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Introduction to Design of Experiments (DOE)
DOE is an essential piece of the
reliability program pie. It plays an important role in Design for
Reliability (DFR) programs, allowing the simultaneous investigation of the
effects of various factors and thereby facilitating design optimization.
This article introduces the concept of DOE. Future articles will cover more
DOE fundamentals in addition to applications and discussion of DOE analyses
accomplished with a soon-to-be-introduced ReliaSoft software product!
DOE helps in:
- Identifying relationships between
cause and effect.
- Providing an understanding of
interactions among causative factors.
- Determining the levels at which to set
the controllable factors (product dimension, alternative material,
alternative designs, etc.) in order to optimize reliability.
- Minimizing experimental error (noise).
- Improving the robustness of the design
or process to variation.
Introduction
Much of our knowledge about products and processes in the engineering and
scientific disciplines is derived from experimentation. An experiment is a
series of tests conducted in a systematic manner to increase the
understanding of an existing process or to explore a new product or process.
Design of Experiments, or DOE, is a tool to develop an experimentation
strategy that maximizes learning using a minimum of resources. Design of
Experiments is widely used in many fields with broad application across all
the natural and social sciences. It is extensively used by engineers and
scientists involved in the improvement of manufacturing processes to
maximize yield and decrease variability. Often times, engineers also work on
products or processes where no scientific theory or principles are directly
applicable. Experimental design techniques become extremely important in
such situations to develop new products and processes in a cost-effective
and confident manner.
Why DOE?
With modern technological advances, products and processes are becoming
exceedingly complicated. As the cost of experimentation rises rapidly, it is
becoming impossible for the analyst, who is already constrained by resources
and time, to investigate the numerous factors that affect these complex
processes using trial and error methods. Instead, a technique is needed that
identifies the "vital few" factors in the most efficient manner and then
directs the process to its best setting to meet the ever-increasing demand
for improved quality and increased productivity. The techniques of DOE
provide powerful and efficient methods to achieve these objectives. Designed
experiments are much more efficient than one-factor-at-a-time experiments,
which involve changing a single factor at a time to study the effect of the
factor on the product or process. While the one-factor-at-a-time experiments
are easy to understand, they do not allow the investigation of how a factor
affects a product or process in the presence of other factors. When the
effect that a factor has on the product or process is altered due to the
presence of one or more other factors, that relationship is called an
interaction. Many times the interaction effects are more important than the
effects of individual factors. This is because the application environment
of the product or process includes the presence of many of the factors
together instead of isolated occurrences of single factors at different
times. Consider an example of interaction between two factors in a chemical
process where increasing the temperature alone increases the yield slightly
while increasing pressure alone has no effect on the yield. However, in the
presence of both higher temperature and higher pressure, the yield increases
rapidly. Thus, an interaction is said to exist between the two factors
affecting the chemical reaction.
The methodology of DOE ensures that all
factors and their interactions are systematically investigated; thus,
information obtained from a DOE analysis is much more reliable and complete
than results from one-factor-at-a-time experiments that ignore interactions
and may lead to misleading conclusions.
Stages of DOE
Designed experiments are usually carried out in five stages planning,
screening, optimization, robustness testing and verification.
1. Planning
It is important to carefully plan for the course of experimentation before
embarking upon the process of testing and data collection. A few of the
considerations to keep in mind at this stage are a thorough and precise
objective identifying the need to conduct the investigation, assessment of
time and resources available to achieve the objective and integration of
prior knowledge to the experimentation procedure. A team composed of
individuals from different disciplines related to the product or process
should be used to identify possible factors to investigate and the most
appropriate response(s) to measure. A team approach promotes synergy that
gives a richer set of factors to study and thus a more complete experiment.
Carefully planned experiments always lead to increased understanding of the
product or process. Well planned experiments are easy to execute and
analyze. Botched experiments, on the other hand, may result in data sets
that are inconclusive and may be impossible to analyze even when the best
statistical tools are available.
2. Screening
Screening experiments are used to identify the important factors that affect
the process under investigation out of the large pool of potential factors.
These experiments are carried out in conjunction with prior knowledge of the
process to eliminate unimportant factors and focus attention on the key
factors that require further detailed analyses. Screening experiments are
usually efficient designs requiring few executions, where the focus is not
on interactions but on identifying the vital few factors.
3. Optimization
Once attention has been narrowed down to the important factors affecting the
process, the next step is to determine the best setting of these factors to
achieve the desired objective. Depending on the product or process under
investigation, this objective may be to either increase yield or decrease
variability or to find settings that achieve both at the same time.
4. Robustness Testing
Once the optimal settings of the factors have been determined, it is
important to make the product or process insensitive to variations that are
likely to be experienced in the application environment. These variations
result from changes in factors that affect the process but are beyond the
control of the analyst. Such factors (e.g. humidity, ambient
temperature, variation in material, etc.) are referred to as noise or
uncontrollable factors. It is important to identify such sources of
variation and take measures to ensure that the product or process is made
insensitive (or robust) to these factors.
5. Verification
This final stage involves validation of the best settings by conducting a
few follow-up experimental runs to confirm that the process functions as
desired and all objectives are met. |
