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Design of Experiments (DoE): Optimizing Products and Processes Efficiently

By Wilhelm Kleppmann Hochschule Aalen |

Experiments are done to quantify cause-and-effect relationships between adjustable parameters (inputs, factors, x) and the results (outputs, responses, y). They help to answer questions like: How do the ingredients and proportions of a mixture (inputs) influence its properties (outputs)? How do process parameters like temperature and stir-rate (inputs) influence the yield of a reaction and the purity of the end product (outputs)? Which settings offer the best compromise between conflicting aims, such as yield and purity? What range of inputs is permissible without causing out-of-specification outputs? FIGURE 1.  Shown here is the central composite design for the factors used in the example. The corners of the cube are at time 40 and 50 min, temperature 80 and 90°C and catalyst amount 2 and 3%   Design of experiments (DoE) looks at a complete experimental program, that is, a whole set of factor level combinations (individual experiments, runs) and addresses questions like: How should I distribute these runs in design space (that is, the plausible range for all factors) to gain as much information as possible? How many runs do I need to achieve the desired accuracy? In what order should I do these runs to avoid spurious…
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