Design of Experiments has created huge value through accelerating innovation in process and product development … and has saved companies billions in cost of experiments and through improved quality.
But scientists and engineers struggle to apply DoE when the key output of their experiments is a curve or a profile. How do you relate the input factors to an output response that is in the form of a trend …when it is a function of some other variable?
JMP Pro is the only software that empowers you to directly relate experimental factors to a functional response.
In this example, we need to understand, optimise and control a bead milling process to consistently produce nanoscale dispersions. So we’ve designed an experiment – a Definitive Screening Design – to vary factors that are likely to be important, including bead loading and temperature. The measured response for each run of this experiment is a profile of particle size versus time. We need to understand how the factors relate to the shape of this curve, so that we can understand how to achieve the ideal milling profile.
You can see how the factors affect our response curve. And we can achieve this by modelling our functional design of experiments data using the functional data explorer in JMP Pro. So you can learn more from your experiments, understand complex processes and find better solutions in more situations.
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