Free Online Robust Design (Taguchi Method) Tool

Plan and analyze robust experiments using Orthogonal Arrays to optimize product and process quality.

What is the Taguchi Method?

The Taguchi Method, developed by Japanese engineer and statistician Genichi Taguchi, is a systematic approach to improving product and process quality through robust design. Unlike traditional experimental design methods that focus primarily on optimizing mean performance, the Taguchi Method emphasizes minimizing the effects of uncontrollable factors (noise) to create products and processes that perform consistently under varying conditions.

The core principles of the Taguchi Method include:

Robust Design: Designing products and processes that are insensitive to variations in manufacturing, environmental conditions, and component aging.
Quality Loss Function: Quantifying the economic loss when a product deviates from its target value, even if it remains within specification limits.
Orthogonal Arrays: Using specially designed fractional factorial experiments that allow testing multiple factors simultaneously with a minimal number of experimental runs.
Signal-to-Noise (S/N) Ratios: Metrics that simultaneously consider both the mean performance and variability, helping identify factor settings that maximize performance while minimizing variation.

Brief History of the Taguchi Method

The Taguchi Method was developed by Genichi Taguchi in the 1950s while he was working at the Electrical Communications Laboratory (ECL) of Nippon Telegraph and Telephone (NTT) in Japan. His early work focused on improving telephone switching systems, where he recognized that traditional quality control methods were insufficient for preventing quality loss throughout the product lifecycle.

Taguchi's key insight was that quality should be measured by the deviation from a target value, not just by conformance to specification limits. He introduced the concept of the Quality Loss Function, which mathematically represents the economic loss incurred when a product characteristic deviates from its ideal value.

How to Use This Tool

Go to the Setup Experiment tab.
Load Data or Start Fresh: Use "Load Example Data" for a quick demo or fill in the setup fields yourself.
Experiment Setup: Give your experiment a name and objective.
Define Factors and Levels: Add factors (variables) and their levels (settings).
Generate Design: Click "Generate Taguchi Design". This will take you to the Design & Data tab.
Enter Data: Enter your measured results for each run.
Calculate: Go to the Analysis & Report tab and click "Calculate Full Analysis".

Experiment Setup

Experiment Name:

Experiment Objective:

Factors and Levels

Response Variable:

Select Taguchi Array

Signal-to-Noise Ratio Type

Smaller is better Larger is better Nominal is best

Design & Data Entry

Enter your measured results in the rightmost column.

Analysis & Report

No analysis generated yet. Please enter data in the "Design & Data" tab and then click the button below to calculate.

Mathematical Formulas

Signal-to-Noise Ratio (Smaller is Better)

$$ S/N = -10 \log_{10} \left( \frac{1}{n} \sum_{i=1}^{n} y_i^2 \right) $$

Where $ y_i $ are the measured responses and $ n $ is the number of observations.

Signal-to-Noise Ratio (Larger is Better)

$$ S/N = -10 \log_{10} \left( \frac{1}{n} \sum_{i=1}^{n} \frac{1}{y_i^2} \right) $$

Where $ y_i $ are the measured responses and $ n $ is the number of observations.

Signal-to-Noise Ratio (Nominal is Best)

$$ S/N = -10 \log_{10} \left( \frac{1}{n} \sum_{i=1}^{n} (y_i - T)^2 \right) $$

Where $ T $ is the target value, $ y_i $ are the measured responses, and $ n $ is the number of observations.

Mean Response

$$ \bar{y} = \frac{1}{n} \sum_{i=1}^{n} y_i $$

Where $ y_i $ are the measured responses and $ n $ is the number of observations.

Main Effect

$$ ME = \frac{\sum \text{Responses at Level } i}{\text{Number of observations at Level } i} $$

Where $ i $ represents a specific factor level.

ANOVA Sum of Squares

$$ SS = \sum_{i=1}^{k} n_i (\bar{y}_i - \bar{y})^2 $$

Where $ n_i $ is the number of observations at level $ i $, $ \bar{y}_i $ is the mean at level $ i $, and $ \bar{y} $ is the overall mean.