![The substituent effect of π-electron delocalization in N … · 2020-04-30 · R[F2 >2σ(F2)] Final R indices R1=0.0572,wR2=0.0958 R1=0.0364,wR2=0.0549 R1=0.0252, wR2=0.0721 R indices](https://static.fdocument.org/doc/165x107/5f6e463324a3df634645499f/the-substituent-effect-of-electron-delocalization-in-n-2020-04-30-rf2-2ff2.jpg)
SPC Pocket Card Exterior copy - SPC SoftwareSPC Pocket Card-3σ-3 σ+3-2σ -1σ X +1σ +2σ +3σ...
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North America: 1.800.772.7978 Europe: +44 (0)800.888.6200 China: 86)10 6569 9909 www.infinityqs.com Washington DC | Seattle | London | Beijing | Shanghai For more information, visit www.infinityqs.com To order pocket cards, contact: P pl = P pu = P p = X – LSL 3s USL – X 3s USL – LSL 6s C pl = C pu = C p = μ – LSL 3σ USL – μ USL – LSL 6σ 3σ s = σ = μ = n – 1 R Estimated population mean d 2 X = Σ X Σ ( X i – X ) 2 n RULE 1 Point beyond the control limit. RULE 3 4 out of 5 Zone B or beyond. RULE 5 Six or more in a row increasing or decreasing. RULE 2 2 out of 3 in Zone A or beyond. RULE 4 8 or more on one side of centerline without crossing. Lower Control Limit Upper Control Limit Zone A Zone B Zone C Zone C Zone B Zone A Special Cause Patterns 0.975 0.927 0.886 0.850 0.817 0.789 0.763 0.739 0.718 0.698 0.680 0.663 0.647 0.633 0.619 0.606 0.284 0.321 0.354 0.382 0.406 0.428 0.448 0.466 0.482 0.497 0.510 0.523 0.534 0.545 0.555 0.565 1.716 1.679 1.646 1.618 1.594 1.572 1.552 1.534 1.518 1.503 1.490 1.477 1.466 1.455 1.445 1.435 0.9727 0.9754 0.9776 0.9794 0.9810 0.9823 0.9835 0.9845 0.9854 0.9862 0.9869 0.9876 0.9882 0.9887 0.9892 0.9896 n A 2 D 3 D 4 d 2 2.660 1.880 1.023 0.729 0.577 0.483 0.419 0.373 0.337 0.308 0.285 0.266 - 0 0 0 0 0 0.076 0.136 0.184 0.223 0.256 0.283 - 3.267 2.574 2.282 2.114 2.004 1.924 1.864 1.816 1.777 1.744 1.717 - 1.128 1.693 2.059 2.326 2.534 2.704 2.847 2.970 3.078 3.173 3.258 133,620 71,860 35,730 16,396 6,934 2,700 966 318 96 26 7 2 0.340 0.060 0.012 0.002 66,810 35,930 17,865 8,198 3,467 1,350 483 159 48 13 3 1 0.170 0.030 0.006 0.001 Fallout rates expressed in PPM (parts per million) P p Fallout (both sides combined) Calculated Capability Ratio X and R Control Charts X and S Control Charts P pk Fallout (one side only) 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 1.40 1.50 1.60 1.70 1.80 1.90 2.00 1 2 3 4 5 6 7 8 9 10 11 12 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 n A 3 B 3 B 4 c 4 Control Chart Constants P p and P pk Fallout Rates SPC Pocket Card -3σ -3σ +3σ -2σ -1σ +1σ +2σ +3σ X 68% Capability (6σ) Voice of the Process Upper Specification Limit Lower Specification Limit Engineering Tolerance Voice of the Customer 95% 99.7% Capability Study Empirical Rule Capability Formulas
Transcript of SPC Pocket Card Exterior copy - SPC SoftwareSPC Pocket Card-3σ-3 σ+3-2σ -1σ X +1σ +2σ +3σ...
