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Industrial Design of ExperimentsPowerPoint Presentation

Industrial Design of Experiments

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Industrial Design of Experiments

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Industrial Design of Experiments

STAT 321

Winona State University

Course Objectives

- ·outline the basic steps of an industrial experiment;
- ·design experiments using the concepts of randomization and blocking;
- ·design and analyze two level factorial and fractional factorial designs;
- ·contrast Taguchi's methods with classical methods;
- ·recognize examples of poor statistical statements and graphics.

- A scientific method for designing the collection of information about a phenomenon or process, and then analyzing the information to learn about relations of potentially important variables. Economy and efficiency of data collection have high priorities.

- ·Process Optimization and Problem Solving with Least Resources for Most Information.
- ·Allows Decision Making with Defined Risks.
- ·Customer Requirements --> Process Specifications by Characterizing Relationships
- ·Determine effects of variables, interactions, and a math model
- ·DOE Is a Prevention Tool for Huge Leverage Early in Design

Steps to a Good Experiment

- Define the objective of the experiment.
- Choose the right people for the team.
- Identify prior knowledge, then important factors and responses to be studied.
- Determine the measurement system.
- Design the matrix and data collection responsibilities for the experiment.
- Conduct the experiment.
- Analyze experiment results and draw conclusions.
- Verify the findings.
- Report and implement the results.

Six Sigma Methods

- Industry is training engineers, decision-makers, process owners in quality improvement methods

- Define & Measure Phase - Week 1
- Flow chart total process
- Create cause & effect diagram
- Control chart project metrics
- Estimate capability/ performance of project metrics
- Create Pareto charts
- Conduct measurement system analysis

- Create multi-vari charts
- Determine confidence intervals for key metrics
- Conduct hypothesis tests ***
- Determine variance components
- Assess correlation of variables
- Conduct regression analysis ***
- Conduct analysis of variance

- Select designed experiment (DoE) factors and levels
- Plan DoE execution
- Conduct DoE
- Implement variability reduction designs & assessments
- Consider response surface methods

- Determine control plan
- Implement control charts
- Consider short run control charts
- Consider CUSUM and moving average control charts
- Consider pre-control
- Mistake-proof processes

- 4 weeks of training
- Plus, save your company $100K on an improvement project