Design of Experiments (DOE) is a method of simultaneously investigating the effects of multiple variables on an output variable or response. It is a key tool in the Six Sigma methodology because it effectively explores the cause and effect relationship between numerous process variables and the output. DOE helps to solve complex engineering problems.
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. 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.
What is DOE?
Types of Designed Experiments
Where DOE Fits in with Other Tools/Methods
DOE Requirements: Before You Can Run an Experiment
Writing Problem and Objective Statements
Ensuring DOE is the Correct Tool
Selecting Response Variable(s) and Experimental Factors
Actual vs. Surrogate Responses
Attention to Experiment Logistics
Test Set-up and Data Collection Planning
Selecting and Evaluating a Gage
Full Factorial Experiments
Introduction to Cube Plots for 3- or 4-factor 2-level Experiments
Factor Levels, Repetitions, and “Right-Sizing” the Experiment
Experiment Terms to Estimate (Main Effects and Interactions)
High-Level Significance Evaluation
DOE Statistical Analysis
ANOVA Principles for Simple Full Factorial Experiments
Regression Analysis of Simple Full Factorial Experiments
Using MiniTab ™ for Full Factorial DOE Experiments
Fractional (Partial) Factorial Experiments
The Confounding Principle
Selecting and Using Generators (Identities) to Set Up Confounding Strings
Determining Which Factor Combinations to Run
Analyzing Fractional Factorial Experiment Data
Using MiniTab ™ for Fractional Factorial Experiments.
Robust Design Experiments (Overview)
What is Robustness?
Control and Noise Factors
Classical and Taguchi Robust DOE Set-Up
Analytical and Graphical Output Interpretation
Response Surface Modeling
What Response Surface Models do BEST
Available Response Surface DOEs (Plackett-Burman, Box-Behnken, etc.)
Analyzing Response Surface Experiment Data
Methods for Finding Optimum Factor Values
The participant will have a hands-on experience on designing & interpreting the results
The participant will be capable of improving efficiency or yield, by gaining proficiency in identifying the vital few X's (inputs) that influence Y (output) and capable of studying all the possible interactions between them to find optimum process settings
The participants will be able to Recognize variables in an experiment and how they interact
The participants will learn how to create and use an Analysis of Variance (ANOVA) table.
The participants will be able to Identify the advantages, disadvantages, assumptions and hypotheses related to various types of designs and factorial designs
The participants will learn how to conduct and analyze the results of a contrast test.
Target Participants :
Senior Managers, Managers, Design Engineers, Manufacturing Engineers, R&D Professionals, Executives working in Manufacturing companies involved in designing a new product/process and solving industrial problems can attend this workshop.