Diese Seite ist entworfen worden, um im
Verstehen und im Lernen des Gebrauches, des Designs und der
Analyse von Control Charts zu helfen, die das wichtigste
Hilfsmittel der statistischen Qualitätskontrolle sind.
Die Informationen sind in Form eines
Tutorial formatiert worden, der Sie durch den Prozeß führt. Es
umfaßt die Geschichte, die Hintergrundinformationen, den
Gebrauch, die Arten mit Beispielen, Analyse der Muster, in
Verbindung stehende Software und zusätzliche Quellen über
Control Charts.
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Control charting ist
eins der Hilfsmittel der statistischen Qualitätskontrolle(SQC).
Es ist das technisch am weitesten entwickelte Hilfsmittel von
SQC. Es wurde in den zwanziger Jahren vom Dr. Walter A. Shewhart
den Bell Telephone Labs entwickelt.
Dr. Shewhart entwickelte die Control Charts
als statistisches Verfahren zur Studie der Variation in
Herstellungsverfahren mit dem Ziel der Verbesserung der
ökonomischen Wirksamkeit des Prozesses. Diese Methoden basieren
auf der ununterbrochenen Überwachung der Prozeßvariation.
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A typical control chart is a graphical
display of a quality characteristic that has been measured or
computed from a sample versus the sample number or time. The
chart contains a center line that represents the average value of
the quality characteristic corresponding to the in-control state.
Two other horizontal lines, called the upper control limit(UCL)
and and the lower control limit(LCL) are also drawn. These
control limits are chosen so that if the process is in control,
nearly all of the sample points will fall between them. As long
as the points plot within the control limits, the process is
assumed to be in control, and no action is necessary.
However, a point that plots outside of
the control limits is interpreted as evidence that the process is
out of control, and investigation and corrective action is
required to find and eliminate the assignable causes responsible
for this behavior. The control points are connected with straight
line segments for easy visualization.
Even if all the points plot inside the
control limits, if they behave in a systematic or nonrandom
manner, then this is an indication that the process is out of
control.
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Control chart is a device for describing in a
precise manner what is meant by statistical control. Its uses are
- It is a proven technique for improving
productivity.
- It is effective in defect prevention.
- It prevents unnecessary process
adjustments.
- It provides diagnostic information.
- It provides information about process
capability.
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- Control
charts for Attributes.
- p
chart
- c
chart
- u
chart
- Control
charts for Variables.
- X
bar chart
- R
chart
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A control chart may indicate an
out-of-control condition either when one or more points fall
beyond the control limits, or when the plotted points exhibit
some nonrandom pattern of behavior.
The process is out of control if any one or
more of the criteria is met.
- One or more points outside of the
control limits. This pattern may indicate:
- A run of eight points on one side of
the center line. This pattern indicates a shift in the
process output from changes in the equipment, methods, or
materials or a shift in the measurement system.
- Two of three consecutive points
outside the 2-sigma warning limits but still inside the
control limits. This may be the result of a large shift
in the process in the equipment, methods, materials, or
operator or a shift in the measurement system.
- Four of five consecutive points beyond
the 1-sigma limits.
- An unusual or nonrandom pattern in the
data.
- A trend of seven points in a
row upward or downward. This may show
- Cycling of data can indicate
- Several points near a warning or
control limit.
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The March issue of Quality Progress
contains a directory of software packages available from
different suppliers and arranged in different application
categories.
The Journal of Quality Technology
has published computer programs in either BASIC or FORTRAN since
1969. A selected list of programs, related to control charts,
published through 1989 are in the following table.
---------------------------------------------------------------- Volume PageProgram Capability & year Reference----------------------------------------------------------------1. Plotting X bar and R charts 1/1969 1492. Plotting p and np charts 1/1969 2173. Plotting c and u charts 1/1969 285 4. Economic design of X bar charts 2/1970 405. Plotting cumulative-sum control 2/1970 54 charts6. Plotting exponentially weighted 5/1973 84 moving average control charts7. ARL for cumulative-sum control 18/1986 189 charts for controlling normal means8. ARL for combined individual 19/1987 103 measurement and moving-range charts9. ARL for exponentially weighted 19/1987 161 moving-average control charts10. New limits for X bar and R charts 20/1988 149 sample size is changed11. Optimal design parameters of joint 21/1989 65 X bar and R charts-----------------------------------------------------------------Source : Introduction to Statistical Quality control
- by Douglas C. Montgomery, page
252-254.
-----------------------------------------------------------------
Ordinary spreadsheets like Microsoft Excel
and Lotus 123 can also be used for creating the above control
charts. The charts presented in the examples have been created in
Microsoft Excel.
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