Transcript GR&R and MSA Training

1
1.
Product Control: to prove a particular gage is
capable of distinguishing good parts from bad
parts and can do so accurately every time.
◦ Ideal situation is to have a measurement system:
 …that rejects ALL non-conforming parts
 …that accepts ALL conforming parts
 …that gets repeatable results for ALL operators
2.
Process control: For variable gaging, the desire
is also to ensure that the gage can resolve
small enough changes to allow for process
control initiatives.
2

Gage Repeatability & Reproducibility

Repeatability: how consistently one person
obtains the same measurement on a part
◦ also known as Equipment Variability (EV) or within
system variation

Reproducibility: how consistently multiple
people obtain the same measurement on a
part
◦ also known as Appraiser Variability (AV) or between
system variation
3
Range of 3
readings taken
on each part.
Operator 1 has
a repeatability
issue.
With perfect
repeatability
the range
would be 0.
How consistently can one person get the same measurement on a part?
4
Average of 3
readings taken
on each part.
With perfect
reproducibility
all lines would
be exactly the
same.
Operator 3 has
a
reproducibility
issue.
How consistently can multiple people get the same measurement on a part?
5

Measurement System Analysis

5 Components
◦
◦
◦
◦
◦
Repeatability (EV)
Reproducibility (AV)
Bias
Linearity
Stability
6

Bias: The difference between the average
of measurements and a standard value.
Value of known standard
Average of measured value
Bias
Measurement Scale
7

Linearity: The difference in bias or
repeatability at different points in the
operating range of the gage.
Low End
Measurement Scale
High End
8

Stability: Variation in measurements of a
known master over an extended period of
time.
◦ Variation can be in the amount or direction of bias.
◦ Variation can be in the repeatability of the
measurement.
9

Go watch the inspection
◦ Look for potential issues with the gage
◦ Look for potential issues with the method being
used
◦ ASK QUESTIONS!!

Make sure you understand what is being
inspected and how it is being inspected
◦ Validate the gage is calibrated
◦ Understand it’s finest level of discrimination (.001,
.0001, etc). Does it meet the 10:1 rule?

View more than one operator if you can
◦ Look for differences in technique
10

Ask to see specific instructions on the usage
of the gage. (preferably written instructions)
◦ If there are no formal instructions on the usage of
the gage help the operators create some.
◦ Include several of the usual inspectors in the
creation of the instructions to make sure they are
all on the same page.
The key to a successful study is to reduce all possible variation.
Eliminate as much variation as possible BEFORE getting started.
11





Define: The gage, the SOP, the desired
outcome, etc.
Measure: Do the study
Analyze: Review the study and determine if it
is acceptable or if it needs improvement
Improve: Determine any changes that can or
should be made to improve the variation
Control: Lock in all changes and critical
settings as part of the SOP
12
The next slides specifically
apply to Variable Data studies.
Attribute (Go/NoGo) studies
will be covered later.
13

People
◦ Preferably get 3 people that are familiar with the
parts, the gage, and the feature.
◦ If that is not possible, get people that have used
the specific or similar gage on other parts.
◦ If that is not possible, get people that have used
a wide variety of gages and can be trained on
the usage of the gage by an expert.
The further you must go down this list the more variation is being
brought into the study.
14

Parts
◦ Find 10 parts that are representative of the real
process, ideally using real parts.
 Calibrated “masters” can be used if the part feature is
similar to a master (Plug Gage, Jo Blocks, Ring Gage, etc)
 If you can do this you get the advantage of seeing bias
 If using “masters” ensure you are testing the finest
discrimination of the gage.
 Try to cover the full range of tolerance and if possible
include parts slightly in and slightly out of tolerance.
◦ Uniquely label each part so parts do not get mixed
up.
15

