Design of Experiments Quality System 1 Lets get

Design of Experiments Quality System 1

Let’s get started! Lecture 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. 2

Design of Experiments Definition: • A scientific method for designing the collection of information / data about a process, and then analyzing the data / information to learn about relations of potentially important variables. Economy and efficiency of data collection have high priorities. 3

Advantages of Do. E: • · 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, main effect and as well as interactions, and a math model • · DOE Is a Prevention Tool for Huge Leverage Early in Design 4

1. Define the objective of the experiment. 2. Choose the right people for the team. 3. Identify prior knowledge, then important factors and responses to be studied. 4. Determine the measurement system. 5. Design the matrix and data collection responsibilities for the experiment. 6. Conduct the experiment. 7. Analyze experiment results and draw conclusions. 8. Verify the findings. 9. Report and implement the results. 5

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Factors, Responses and Interactions • Numeric Factors are variable inputs to a process e. g. feed rate, temperature, pressure, component concentration, knobs, levers • Categorical Factors are discrete inputs e. g. catalyst type, feed material, operator • Responses are effects of changes in factors e. g. Reaction rate increases w/ temp. • Factors that affect each other are said to 7 interact

Current Industrial Usage • Industry is training engineers, decision-makers, process owners in quality improvement methods 8

Topics in Six Sigma • Define & Measure Phase – 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 9

Six Sigma Analysis Phase • 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 10

Six Sigma Improvement Phase • Select designed experiment (Do. E) factors and levels • Plan Do. E execution • Conduct Do. E • Implement variability reduction designs & assessments • Consider response surface methods 11

Control Phase Six Sigma • • Determine control plan Implement control charts Consider short run control charts Consider CUSUM and moving average control charts • Consider pre-control • Mistake-proof processes 12

Six Sigma • Six Sigma is highly application oriented. A student cannot get Six Sigma certification through classroom knowledge-building alone. 13