing quantities of similar materials in a single lot or batch. Examples include vats of materials produced for the food industry, drums of paint all mixed at one time, or other quantities of raw materials mixed together to produce a finished product.
- Continuous - A non-stop process that is continually fed raw materials on one end, producing a steady stream of finished product on the other end. Examples include petroleum, paper, powders, and pellets.

Short run vs. mass production

- Short run processes include both very slow and very fast manufacturing cycles.
- Short run processes include operations that create:
- A small number of complex products in a long period of time
- A large number of products in a very short period of time
- One product per run

- Short run processes include operations that create:
- Mass production may produce hundreds, thousands or millions of parts per year.
- It becomes economically impossible to measure each and every part as it is finished on the machine.
- Requires dedicated measuring equipment, tooling and checking fixtures to be used and a much different quality measurement system than other processes.

How to handle short run

- We have seen how to deal with large quantities of data – usually associated with mass production (sampling for control and acceptance)
- Part of this topic is in Chapter 7 (short run) and part is in Chapter 8 (small run)
- We also have to think statistical “process” control – not statistical “part” control

Problems with short/small run

- Limited data
- Spread out over long periods of time
- Less chance to detect variation
- Part runs finish before trends can be seen
- Risk of control limits too tight (over sensitive)

S”P”C?

Statistical “process” control or statistical “part” control?

- Your book mentions a way to deal with small data sets using inflated D4 and A2 values.
- We will skip that part because it promotes “part” control and calculating control limits based on limited data
- Instead we will concentrate on the part that promotes “process” control – nominal or target charts
Note:Your book uses a term called the “T test” – don’t confuse this with the student-t test, commonly referred to as the “t-test”

Nominal or target charts

- Used when a processproduces several different parts
- This type of process is assumed to produce the same variation on all parts produced
- This allows all parts to be tracked on one control chart
- Commonly known as the

Criteria for use

- All processes are considered the same
- Variation is the same for all parts produced
- Sample size is the same
- Recommended for use with specific types of gaging that resolves in delta values (i.e. indicators)

Coding data

- Used to simplify arithmetic with the chart
- Used with certain types of gaging
- Used on target charts

How we do it

- Code each measurement by subtracting the target value
- Chart the coded values on the and specify different parts with vertical lines
- Calculate average, range, and control limits - analyze for control in the usual manner
- Calculate and analyze process capability

Transformation chart

- Used to track several measurements from several processes on one chart.
- Only restriction is they must have the same sample size and they are expected to perform the same.
- This is a very advanced control chart used to normalize the data.
- We will do one similar to these when we do the Gage R&R analysis, except we will not code the data.

Nominal chart exercise data

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