Table of Contents
- 1 Response Surface Methods for Peak Performance
Response Surface Methods for Peak Performance
Enhance your products and processes using response surface designs. Acquire advanced data analysis skills to maximize insights from your experiments. Model your processes using the full capabilities of classic and optimal designs. Contact us for the Design Expert training.
This course on Response Surface Methods is intermediate-level and includes the following topics::
- Master central composite, Box-Behnken, and custom designs
- Employ top techniques for RSM design analysis
- Enhance predictions via model reduction
- Expand design space with applied constraints
- Incorporate discrete numeric factors
- Utilize numerical and graphical tools for optimization.
- Knowledge of basic statistics (mean and standard deviation), and exposure to simple comparative experiments (e.g. two-sample t-test) are required.
- Working knowledge of Design-Expert software and Two-Level Factorial Design is expected.
Building Central Composite Designs
The most popular response surface method (RSM) design is the central composite design (CCD).
- Central composite designs utilize 5 factor levels: -Alpha, -1, 0, 1, +Alpha
- Notable trait: Sequential experimentation supported by design structure
- Central composite experiments can be conducted in blocks.
Analyzing Response Surface Designs
Apply Analyze Response Surface Design to capture data curvature and determine optimal settings.
- Typically used post-factorial or fractional factorial experiments.
- Comes after identifying key process factors.
Numerical and Graphical Optimization
The optimization module seeks a blend of factor levels that fulfill criteria for both responses and factors concurrently.
- In numerical optimization, models are employed to explore the factor space, aiming for optimal compromises across multiple objectives.
- Graphical optimization employs models to visually depict the area in which acceptable response outcomes are attainable.
Building Box-Behnken Designs
Box-Behnken designs are valuable when curvature modeling is essential, as they often involve fewer runs compared to central composite designs while maintaining the same number of factors.
- Box-Behnken designs consist of three levels for each factor, tailored for quadratic models.
- They lack extreme factor combinations, offering more precise predictions at the factor space center.
- Few runs might be unreliable, emphasizing accuracy of remaining observations for model dependability.
- Possible inclusion of categorical factors, but design replication required for each category combination.
Building Optimal (custom) Designs
Optimal (Custom) designs are employed when the experiment necessitates adjustments that go beyond the capabilities of a standard design. These adjustments include:
- Variation among mixture components not uniform.
- Mixture and process variables coexist in design.
- Dual independent mixtures share one design.
- Constraints supplement factor limits.
- Expanding beyond full quadratic models.
- Standard designs entail excessive runs.
- Wide distribution of replicates across design.
- Blend of the mentioned factors.
Multiple Linear Constraints
Multiple Linear Constraints is a technique used to impose restrictions on the factors and responses in experimental designs.
- Basic bounds insufficient for defining feasible experiment area.
- Design-Expert enables linear multi-factor constraints.
- Applicable to response surface or mixture designs.
- Offers flexibility in experimental region definition.
Including Categoric or Discrete Numeric Factors
Categoric or Discrete Numeric Factors are variables that can take on distinct values or categories, rather than continuous ranges. These factors are integral to experimental designs and response surface methodologies, where their levels are selected deliberately to study their impact on the response variables of interest.
- Variables with distinct values or categories
- Part of experimental designs and response surface methodologies
- Chosen levels examine their effect on response variables