North America: 1.800.772.7978Europe: +44 (0)800.888.6200
China: 86)10 6569 9909
www.infinityqs.com
Washington DC | Seattle | London | Beijing | Shanghai
For more information, visit www.infinityqs.com
To order pocket cards, contact:
Ppl
=
Ppu
=
Pp =
X – LSL
3s
USL – X
3s
USL – LSL
6s
Cpl
=
Cpu
=
Cp =
µ – LSL
3σ
USL – µ
USL – LSL
6σ
3σ
s =
σ =
µ =
n – 1R
Estimated population mean
d2
X =
Σ X
Σ ( Xi – X )2
n
RULE 1Point beyondthe control limit.
RULE 34 out of 5 Zone B or beyond.
RULE 5Six or more in a rowincreasing or decreasing.
RULE 22 out of 3 in Zone A or beyond.
RULE 48 or more on one sideof centerline without crossing.
Lower Control Limit
Upper Control Limit
Zone A
Zone B
Zone C
Zone C
Zone B
Zone A
Special Cause Patterns
0.975
0.927
0.886
0.850
0.817
0.789
0.763
0.739
0.718
0.698
0.680
0.663
0.647
0.633
0.619
0.606
0.284
0.321
0.354
0.382
0.406
0.428
0.448
0.466
0.482
0.497
0.510
0.523
0.534
0.545
0.555
0.565
1.716
1.679
1.646
1.618
1.594
1.572
1.552
1.534
1.518
1.503
1.490
1.477
1.466
1.455
1.445
1.435
0.9727
0.9754
0.9776
0.9794
0.9810
0.9823
0.9835
0.9845
0.9854
0.9862
0.9869
0.9876
0.9882
0.9887
0.9892
0.9896
n A2 D3 D4 d22.660
1.880
1.023
0.729
0.577
0.483
0.419
0.373
0.337
0.308
0.285
0.266
-
0
0
0
0
0
0.076
0.136
0.184
0.223
0.256
0.283
-
3.267
2.574
2.282
2.114
2.004
1.924
1.864
1.816
1.777
1.744
1.717
-
1.128
1.693
2.059
2.326
2.534
2.704
2.847
2.970
3.078
3.173
3.258
133,620 71,86035,73016,3966,9342,700
9663189626720.3400.0600.0120.002
66, 81035,93017,8658,1983,4671,350
483159481331 0.1700.0300.0060.001
Fallout rates expressed in PPM (parts per million)
Pp Fallout(both sides combined)
Calculated Capability Ratio
X and R Control Charts X and S Control Charts
Ppk Fallout(one side only)
0.500.600.700.800.901.001.101.201.301.401.501.601.701.801.902.00
1
2
3
4
5
6
7
8
9
10
11
12
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
n A3 B3 B4 c4
Control Chart Constants
Pp and Ppk Fallout Rates
SPC Pocket Card
-3σ
-3σ +3σ
-2σ -1σ +1σ +2σ +3σX68%
Capability (6σ)Voice of the Process
UpperSpecification
Limit
LowerSpecification
Limit
Engineering ToleranceVoice of the Customer
95%
99.7%
Capability Study
Empirical Rule
Capability Formulas
Xand R
IXandMR
Xands
X =∑ X
kUCL X = X + A2 R
LCL X = X – A2 R
UCL R = D4 R
LCL R
UCL IX = IX + A2 MR
LCL IX = IX - A2 MR
UCL MR = D4 MRLCL MR = 0
UCL X = X + A3 s
LCL X = X - A3 sUCL s = B4 s
LCL s = B3 s
R =∑ R
k
IX =∑ IX
k
MR =∑MR
k-1
X =∑ X
k
s =∑ s
k
MRd2
sc4
= D3 R
Variables Control Charts
Attribute Control Charts
p
np
c
u
p =∑ p
k
np =∑ np
k
c =∑ c
k
u =∑ u
k
UCLp = p +3
LCL p = p-3
UCLnp = np+3
LCL np= np-3
UCLc = c+3
LCL c = c-3
UCLu = u+3
LCL u= u-3
p(1- p )
))n
p(1- pn
np(1- p)
np(1- p)
c
c
un
un
un
np (1- p)
p (1- pn
c
Rd2
Estimate of SigmaControl LimitsCenterlineChart Type
Estimate of SigmaControl LimitsCenterlineChart Type
Control Chart Formulas
YesNoYesNoYesNoYesNoYesNoYesNoYesNoYesNo
YesNoYesNoYesNoYesNoYesNoYesNoYesNoYesNo
YesNoYesNoYesNoYesNoYesNoYesNoYesNoYesNo
