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12 Cards in this Set
- Front
- Back
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SPC
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Technique for applying statistical analysis to measure, monitor, and control processes
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Variation
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Classified as chance (common) cause, assignable cause
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Subgroup selection
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Makes subroup as homogenous as possible and maximizes opportunity for variation from one subgroup to another
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Sources of variability
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1) Lot-to-lot
2) Stream-to-stream 3) Time-to-time 4) Piece-to-Piece 5) Error of measurement: equipment and human |
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Process in statistical control
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Characterized by plot points that do not exceed the upper or lower control limits.
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X bar - R Chart
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UCL(Xbar)= Xbarbar + A2*Rbar
LCL(Xbar)= Xbarbar - A2*Rbar UCL(R)= D4*Rbar LCL(R)=D3*Rbar Used when data is readily available Limits at 3 sigma (covers 99.73% of population) |
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Xbar-s
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s=√(Σ(X-Xbar)^2/(n-1))
UCL(Xbar)=Xbarbar + A3*sbar LCL(Xbar)=Xbarbar - A3*sbar UCL(s)=B4*sbar LCL(s)=B3*sbar Used when larger sample sizes are used for increased sensistivity to variation |
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MXbar-MR
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UCL(Xbar)=Xbarbar + A3*sbar
LCL(Xbar)=Xbarbar - A3*sbar UCL(s)=B4*sbar LCL(s)=B3*sbar Individual Xbar's and R's are calculated based upon some number of measurements in a sample. Each sample is comprised of one new measurement and the rest old measurements. Used where data is less readily available |
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X-MR
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Individual data points and moving range
UCL(X)= Xbar+E2*MRbar LCL(X)= Xbar-E2*MRbar UCL(MR)= D4*MRbar ONLY chart which may have specification limits NOT as sensitive to process changes |
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CuSum
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More efficient at detecting small shifts (2 sigma or less)
If process remains in control, centered at μ0, the CuSum plot shows variation in a random pattern about zero. If the process drifts, the CuSum points will drift as well. |
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Exponentially Weighted Moving Average (EWMA)
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EWMA(t)=λY(t)+(1-λ)EWMA(t-1)
s^2(EWMA)=(λ/(2-λ))s^2 UCL=EWMA0+ks(EWMA) LCL=EWMA0-ks(EWMA) k=3 |
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p Chart
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Fraction defective
UCLp=pbar+3*sqrt((pbar(1-pbar))/n) |
