Taguchirobuste

Taguchi robustness, or robust design, is a design approach that aims to reduce a product's sensitivity to uncontrollable sources of variation, such as environmental conditions or manufacturing differences. The goal is consistent performance by minimizing the impact of noise factors on the target specification.

Developed by Genichi Taguchi in the latter half of the 20th century, the method uses designed experiments to separate the effects of controllable design parameters from those of noise factors. A core element is the Taguchi loss function, which assigns a cost to deviations from a target.

Key concepts include separating control factors from noise factors, using orthogonal arrays to study many factors with relatively few runs, and employing a signal-to-noise ratio to measure robustness. Options for SNR reflect whether the target is nominal, smaller, or larger than the specification.

Practically, Taguchi robustness involves screening to identify influential factors, designing settings that minimize sensitivity to noise, and performing confirmation experiments. It distinguishes robust parameter design from tolerance design and emphasizes reducing variation rather than merely meeting a threshold.

Today, Taguchi robustness remains widely taught and applied in manufacturing and product development, though it faces critique for certain statistical assumptions and for comparisons with modern design-of-experiments approaches. It is often studied as part of broader quality engineering curricula.