Parts
(cont.)
◦ Variation between parts is the only variation in a
study that is good.
◦ Look for the “right” amount of variation
 Enough spread to prove the gage can tell the
difference between parts.
 Not too much spread where the operator will start
to remember specific readings.
 Have several parts either “identical” or very close in
size as part of the group.
 Have sample parts at or very near each spec. limit.
16
Parts (cont.)
Example: .495 - .505 OD with OD Mic. (w/ Vernier Scale)

.4900
.4920
.4940
.4960
.4980
.5000
.5020
.5040
.5060
.5080
BAD
• WAY below &
above Spec.
• No usage of
.0001’s
• Even increments
may be
remembered by
operators regardless
of randomization.
• No points @ Spec
.4990
So-So
.4948
Best
.4992
• Too tightly within
spec limits
.4950
• Good grouping at
both limits (in and
out)
.4994
.4996
.4998
.5000
.5003
.5006
.5009
.5010
• Ok usage of
.0001’s
• Fairly Even
increments may be
remembered by
operators regardless
of randomization.
• No points @ Spec
.4952
.4973
.4999
.5001
.5024
.5047
.5050
.5051
• Good usage of
.0001’s
• All but 2 points
hard to “remember”
due to similar parts.
• Points at both
specs & split
17

Gage
◦ Does the gage have the right number of
discriminations for the measurement?
 Minimum of 5
 Prefer 10 or more
◦ Verify calibration
◦ Verify gage is functioning properly
◦ Verify there is nothing with the gage that will
obviously sink your study.
◦ If it requires “zeroing” determine what that
process will be for each inspector BEFORE
starting.
18

BE PRESENT!!!!
◦ Administer the study yourself.
◦ Watch each operator closely
◦ Document anything you notice (differences, similarities,
special “tricks,” speed, anything the operator is doing that
they may not even realize they are doing.)
◦ When analyzing the study there is no substitute for
personal observation.

Blind test randomization
◦ Give the parts to the operators “randomly”
◦ This ensures they don’t remember the “right” values
◦ This also protects against error due to “slop” in the gage
19

Location
◦ If possible, perform the study in the same type
of environment as the actual inspections would
occur
◦ Document anything about the location or
environment that may be affecting the study

Timing
◦ If possible, perform the study in as tight of a
time window as possible to minimize variation
(unless you are doing a stability study)
20

Key Points to Remember:
◦ BE PRESENT!!
◦ Variation is the enemy
◦ Observe & document everything you can
The more thorough you are doing the study, the more likely it is to
pass.
and…
If it does fail, the more ammunition you’ll have to fix it!
21
All within?
All the same?
Part-to-part
much larger?
At least 50% out?
22

Take the Std. Dev of the Total Measure and
multiply by 5.15 (Some people will say 6)
2.445 * 5.15 = 12.59
(This gives 99% Confidence)
2.445 * 6
= 14.67
(This gives 99.73% Confidence)
23

Documentation is everything!
◦ Add a “purpose” tab to your file that includes a
detailed write up including at least:
 The date, location, and person giving the study.
 The people, parts, and gage used.
 Detailed instructions on how the gage was used
(attach electronic setup sheet, pictures, etc).
 A write up of your final analysis along with the
rationale used.
 Include any other observation you may have had
along the way that could be used to replicate or
improve upon the study.
24

Study Complete!
◦ Pass: Save a copy of your study into an
appropriate folder within the GR&R reports area
~or~
◦ Fail: DMAIC!
25
For Attribute data enter A for
Accept and R for Reject
MSA Data Template
Date:
Part Type:
6/18/2010
USL:
LSL:
Part #
1
2
3
4
5
6
7
8
9
10
Description:
Reference Rep 1
0.65
1
0.85
0.85
0.55
1
0.95
0.85
1
0.6
1.0
0.5
Operator 1
Rep 2
Rep 3
0.6
0.6
0.95
0.95
0.8
0.8
0.95
0.9
0.45
0.5
1
1
0.95
0.95
0.8
0.85
1
1
0.65
0.65
Rep 1
0.6
0.95
0.8
0.8
0.4
1
0.95
0.75
1
0.55
Operator 2
Rep 2
Rep 3
0.6
0.65
0.95
0.95
0.75
0.75
0.75
0.75
0.4
0.45
1.05
1
0.9
0.9
0.7
0.75
0.95
0.95
0.5
0.55
Rep 1
0.6
0.95
0.8
0.8
0.45
1
0.95
0.8
1.05
0.65
Operator 3
Rep 2
Rep 3
0.65
0.6
1
1
0.8
0.85
0.8
0.8
0.5
0.45
1.05
1
0.95
0.9
0.8
0.8
1.05
1
0.6
0.6
Perform in class exercise
26





