Fractional Factorials for Process Development
Optimize the utilization of your assets through contemporary designs tailored for small-scale operations, leading to time and cost savings in experimentation. Shift from factorial experiments to response surface techniques for enhanced product and process optimization. Unify these approaches by acquiring proficiency in robust ANOVA analysis methods, instilling you with assurance in the outcomes of your investigations. Contact us for the Design Expert training.
During this DOE workshop, discover how to save time and resources by:
- Making the most of resource-effective fractional designs for concise screening and characterization purposes.
- Adhering to a sequential experimental approach spanning from screening and characterization to the application of response surface methodologies.
- Acquiring expertise in the optimal techniques for dissecting factorial designs.
Prerequisites:
- Foundations of Factorial DOE for Breakthroughs is a required prerequisite. This requirement could be exempted if you possess comprehension of 2-level factorial designs.
- A foundational understanding of the Design-Expert software is expected.
Topics Included:
Introduction to the Strategy of Experiments
The strategy of experiments aims to systematically explore the effects of factors while minimizing the number of experimental runs required.
- Efficiency Benefits: Reduces time, resources, and effort compared to testing all possible factor level combinations in a full factorial design.
- Strategic Planning: Crucial to meticulously plan, execute, and analyze experiments to ensure accurate conclusions.
- Informed Decisions: Findings from well-executed experiments aid in making informed choices for enhancing process development and optimization.
Designing fractional factorials and assessing aliases
Understanding aliasing is a fundamental aspect of designing fractional-factorial experiments effectively and drawing meaningful conclusions from the obtained results.
- Aliasing in fractional-factorial design: inability to estimate all effects due to fewer unique combinations than full-factorial design.
- Alias structure: determines combination of effects in the design.
- Understanding aliasing basics helps researchers choose suitable designs for experimental goals.
Characterization case study
Characterization via fractional factorial experiments is iterative.
- Researchers enhance experimental designs as process understanding grows.
- Additional runs conducted to delve deeper into specific interactions or factors.
- Characterization reveals intricate process relationships.
- Improved process control, quality, and efficiency result.
Screening case study
Screening refers to the initial phase of experimentation.
- Involves studying a subset of factors and their interactions.
- Aim: Identify the most influential factors.
- Efficient way to explore numerous factors with fewer experiments.
- Contrasts with full factorial experiments.
Minimum-run characterization design
Minimum-run characterization design refers to a specific approach where a reduced number of experimental runs are strategically planned to capture essential information about the factors and their interactions.
- Efficient data gathering method for process characterization
- Minimizes required experiments
- Selects key factor combinations
- Identifies factors with significant process impact
- Avoids full set of experiments
- Valuable with limited resources or for quick factor identification
Minimum-run screening design
Minimum-run screening design refers to a specific experimental design technique that aims to identify the most influential factors affecting a process while minimizing the number of experimental runs required.
- Used in fractional factorials for process development
- Identifies influential factors while minimizing experiments
- Ideal for resource, time, or budget constraints
- Useful when multiple potential process factors exist
Adding center points to test for non-linearity
Adding center points involves including additional experimental runs at the center point of the experimental design.
- Purpose: Assess non-linear effects of factors in process
- Center points: Experimental runs with factors at mid-levels
- Comparison: Center point results vs. linear model predictions
- Detects: Deviations from linearity
- Evaluates: Curvature of response surface
Transition to response surface designs
Here's the concept broken down into bullet points:
- Transition to response surface designs
- Follows fractional factorial experimental design
- Progresses to response surface methodology (RSM)
- Shift from initial screening to detailed analysis
- Explores interactions and optimal process conditions
- Provides deeper understanding of the process
- Aims for optimization and improved performance
