Efficient Industrial Design

Design of Experiments

Safe experiments to draw important conclusions!

Design of industrial Experiments (DOE)

Design of Experiments is based on the objective of desensitizing a product's performance characteristic to variation in critical product and process design parameters. Genichi Taguchi developed the concept of "loss to society". In this concept, variability in critical design parameters will increase the loss to society, which is an expanded view of the traditional, internally oriented cost of quality. This is a quadratic relationship of increasing costs (loss to society) as these critical design parameter values vary from the desired mean value of the parameter.

To consider quality implications during design, the design process can be segmented into three stages. The first stage, system design, establishes the functionality of the product, the physical product envelope, and general specifications. The second stage, parameter design, establishes specific values for design parameters related to physical and functional specifications. It is during these first two stages that the designer has the greatest opportunity to reduce product costs through effective functional design and parameter specification. The third stage, tolerance design, establishes the acceptable tolerances around each parameter or target. This stage typically will add costs to the product through efforts to ensure compliance with the tolerances associated with product parameters.

Since an organization cannot cost-effectively inspect quality into the product, it must focus on minimizing variability in the product through product and process design and control of processes. However, some variability is uncontrollable or very difficult to control. This difficult to control variation is referred to as noise. Noise is the result of variation in materials, processes, the environment and the product's use or misuse. Products need to be designed so that they are robust - their performance is insensitive to this naturally occurring, difficult to control variation.

Design of Experiments techniques provide an approach to efficiently designing industrial experiments which will improve the understanding of the relationship between product and process parameters and the desired performance characteristic. This efficient design of experiments is based on a fractional factorial experiment, which allows an experiment to be conducted with only a fraction of all the possible experimental combinations of parameter values. Orthogonal arrays are used to aid in the design of an experiment. The orthogonal array will specify the test cases to conduct the experiment. Frequently, two orthogonal arrays are used: a design factor matrix and a noise factor matrix, the latter used to conduct the experiment is the presence of difficult to control variation so as to develop a robust design.

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