• Basic plots of statistical variation to show trends. Uses basic values like,
• the uses for control charts,
- these give a measure of performance, and therefore we can estimate the benefits of process parameter adjustment.
- process capability can be determined
- process specifications can be made greater than process capability
- can indicate when a process is out of control, and be used to reject a batch of product.
• values used for control charts should be numerical, and express some desired quality.
• selecting groups of parts for samples are commonly done 2 ways.
INSTANT-TIME METHOD - at predictable times pick consecutive samples from a machine. This tends to reduce sample variance, and is best used when looking for process setting problems.
PERIOD-OF-TIME METHOD - Samples are selected from parts so that they have not been presented consecutively. This is best used when looking at overall quality when the process has a great deal of variability.
• Samples should be homogenous, from same machine, operator, etc. to avoid multi-modal distributions.
• Suggest sample group size can be based on the size of the production batch
• The central line is an average of ‘g’ historical values.
• The control limits are +/-3 σ of the historical values
• At start-up these values are not valid, but over time it is easy to develop a tight set of values.
• For the non-technical operators there are a couple of techniques used.
- to simplify calculation of the control limits we can approximate σ with
• Over time σR and σX should decrease.
• If some known problems occurred that created out of control points, we can often eliminate them from the data and recreate the chart for more accurate control limits.
• If we recalculate values from the beginning there is no problem, but if we are using the numerical approximation (using constants from table B)
• Instead of an R-chart we can use an s-chart to measure variance
• This chart will reduce the effect of extreme values that will occur with R charts. And, as the number of samples grows, so does the chance of extreme values to throw off the R-chart.
• the approximate technique is,
• We consider a point that lies outside of the 3σ control limits to be very unlikely, therefore the process is ‘out of control’
• In some cases a process may have points within the control limits, but in highly unlikely trends that indicate a process is out of control
1. change or jump in level (X)
- indicates a discontinuity in the process, could be caused by material, new operator, etc.
- e.g. measurement gauge has slipped.
2. Change or jump in level (R)
- indicates a change in process accuracy, caused by failure of small parts, material, etc.
- e.g. measurement gauge has stretched
3. trend, or steady change in level (X and R)
- indicates a gradual change in the process caused by wear, aging, etc.
- indicates time variance in process
- e.g. shift change, day-night temp changes, etc.
- indicates multimodal distributions caused by mixed material batches, alternate operators, etc.
- e.g. steel from scrap dealer, and from steelco is used.
- readings are incorrect because of misreadings, transcription error, etc.
