Quality Progress - December 2016 - 52


The U.S. Army Armament Research, Develop-

variables. The performance metric in this case was

ment and Engineering Center (ARDEC) chemists,

time-to-failure in a salt spray chamber, where the ob-

statisticians and engineers from arsenals in Pica-

jective was to maximize the time to failure given a

tinny, NJ, and Rock Island, IL, recently teamed up

wide variety of process variables. There were nine

to tackle a complex problem, and this led to discov-

variables (factors), and a DoE approach was used to

eries and breakthroughs in optimizing a chemical-

systematically vary the factors in the most efficient

coating process.

manner possible.

In the spirit of continuous improvement, a program manager was looking to the ARDEC to optimize

Choosing the team and approach	

a coating process applied to a family of products in

Given the technical challenges posed by this effort,

manufacturing (additional product details are classi-

the project lead, who was an experienced ARDEC

fied). The coating is intended to prevent corrosion be-

chemical engineer, selected team members with the

ing formed across various battlefield environments.

necessary skills for this task. The team included a

Characterizing chemical reactions involved in

chemist, who was a subject matter expert in corro-

corrosion formation on a product can be a daunt-

sion chemistry, and ARDEC's lead mathematical stat-

ing task because of the large number of potential

istician, who was an experienced DoE practitioner.

variables and interactive effects between these

A seemingly intuitive approach to testing still used
by some engineers involves changing one factor at a
time (OFAT) while keeping other factors at a nominal

Chemical process A temperature
and concentration vs. time-tofailure response / FIGURE 1

value and picking the best combination. The OFAT
approach has been shown to be ineffective due to its
inability to quantify interactions between inputs-often leading to incorrect results-and the increased
quantities required to run tests in this manner.

Chem. = chemical
SS TTF X = salt-spray time to failure, transformed

52 QP * www.qualityprogress.com

ed SS T
TF X

Estimat

Estimated SS TTF X

Con
cent
rocess A
ratio
chem. p 190 200 15
re
tu
n ch
ra
14
180
Tempe
em.
170
13
proc
160
12
ess
150
11
A
70
10
9
65
70
60
65
55
60
50
55
45
50
65
40
45
35
40
50
30
35
25
45
30
20
25
15
40
20
10
15
5
10
0
5
-5
0
-10
-5
Co15
-10
nc 14
200
en
tra 13
190 A
tio
180
n 12
ess
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oc
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170
em 11
.
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150
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ss 9
pe
A
m
Te

To maximize time to failure in the salt spray
chamber while accounting for all of the process variable effects and potential for interactions, DoE was
selected as the most appropriate technique. DoE is a
family of rigorous statistical test-design techniques
that yield the most accurate information about the
causal relationships between inputs (factors, or x's)
and outputs (responses, or y's) to drive product and
process improvements.2 If applied correctly, DoE
can achieve accurate results while minimizing test
quantities by leveraging projection principles inherent to the distribution of points (factor-level combinations) in the design space. Right-sizing test quantities often results in reductions in resources, costs
and schedule in executing tests and experiments.
It's important to consider that using a full-factorial design approach3 for all possible factor-level
combinations in this nine-factor experiment (two
factors had two levels each, and seven factors were
varied across three levels each) would have resulted in 22 x 37 = 8,748 total runs. Obviously, this is not
a practical approach. The challenge is to use statistical test-design principles, metrics and DoE best
practices to design a test that captures significant


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Table of Contents for the Digital Edition of Quality Progress - December 2016

Quality Progress - December 2016 - cover1
Quality Progress - December 2016 - cover2
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Quality Progress - December 2016 - cover3
Quality Progress - December 2016 - cover4
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