3 - Understanding key interactions in processes

Abstract

Publisher Summary This chapter illustrates the significance of interactions in industrial processes and how they should be dealt with. To study and analyze interactions among the process or design parameters, one has to vary them at their respective levels simultaneously. To understand the presence of interaction between two process parameters, it is encouraged to employ a simple and powerful graphical tool called interaction graph or plot. If the lines in the plot are parallel, it implies no interaction between the process parameters. In contrast, nonparallel lines are an indication of the presence of interaction. The effects of process parameters can be either fixed or random. Fixed process parameter effects occur when the process parameter levels included in the experiment are controllable and specifically chosen because they are the only ones for which inferences are desired. In contrast, random process parameter effects are associated with those parameters whose levels are randomly chosen from a large population of possible levels. Inferences are not usually desired on the specific parameter levels included in an experiment, but on the population of levels represented by those in the experiment. The discussion also presents two scenarios for better and rapid understanding of how to interpret interactions in industrial experiments.