Design of experiment algorithms for assembled products

Designing experiments to identify improvement in products that are assembled from manufactured components does not readily fit into conventional design of experiments methods and can be costly. Efficient methods are explored for determining designs for engineering problems where some, or all, of the factors of interest are (a) not easily set to prescribed values and (b) are dependent on a combination of properties of several components. The methods involve taking a sample of each type of component, measuring the relevant features and then finding a design that specifies an optimal set of assembled products for experiment. Three examples from manufacturing industry are presented to illustrate the approach. Two different algorithms for finding designs are described, an exchange algorithm and a genetic algorithm, and a comparison of their performances is made on the three examples