Design of experiments
Design of experiments (DOE) is defined as a branch of applied statistics that deals with planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that control the value of a parameter or group of parameters. DOE is a powerful data collection and analysis tool that can be used in a variety of experimental situations.
It allows for multiple input factors to be manipulated, determining their effect on a desired output (response). By manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed when experimenting with one factor at a time. All possible combinations can be investigated (full factorial) or only a portion of the possible combinations (fractional factorial).
A strategically planned and executed experiment may provide a great deal of information about the effect on a response variable due to one or more factors. Many experiments involve holding certain factors constant and altering the levels of another variable. This "one factor at a time" (OFAT) approach to process knowledge is, however, inefficient when compared with changing factor levels simultaneously.
A systematic approach to planning for a designed industrial experiment
David E. Coleman
Alcoa Laboratories
Alcoa Center, A 15069
Douglas C. Montgomery
Industrial Engineering Department
Arizona State University
Tempe, AZ 85287
Design of experiments and analysis of data from designed experiments are well-established
methodologies in which statisticians are formally trained. Another critical and rarely taught
skill is the planning that precedes designing an experiment. This article suggests a set of tools
for presenting generic technical issues and experimental features found in industrial experi-
ments. These tools are predesign experiment guide sheets to systematize the planning process
and to produce organized written documentation. They also help experimenters discuss com-
plex trade-offs between practical limitations and statistical preferences in the experiment. A
case study involving the (computer numerical control) CNC-machining of jet engine impellers
is included.
TECHNOMETRICS, FEBRUARY 1993, VOL. 35, NO. 1
David E. Coleman
Alcoa Laboratories
Alcoa Center, A 15069
Douglas C. Montgomery
Industrial Engineering Department
Arizona State University
Tempe, AZ 85287
Design of experiments and analysis of data from designed experiments are well-established
methodologies in which statisticians are formally trained. Another critical and rarely taught
skill is the planning that precedes designing an experiment. This article suggests a set of tools
for presenting generic technical issues and experimental features found in industrial experi-
ments. These tools are predesign experiment guide sheets to systematize the planning process
and to produce organized written documentation. They also help experimenters discuss com-
plex trade-offs between practical limitations and statistical preferences in the experiment. A
case study involving the (computer numerical control) CNC-machining of jet engine impellers
is included.
TECHNOMETRICS, FEBRUARY 1993, VOL. 35, NO. 1
A paper with application of DOE
Screen Printing Process Design of Experiments for Fine Line Printing of Thick Film Ceramic Substrates
Jianbiao Pan, Gregory L. Tonkay, Alejandro Quintero
Lehigh University
Dept. of Industrial and Manufacturing Systems Engineering
200 W. Packer Ave
Bethlehem, PA 18015, USA
Ud. 13.3.2026
Pub. 29.6.2013
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