– Group 3D Standardized IX-MR– Group 3D Target IX-MR– Group 3D Standardized IX-MR– Group 3D IX-MR– Group Standardized IX-MR– Group Target IX-MR– Group Standardized IX-MR– Group IX-MR– 3D Standardized IX-MR– 3D Target IX-MR– 3D Standardized IX-MR– 3D IX-MR– Standardized IX-MR– Target IX-MR– Standardized IX-MR– IX-MR
– Group 3D Standardized Xbar-R– Group 3D Target Xbar-R– Group 3D Standardized Xbar-R– Group 3D Xbar-R– Group Standardized Xbar-R– Group Target Xbar-R– Group Standardized Xbar-R– Group Xbar-R– 3D Standardized Xbar-R– 3D Target Xbar-R – 3D Standardized Xbar-R– 3D Xbar-R– Standardized Xbar-R– Target Xbar-R– Standardized Xbar-R– Xbar-R
– Group 3D Standardized Xbar-S– Group 3D Target Xbar-S– Group 3D Standardized Xbar-S– Group 3D Xbar-S– Group Standardized Xbar-S– Group Target Xbar-S – Group Standardized Xbar-S– Group Xbar-S– 3D Standardized Xbar-S– 3D Target Xbar-S – 3D Standardized Xbar-S– 3D Xbar-S– Standardized Xbar-S– Target Xbar-S– Standardized Xbar-S– Xbar-S
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
n=1
n>1<10
n>9
InfinityQS Supports All These Variables Control Charts And Many MoreInfinityQS Supports All These Variables Control Charts And Many More
Question 5
Coded?
Question 4
Target?
Question 3
3D?
Question 2
Group?
Question1
SampleSize?
Use This Chart
Variable Data Control Chart Selection Tree
Defects
Defectives
Question 1Data Type?
Question 2Sample Size?
Question 3Group?
Question 4Coded?
Use This Chart
Constant
Constant
Varies
Varies
YesYes – Group DPMO or Group DPTO
No
No – Group cYes – DPMO or DPTONo – cYes – Group DPMO or Group DPTONo – Group uYes – DPMO or DPTONo – uYes – Group PPM or Group PPTNo – Group npYes – PPM or PPTNo – npYes – Group PPM or Group PPTNo – Group npYes – PPM or PPTNo – p
Yes
No
Yes
No
Yes
No
Attribute Data Control Chart Selection Tree
Data Type: There are two types of attribute data - defects and defectives. Defects data are count data and are described with a Poisson distribution. Counting the number of visual blemishes on a part or the number of support calls received in an hour are examples of defects data. Defectives data are pass/fail in nature.The number of rejected parts in a lot is an example of defective data. Defectives data are described with a binomial distribution.
Sample Size: The number of items in a single subgroup.
Group: Group processing is required when desiring to combine multiple process streams on the same chart. Plotting the output from multiple fill heads, a multi-cavity mold or multiple lines are classic examples for using Group charts.
3D: Used when measuring within-piece and piece-to-piece variation. Examples include measuring a spacer thickness in multiple places or measuring a bore diameter in three places to test for out-of-roundness.
Target: This processing is required when combining characteristics on the same chart that have different nominal or target values.
Coded: This processing is required when combining characteristics on the same chart that are of different units of measure, different expected levels of variation or different expected fallout rates.
Decision Tree Definitions
