RELIABILITY ENGINEERING
PAPER SERIES 2020
DOE - Design of Experiments
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DOE – DESIGN OF EXPERIMENTS
Management Dilemmas
QUESTIONS?
o Why and How the things fail?
o How much a failure cost?
o How many failures will occur next year?
o How many spare parts will I need next year to supply my process?
o How can I more quickly solve the problem caused by a failure?
o What could I have done to avoid failures?
ANSWERS!
✓ You need to know the causes of failures!
✓ You need to know the probability of occurrence of failures!
✓ You need to know the consequences of failures!
✓ You need to know the behavior of failures!
✓ You need to know how to conduct a financial analysis of the failure!
✓ You need to know how to implement effective action plans!
Don´t you know how to do it?
WHY Consultoria has the answer!
DOE – DESIGN OF EXPERIMENTS 2
LDA – LIFE DATA ANALYSIS
DOE Design of Experiments
DOE training provides the concepts needed to effectively perform improvements in your process and has been
widely used to quickly identify important factors and to determine the best values of them in order to optimize the
performance of a product or process. 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 (DOE), then, is the tool to develop an experimentation strategy that maximizes learning using a minimum
of resources. DOE 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 engineers also work on products or processes where no scientific theories or
principles are directly applicable. Experimental design techniques become extremely important in such studies to
develop new products and processes in a cost effective and confident manner.
DOE Principles
The design and analysis of experiments revolves around the
understanding of the effects of different variables on another variable.
In technical terms, the objective is to establish a cause-and-effect
relationship between a number of independent variables and a
dependent variable of interest. The dependent variable, in the context
of DOE, is called the response, and the independent variables are
called factors. Experiments are run at different factor values, called
levels. Each run of an experiment involves a combination of the levels
of the investigated factors, and each of the combinations is referred to
as a treatment. Repeated observations at a given treatment are called
replicates. The number of treatments of an experiment is determined
on the basis of the number of factor levels being investigated.
DOE Types
There are several types of design, each of which has an application:
One Factor – for comparison (Anova)
Factorial – Factor screening
o General Full Factorial
o Two Level Full Factorial
o Two Level Fractional Factorial
o Plackett-Burman
o Taguchi's Orthogonal Arrays
Response Surface Method – for optimization
Robust Parameter - for Product or Process Robustness
Reliability Tests DOE – for QALT Tests
Optimal Custom - for Experiments with Constraints
DOE Stages
Designed experiments are usually carried out in five stages:
Planning
Screening
Optimization
Robustness Testing
Verification
DOE – DESIGN OF EXPERIMENTS 3
LDA – LIFE DATA ANALYSIS
DOE Statistical Background
Designed experiments needs a basic statistical concepts:
Random variables and Normal distribution
Population Mean, Sample Mean and Variance
Central Limit Theorem
Unbiased and Biased Estimators
Degrees of Freedom (dof)
Standard Normal Distribution
Chi-Squared Distribution
Student's t Distribution (t Distribution)
F Distribution
Hypothesis Testing
Two-sided and One-sided Hypotheses
Linear Regression (single and multiple)
Who Should Attend
Engineers, Technician, analysts, Production supervisors,
production lead personnel, quality engineers, manufacturing
engineers, maintenance engineers, manufacturing managers,
contract administrators, procurement personnel, procurement
specialists, project managers, project engineers, design
engineers and program managers should attend this training.
DOE Training Content
Topics covered in the training:
• DOE Goals and Scope
• DOE Definition and Application
• Basic Concepts
• Historic
• Statistics versus Experiments
• Randomization and Blocking
• Analysis steps
• DOE Stages
• DOE Types
• Statistical Concepts
• Confidence Intervals
• Hyphotesys Tests
• Linear Regression
• Analysis of Variance (ANOVA)
• One Factor Design
• Full Factorial Design
• Two Level Factorial Design
• Repetitions, Replications and Blocking
• Central Point Executions
• Curvature
• Two Level Fractional Factorial Design
• Plackett-Burman Design
• Taguchi OA Design
• Response Surface Method
• Tagushi Robust Method
• Reliability DOE
• Case Studies
DOE – DESIGN OF EXPERIMENTS 4
