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Design of Experiments

The DOE (Design of Experiments) module provides a comprehensive framework for creating experimental designs that help you efficiently explore factor effects and interactions while minimizing the number of experimental runs required.

Overview

Design of Experiments is a systematic approach to understanding how different factors (variables) affect a response. Instead of changing one factor at a time, DOE allows you to study multiple factors simultaneously, revealing interactions between factors and providing more information with fewer experiments.

Key Features

  • Multiple Design Types: Support for full factorial, fractional factorial, and specialized 2^k designs
  • Flexible Factor Definition: Handle both numerical and categorical factors with custom levels
  • Advanced Options: Replication, blocking, central points, and randomization
  • Foldover Support: Resolve aliasing in fractional factorial designs
  • Automatic Encoding: Smart conversion between original factor values and coded levels

When to Use DOE

  • Process Optimization: Find optimal settings for manufacturing processes
  • Product Development: Understand how design parameters affect performance
  • Quality Improvement: Identify factors that affect product quality
  • Screening Studies: Determine which factors are most important
  • Response Surface Methodology: Build predictive models of your system

Quick Start

Here's a simple example of creating a full factorial design:

import daspi as dsp

# Define factors
temperature = dsp.Factor('Temperature', (150, 200))
pressure = dsp.Factor('Pressure', (10, 15))
catalyst = dsp.Factor('Catalyst', ('A', 'B'), is_categorical=True)

# Create design
builder = dsp.FullFactorialDesignBuilder(
    temperature, pressure, catalyst,
    replicates=2,
    shuffle=True
)

# Generate the experimental design
design = builder.build_design(corrected=False)
print(design)

This creates a 2×2×2 factorial design with 2 replicates (16 total runs), showing all possible combinations of the three factors.

Documentation Structure

The DOE module documentation is organized into the following sections:

For practical guidance and real-world examples, see the DOE User Guide.