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?
- Brief History
- Types of Designed Experiments
- Application Examples
- 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
- Experiment Set-Up
- 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
- Analysis Plots
- 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.