Do nothing?
Find a new gage?
Do it over?
Change our tolerance?
Do something to improve it?
Let’s try something unorthodox!
27

Define:
◦ We have an unacceptable GR&R or MSA!

Measure:
◦
◦
◦
◦
We have a GR&R of .0376 and our tolerance is .5
We have an EV of .0256 (8.8% of tolerance)
We have an AV of .0275 (9.48% of tolerance)
We are using 45.12% of tolerance and we know we
need to be less than 30%, preferable 10%
28

Analyze:
◦ Software gave us some charts we could use
◦ Start with the charts – See similar example on
slide 22 – Do they all pass?

Improve
◦ Once you know what does not pass, then
PF/CE/CX/SOP
◦ Utilize team members in this process
◦ Treat it like a mini-Green Belt project with a
scope of “fixing the measurement system”
29

Control
◦ Revise and add detail to the original SOP to
reduce/eliminate variation identified by the team
with the PF, CE, & CNX.
◦ Officially re-perform the study and begin the
DMAIC process over again as necessary until the
study gets below 30% of tolerance, 10% preferred
30
Variable Study
Questions?
31

Much of the setup and process is the same
as with a variable study.
◦ Go watch the inspection
◦ Understand how it is being done and look for
potential sources of variation
◦ Make sure there are solid instructions in place
for usage of the gage.
The key to a successful study is to reduce all possible variation.
Eliminate as much as possible BEFORE getting started.
32

People
◦ This type of study uses 2 people instead of 3.

Parts
◦ This type of study uses 20 parts vs. 10 parts.
◦ Ideally, calibrated “masters” should be used.
◦ It is as, or more, important to cover the entire range of
tolerance in this type of study.
◦ If possible, have parts that are barely “good” and barely
“bad” for each potential failure mode.
◦ Ideally, there should be parts that represent all types of
failure modes within the sample of parts.

Gage
◦ Verify calibration and proper function.
33

Just like variable study…
◦ BE PRESENT & document everything
◦ Use randomization when evaluating the parts
◦ Do the study in the normal environment where
parts will be checked in production
◦ Try to get through all the checks in “one sitting”
if at all possible
34
MSA Data Template
For Attribute data enter A for
Accept and R for Reject
Date:
Part Type:
Description:
6/18/2010
USL:
LSL:
Part #
1
2
3
4
5
6
7
8
9
Reference
a
r
a
a
r
r
r
r
r
10
a
Operator 1
Rep 1
Rep 2
a
a
r
r
a
a
r
a
r
r
r
r
a
r
r
r
r
r
a
a
Operator 2
Rep 1
Rep 2
a
a
a
r
r
r
r
r
r
r
r
r
r
r
a
a
r
r
r
r
Operator 3
Rep 1
Rep 2
a
a
r
r
r
a
a
a
a
r
a
a
r
a
a
a
a
r
a
a
Perform in class exercise
35

Fixing an unacceptable attribute study is the
same as a variable study.
◦ Treat it like a mini-project
◦ Create PF, CE, CNX and SOP



The disadvantage is that you may not have as
clear of a direction to start from due to no EV,
AV or charts/graphs.
Look at P(FR) and P(FA) for sources of error.
Attack any sources of variation!
36
Attribute Study
Questions?
37
Observe the entire process
 Document everything you see
 Reduce or eliminate any sources of
variation
 Lock down the final process with a
detailed SOP

38