Central Composite Design Calculator

Here you can easily generat a Central composite design online. Simply select the number of factors, the number of replications and the number of center points. The Central composite design Generator will then create your test plan.

Central composite design Generator

A Central Composite Design (CCD) is a statistical technique used in the field of design of experiments (DoE) for building a second order (quadratic) model for the response variable without needing to use a complete three-level factorial experiment. This method is part of response surface methodology (RSM) and is particularly useful for optimizing and exploring the relationships between several independent variables and one or more response variables. Key aspects of Central Composite Design include:

Structure of the Design

• Factorial or Fractional Factorial Design: The core of a CCD is a factorial or fractional factorial design, which explores all possible combinations of the factors at two levels.
• Centre Points: These are runs where all factors are set at their mid-levels. The inclusion of center points helps in detecting curvature in the response surface and provides an estimate of experimental error.
• Axial Points (Star Points): These are runs added to the factorial or fractional factorial points and are set at some distance (usually denoted as ±α) from the center in the direction of each axis. The axial points allow for estimation of curvature in the response surface.

Types of CCD

• Circumscribed (CCC): The original design, where the axial points are at a greater distance from the center than the factorial points, extending the design space.
• Inscribed (CCI): The factorial points are at the vertices of the cube inscribed within the sphere of the axial points.
• Face-Centered (CCF): The axial points are located on the faces of the factorial cube, making α equal to 1.

Application and Flexibility

CCD is highly flexible and can be adapted to many different types of experimental situations. It's widely used in process optimization experiments where several input variables potentially influence some performance measure or quality characteristic of the product or process.

Advantages

• CCD is efficient in terms of the number of experimental runs needed compared to full factorial designs.
• It allows for estimation of interaction effects and quadratic terms, which are essential for building a comprehensive model of the response surface.

Analysis

The data from a CCD is analyzed by regression methods to fit a quadratic model, which then can be used to find the optimal settings of the process variables for desired responses.

CCD is particularly favored in industrial and scientific research where optimizing processes and product formulations are crucial and where the relationships between variables and responses are not linear. For sure with DATAtab you can also create a Full Factorial Design online.