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Lesson 1
Overview of Design of Experiments |
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Lesson 5
Intermediate
Analysis of Results I |
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Variation
Reduction
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Process Control vs. Process Capability
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Measurement Variation
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The Quality Loss Function
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Overview of DOE
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Calculation
of the Estimated/Predicted Process Average(s)
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Use of Analysis of Variance (ANOVA)
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Use of Static Signal-to-Noise Ratios for Measuring Variation
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Calculation of Static Signal-to-Noise (S/N) Ratios
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Lesson 2
Basic Experimental Design and Analysis I |
Lesson 6
Intermediate
Analysis of Results II |
Steps of
Basic Designs
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Use of Orthogonal Arrays (OAs)
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Calculation of Main Effects
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Plotting Main Effects on Response Graphs
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Calculation
of ANOVA Values
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Use of Parameter Design for Robustness
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Use of S/N Ratios on Repetitions and in
Parameter Design
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Use of Tuning Factors
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Use of Response Tables
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Brief Introduction to Mixture Designs and
Response Surface Methods
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Lesson 3
Basic Experimental Design and Analysis II |
Basic
Experimental Design and Analysis II
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Design of Interactions
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Calculation and Plotting of Interactions
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Confounding (Taguchi), or Aliasing (Traditional)
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Orthogonality
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Modification of OAs for Column Upgrading and Degrading
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Lesson 7
Other Topics |
Use of the
Quality Loss Function
1. Calculation of Cost Savings from Variation Reduction
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2. Tolerance DesignUse of Dynamic S/N Ratios (Dynamic Characteristics)
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Lesson 4
Intermediate
Experimental Design |
Full vs. Fractional Factorial Designs
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2- , 3-
, and 4-way Designs (with OAs
and Single Factors)
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Other Traditional Experimental Design Considerations:
Randomization, Blocking, Replication, Repetition, Resolution, and Centerpoints
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Modification of OAs with Factor Nesting (optional)
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Lesson 8
Summary and
Software-Based Review Exercises |
Review of Course
Practice with Four Factorial DOEs:
1. 1-way design with S/N ratios on repetitions
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2.
2-way design (OA X OA) parameter design with S/N ratios
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3. 2-way design (OA X outside factor)on a modified OA w/ regular analysis
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4. 2-way Dynamic S/N ratio design
Course
Summary Exercise:
2-way design (OA X outside single noise factor) with a 2-step
analysis:
1. Reduce variation, then
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2.
Center the mean
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