Central Composite Designs (CCD)

Introduction: CCD

Central Composite Designs (CCD) are advanced experimental designs used in the Design of Experiments (DOE) methodology. They are particularly effective for modelling and optimising processes where non-linear relationships exist between independent variables and outcomes. By capturing both linear and quadratic effects, CCDs provide a deeper understanding of complex process behaviour.

Background

Developed as an extension of factorial and fractional factorial designs, CCDs expand the experimental space to allow for detection of curvature in response surfaces. This makes them a cornerstone of Response Surface Methodology (RSM), widely used in process optimisation across scientific and industrial fields.

Key Elements / Features

• Factorial or Fractional Factorial Base: Forms the experimental “cube” representing combinations of factors.
• Axial (Star) Points: Placed outside the base design to explore variable extremes.
• Centre Points: Multiple replicates at mid-levels to estimate experimental error and curvature.
• Quadratic Modelling: Designed to capture non-linear effects and interactions between factors.

Applications / Examples

• Chemistry: Optimising reaction conditions such as temperature, pressure, and concentration.
• Pharmaceuticals: Improving drug formulation and stability.
• Food Technology: Refining recipes and processing conditions for quality and safety.
• Manufacturing: Enhancing production efficiency and reducing variability.

Relevance / Impact

Central Composite Designs help organisations and researchers to:

• Reduce the number of experiments compared to full factorial designs.
• Detect and model complex, non-linear relationships.
• Identify optimal process settings for improved quality and performance.
• Support data-driven decision-making in research and industrial practice.

See also

• Design of Experiments (DOE)
• Response Surface Methodology (RSM)
• Factorial Design
• Process Improvement