Design of Experiments (DOE) full or fractional factorial designs are used to find the optimal combination of factors and levels for your process. But, it can take time and resources. The Taguchi Method is a more efficient way to accomplish the same desired outcomes.
Overview: What is the Taguchi Method?
The Taguchi Method is a statistical method, sometimes referred to as a robust design method, developed by the Japanese engineer and statistician Dr. Genichi Taguchi.
Taguchi developed his method for designing experiments to investigate how different process factors will affect the mean and variance of a process performance characteristic. The experimental design proposed by Taguchi involves using orthogonal arrays to organize the factors affecting the process and the levels at which they should be set. Instead of having to test all possible combinations like the factorial design, the Taguchi method tests pairs of combinations.
This method allows for a reduced number of experimental runs to identify which factors are significant. This reduces the time and resources needed to identify the most optimal factors and levels.The Taguchi method is best used when there is a reasonable number of factors (3 to 50), few lower order interactions between factors, and when there are only a few statistically significant factors.
Here is a comparison of the Taguchi method and traditional DOE:
- Only the main effects and two way interactions are considered in the Taguchi method. Higher order interactions are not considered.
- With Taguchi, possible significant interactions must be identified before doing the experiment using one’s best judgment.
- The Taguchi orthogonal arrays are based on judgment sampling and are not randomly generated as with runs for a traditional DOE.
- With a traditional DOE, noise is treated as a nuisance variable and should be blocked out. Taguchi treats noise as a major focus of analysis.
An industry example of the Taguchi Method
Here are two examples showing the application of Taguchi to two situations.
An agricultural engineer studies the effect of five factors on the growth of basil plants. The engineer designs a 2-level Taguchi experiment to determine which factor settings increase the plant’s rate of growth without increasing the variability in growth. The engineer also manipulates two noise factors to determine which settings for the five factors increase plant growth across the true range of temperature and humidity conditions.
An engineer for a golf equipment manufacturer wants to design a new golf ball to maximize ball flight distance. The engineer has identified four control factors (core material, core diameter, number of dimples, and cover thickness) and one noise factor (type of golf club). Each control factor has 2 levels. The noise factor is two types of golf clubs: driver and a 5-iron. The engineer measures flight distance for each club type, and records the data in two noise factor columns in the worksheet.
Frequently Asked Questions (FAQ) about the Taguchi Method
What is the Taguchi Method used for?
The Taguchi method is used to optimize design factors to first minimize variation before optimizing the process factors and levels to achieve target values of the mean for output characteristics.
What is a Taguchi array?
A Taguchi orthogonal array is a type of fractional factorial DOE. It is a highly fractional orthogonal design based on a design matrix proposed by Dr. Genichi Taguchi. It allows the experimenter to consider a selected subset of combinations of multiple factors at multiple levels. It usually requires less time and resources than traditional DOE designs.
What are some of the differences between a Taguchi design and traditional DOE?
With Taguchi designs, the experimenter must select the most likely interactions before the experiment. With a traditional fractional factorial design, the interactions are selected after the results from the experiment have been analyzed. Another difference is in the treatment of experimental noise. Traditional DOE designs attempt to block out the noise while Taguchi believes noise is an important part of understanding the experiment.