1 smu emis 7364 ntu to-570-n more control charts material updated: 3/24/04 statistical quality...
DESCRIPTION
3 Operating Characteristic (OC) Function for the x – Chart (continued)TRANSCRIPT
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SMUEMIS 7364
NTUTO-570-N
More Control Charts Material Updated: 3/24/04
Statistical Quality ControlDr. Jerrell T. Stracener, SAE Fellow
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Operating Characteristic (OC) Function for the x - Chart
• The OC curve describes the ability of the x-chart to detect shifts in process quality.
• For an x-chart with known & constant mean shifts from in-control value, 0 to another value 1, where
1 = 0 + K
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Operating Characteristic (OC) Function for the x – Chart (continued)
μβμOC
σKσμLσnμΦ
σKσμLσnμΦ
nσ
Kσμ-LCLΦ
nσ
Kσμ-UCLΦ
Kσμμμ|UCLXLCLPμ)|sample subsequent
first on theshift detectingP(not
00
00
00
01
n
n
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Operating Characteristic (OC) Function for the x – Chart (continued)
where
andL is usually 3, the three-sigma limits
dzey 2zy 2
2π1Φ
,nkLΦnkLΦ
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Example
If n=5 & L=3, determine & plot the OC function vs K, where 1= 0 + K.
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Example - Solution
KβKOC 53Φ53Φ
ΦΦ
kk
nkLnkL
k b
-3.0 0.000104397-2.5 0.00479646-2.0 0.070492119-1.5 0.361631295-1.0 0.777546112-0.5 0.9700606330.0 0.9973000660.5 0.9700606331.0 0.7775461121.5 0.3616312952.0 0.0704921192.5 0.004796463.0 0.000104397
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
-4.0 -2.0 0.0 2.0 4.0 6.0
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OC Function of the Fraction Nonconforming Control Chart
pβpOC
nLCLFnUCLF
p1pdn
p1pdn
p|nLCLDPp|nUCLDPp|LCLp̂Pp|UCLp̂P
p)|control lstatisticain is process a that hypothesis thegP(acceptin
nLCL
0d
dndnUCL
0d
dnd
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OC Function of the Fraction Nonconforming Control Chart
Where
[nUCL] denotes the largest integer nUCLand <nLCL> denotes the smallest integer nLCL
Note: The OC curve provides a measure of the sensitivity of the control chart – i.e., its ability to detect a shift in the process fraction nonconforming from the nominal value p to some other value p.
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Example
For a fraction nonconforming control chart with parameters
n = 50,LCL = 0.0303,
andUCL = 0.3697,
Determine & plot the OC curve.
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Example - Solution
pβpOC p|52.1DPp|49.81DP
p|0.030350DPp|0.369750DP
p P(D<=18|p) P(D<=1|p) P(D<=18|p) - P(D<=1|p)0.01 1.0000 0.9106 0.08940.03 1.0000 0.5553 0.44470.05 1.0000 0.2794 0.72060.10 1.0000 0.0338 0.96620.15 0.9999 0.0291 0.97080.20 0.9975 0.0002 0.99730.25 0.9713 0.0001 0.97120.30 0.8594 0.0000 0.85940.35 0.6216 0.0000 0.62160.40 0.3356 0.0000 0.33560.45 0.1273 0.0000 0.12730.50 0.0325 0.0000 0.03250.55 0.0053 0.0000 0.0053
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Example - Solution
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
p
OC(p)
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OC Function for c-charts and u-charts
• For the c-chart cβcOC
LCLFUCLF!!
c|LCLXPc|UCLXPc)|control lstatisticain is process a
that hypothesis thegP(acceptin
LCL
0d
UCL
0d
xce
xce xcxc
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OC Function for c-charts and u-charts
• For the u-chart uβuOC
nLCL xwhere!x
u|nLCLXPu|nUCLXPu)|control lstatisticain is process a
that hypothesis thegP(acceptin
nUCL
0d
x
nu nue
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Example
Determine & plot the OC function for a u-chart with parameter.
LCL = 6.48,and
UCL = 32.22.
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Example - Solution
uβuOC
nUCL
0d !x
u|UCLxUCLPu|UCLcPu|UCLcP
u|UCLxPu|UCLxP
xnu nue
nnnn
u P(D<=33|c) P(D<=6|c) P(D<=33|c) - P(D<=6|c)0.01 1.000 0.999 0.0010.03 1.000 0.996 0.0040.05 1.000 0.762 0.2380.10 1.000 0.450 0.5500.15 1.000 0.220 0.7800.20 0.999 0.008 0.9910.25 0.997 0.000 0.9970.30 0.950 0.000 0.9500.35 0.744 0.000 0.7440.40 0.546 0.000 0.5460.45 0.410 0.000 0.4100.50 0.151 0.000 0.1510.55 0.038 0.000 0.038
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-0.2
0.0
0.2
0.4
0.6
0.8
1.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Example - Solution
u
OC(u)
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Average Run Length for x-Charts
• Performance of Control Charts can be characterized by their run length distribution.
• Run Length (RL) of a control chart is defined to be the number of samples until the process characteristic exceeds the control limits for the first time.
• Run Length, RL, is a random variable and therefore has a probability distribution
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Average Run Length for x-Charts
Let p = P(x falls outside control limits)
Then
P(RL = 1) = P(x1 falls outside CL)=pP(RL = 2) = P(x1 falls inside CL & x2 falls outside of CL)
= (1-p)pP(RL = 3) = P(x1, x2 fall inside CL & x3 falls outside of CL)
= (1-p)(1-p)p
P(RL = i) = P(x1, x2, … xi-1 fall inside CL & xi falls outside of CL)
= (1-p)i-1p
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Average Run Length for x-Charts
Therefore, the probability mass function of RL is
The mean or expected value of RL is kRLP 1,2,...Kfor pp1 1K
RLEμ
1a
1a
32
321
1K
1K
p1ap
...p-14p-13p-121p
...p-14pp-13pp-12pp
pp1K
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Average Run Length for x-Charts
• The Average Run Length, ARL, indicates the number of samples needed, on the average before x will exceed the control chart limits.
p1
p-1-11p 2
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Probability of Out-Of-Control Signal and ARL
• Process in control with mean 0
• p = 1 – P(LCL x UCL) = 0.0027
• ARL
i.e., one the average we would expect 1 out-of-control signal out of 370 samples.
,3700.0027
1p1
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Probability of Out-Of-Control Signal and ARL
• Process in control with mean 10+with constant
• What happens if the process goes out of control?
• How long does it take until the control charts detects the shift?
• Probability of detecting shift
n
xn
3μ3μP1δp 00
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Probability of Out-Of-Control Signal and ARL
nδ3Pnδ3P
nδ3nδ3P1
nσ
δσμn
σ3μZ
nσ
δσμn
σ3μP1
0000
ZZ
Z
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Example
For example, if n = 5, and = 1,
and
2225.07775.00000.01
764.01236.553P53P
ZZ 1p
495.40.2225
11p
1
ARL