How to use Cornerstone’s DoE module and regression to perform an optimization on a factor

In many designed experiments, factors with a known high influence on the response(s) are varied. As an example, the factor throughput of a combine harvester is used. The response considered are the loss of grain material or its quality. Other factors are adjustments to the machine and / or technical variants which usually have only a smaller effect on the response compared to the dominating factor throughput. In Cornerstone, regression models can be used to define targets for response variables that are to be achieved by varying the factors. In the example considered here, however, the factor throughput is to be maximized for a defined level of the response variable. Levers for this goal are the remaining factors.

This article shows how to use Cornerstone’s DoE module and regression to perform an optimization on a factor. The method makes it possible to execute the design in its natural way and not to force a factor into a response role during the runs: the problem does not have to be adapted to the software but the software is flexible enough to adequately describe the problem. The idea is to fold the factor from the x axis to the y axis. The present combine harvester example suggests that the relevant dominant factor is added to the design in a full factorial arrangement. This is always a good recommendation if this is possible with relatively little effort. It has been shown how this can also be easily implemented in Cornerstone. The added value of the full-factorial extension is that the process model is particularly stable for the added factor and will benefit by tight confidence intervals.

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