Taguchi’s approach to Design of Experiments (DOE) is a very broad subject, and we won’t be able to cover everything. We will focus on Taguchi DOE as a special type of fractional 2^k design of experiments (although we will also consider 3^k, 4^k, and 5^k designs).
One of the goals of the Taguchi approach is to optimize the early stages of product or system design, especially by reducing the number of experiments or trials that need to be conducted. Here the goal is to extract as much information as possible for the least cost (in terms of time, resources, and money). The approach uses a small number of standard orthogonal arrays and fits the design to one of these arrays.
Topics
- 2-level design basics
- Design optimization
- List of 2-level designs and interactions
- Real Statistics support
- Design with replications
- Signal-to-noise ratio
- 3-level designs
- 3-level examples
- 4-level designs
- Interactions for 4-level designs
- 5-level designs
References
Roy, R. K. (2010) A primer on Taguchi method. 2nd ed. Society of Manufacturing Engineers
https://brharnetc.edu.in/br/wp-content/uploads/2018/11/11.pdf
University of York (2004) Orthogonal arrays (Taguchi designs)
https://www.york.ac.uk/depts/maths/tables/orthogonal.htm
Minitab (2024) Catalogue of Taguchi designs
https://support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/doe/supporting-topics/taguchi-designs/catalogue-of-taguchi-designs/
Kacker, R. N., Lagergren, E. S. and Filliben, J. J. (1991) Taguch’s orthogonal arrays are classical designs of experiments. J. Res. Natl. Inst. Stand. Technol. 96, 577
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927234/pdf/jresv96n5p577_a1b.pdf