Design of Experiments Training
DOE Design of Experiments
Experimental Design
Introduction:
Design of Experiments (DOE), also known as Statistical Experimental Planning, is a key Six Sigma tool. It is a methodology that can be effective for general problem-solving, as well as for improving or optimizing product design and manufacturing processes. Specific applications of DOE include identifying proper design dimensions and tolerances, achieving robust designs, generating predictive math models that describe physical system behavior, and determining ideal manufacturing settings.
This competency-based course utilizes hands-on activities to help participants learn the criteria for running a DOE, the requirements and pre-work necessary prior to DOE execution, and how to select the appropriate designed experiment type to run. Participants will experience setting up, running, and analyzing the results of simple-to-intermediate complexity, Full Factorial, Partial Factorial, and Response Surface experiments utilizing manual methods as well as a hands-on computer tool that facilitates experimental design and data analysis. Participants will also receive an overview of Robust DOE, including the Taguchi DOE Method. The course will also include the use of the Minitab / JMP software tool for analyzing data.
Duration: 4 Days
Target Audience:
Quality Managers, Quality Engineers, Manufacturing Engineers, Production Engineers, Project Engineers and Design Engineers.
Softwares for DOE Training:
The following software will be used for the training ( choose 1 only):
Minitab DOE Training
JMP DOE Training
Note:
1. JMP is a suite of computer programs for statistical analysis developed by the JMP business unit of SAS Institute.
2. Minitab is a statistics package developed at the Pennsylvania State University by researchers Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiner.
(Source: wikipedia.org)
Programme Objectives:
This course will enable participants to be able to:
- Decide whether to run a DOE to solve a problem or optimize a system
- Set-Up a Full Factorial DOE Test Matrix, in both Randomized and Blocked forms
- Analyze and Interpret Full Factorial DOE Results using ANOVA, (when relevant) Regression, and Graphical methods
- Set-Up a Fractional (Partial) Factorial DOE, using the Confounding Principle
- Analyze and Interpret the results of a Fractional Factorial DOE
- Recognize the main principles and benefits of Robust Design DOE
- Decide when a Response Surface DOE should be run
- Select the appropriate Response Surface Design (either Plackett-Burman, Box-Behnken, Central Composite, or D-Optimal)
- Interpret Response Surface Outputs
- Utilize the Minitab / JMP Software tool to analyze data
Programme Outline:
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What is DOE?
- Brief History
- Types of Designed Experiments
- Application Examples
- Where DOE Fits in with Other Tools/Methods
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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
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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
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DOE Statistical Analysis
- ANOVA Principles for Simple Full Factorial Experiments
- Analysis Plots
- Regression Analysis of Simple Full Factorial Experiments
- Using Minitab / JMP 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 / JMP for Fractional Factorial Experiments
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Robust Design Experiments
- What is Robustness?
- Control and Noise Factors
- Classical and Taguchi Robust DOE Set-Up
- Robustness Metrics
- Analytical and Graphical Output Interpretation
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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
- Using Minitab / JMP for response Surface Experiments
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FAQ
1. Who is the target audience of Design of Experiments (DOE) training?
The target audience for the training is:
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- Engineers and Technologists: DOE is widely used in engineering and technology-related fields, including mechanical, electrical, and industrial engineering, as well as manufacturing, quality control, and product development.
- Scientists and Researchers: DOE is used in a variety of scientific fields, including biology, chemistry, and physics, to investigate the relationship between variables and to optimize processes and products.
- Quality and Process Improvement Specialists: DOE is an important tool for continuous improvement, and individuals involved in quality control, process optimization, and production management may benefit from DOE training.
- Data Analysts and Statisticians: DOE requires a strong understanding of statistical methods, and individuals involved in data analysis, statistical modeling, and research may benefit from DOE training.
- Project Managers: Project managers who are involved in product development, process improvement, and problem-solving may benefit from DOE training, as it provides a structured approach to decision-making and problem-solving.
Overall, DOE training is suitable for individuals who are involved in process improvement, product development, and decision-making, and who have a background in engineering, science, statistics, or quality control. The training may also be of interest to individuals who are looking to develop their problem-solving and decision-making skills.
2. What are the considerations of Design of Experiments implementation?
When implementing a Design of Experiments (DOE) study, several considerations should be taken into account, including:
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- Objectives and Scope: It is important to clearly define the objectives of the study and the scope of the experiment, including the variables to be studied and the desired outcome.
- Experimental Design: The choice of experimental design, such as factorial design, response surface methodology, or central composite design, should be based on the objectives of the study and the type of system being studied.
- Sample Size: The sample size should be large enough to provide reliable results but not so large as to be impractical or wasteful. The sample size will also depend on the number of variables being studied and the desired level of precision.
- Selection of Factors and Levels: The variables to be studied and the levels at which they will be tested should be selected carefully, taking into account the available resources and the complexity of the system being studied.
- Data Collection and Analysis: The data collected during the experiment should be accurate, precise, and relevant to the objectives of the study. The data should be analyzed using appropriate statistical methods, such as regression analysis or analysis of variance, to determine the relationship between the variables and the response.
- Control and Randomization: The experiments should be designed with appropriate control and randomization to minimize the effect of extraneous variables and to ensure the validity of the results.
- Replication and Validation: The results of the experiment should be replicated and validated using additional trials or simulations to ensure their reliability and generalizability.
- Interpretation of Results: The results of the experiment should be carefully interpreted, taking into account the limitations and assumptions of the study, and used to make informed decisions about the process or product being studied.
Overall, the implementation of a DOE study requires careful planning, attention to detail, and a sound understanding of statistical methods and experimental design. By taking these considerations into account, it is possible to conduct a DOE study that provides valuable information and supports data-driven decision-making.