Public Program
DESIGN OF EXPERIMENT (DOE)
Course Introduction
Design of experiments, also called experimental design, is a structured and organized way of conducting and analyzing controlled tests to evaluate the factors that are affecting a response variable. Experimental design and optimization are tools that are used to systematically examine different types of problems that arise within, e.g., research, development and production. It is obvious that if experiments are performed randomly the result obtained will also be random. Therefore, it is a necessity to plan the experiments in such a way that the interesting information will be obtained.
In an experiment, we deliberately change one or more process variables (or factors) in order to observe the effect the changes have on one or more response variables. The (statistical) design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions.
Course objective
This program is designed to enable participants to learn the fundamental theory about Design of Experiment, and to use it in a practical situation such as Research and Development, Process Optimization and other possible applications.
Here are the learning objectives for the two days training program; after completing this program, participants will be able to:
– Understand the methodology of design of experiments
– Plan a design of experiment
– Explain the importance of each concept used in design of experiments
– Recognize variables in an experiment and how they interact
– Understand how to create and use an analysis of variance (ANOVA) table
– Understand how to conduct and analyze the results of a contrast test
– Identify the advantages, disadvantages, assumptions and hypotheses related to various types of designs, including completely randomized design, completely randomized block design, Latin Square design, and factorial designs
– Analyze the results of designed experiments
Course Content
DAY 1
INTRODUCTION
- a)Introduction to DOE
Full Factorial Design
- a)One Factor at A Time
- b)Coding Factors
- c)Factorial Design
- d)Calculating Main and Interaction Effects
- e)Creating Full Factorial Design
- f)Analyzing DOE results
- g)Plotting Main and Interaction Effects Plot
- h)Replication, Centre Point and Blocking
- i)Model Reduction.
- j)Limitation of Full Factorial Design
Activity
- a) DOE catapult/Helicopter simulation Workshop
DAY 2
Introduction.
- a)Why Fractional Factorial Design?
Fractional Factorial Design
- a)Screening Design
- b)Fractional Factorial Design
- c)Alias Relationship and Confounding Factors
- d)Design Resolution
- e)Fold Over Design
- f)Sample size determination for 2 level factorial Design
DOE for Variance Reduction and Response Optimizer
- a)DOE for variance reduction
- b)Creating and Analyzing DOE for Variance
- c)Response Optimizer Introduction
- d)Response Optimizer Strategy
- e)Multiple Response Optimizer
Activity
- a) DOE catapult/Helicopter simulation Workshop
- 12 & 13 March 2025
- WED & THU
- 9AM - 5PM
- Dorsett Grand Subang Hotel, Selangor
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