Case Studies and White Papers
In many rubber and plastics processes, powerful interactions affect final performance. These remain undiscovered via traditional one-factor-at-a-time scientific methods. Multifactor design of experiments (DOE) reveals these interactions that lead to breakthrough improvements in process efficiency and product quality. The big gains come from a very simple form of DOE called two-level factorial design. This approach to experimentation has proven to be especially helpful for control of part shrinkage as demonstrated in a case study. However, it can be applied to any measurable response in rubber and plastics production. This primer provides the essential details on two-level factorial DOE from an engineering perspective with an emphasis on the practical aspects.
Software Sleuth Solves Engineering Problems
Engineers at an aluminum-casting company were struggling to understand why a particular part came off the line filled with inclusions. Having conducted many one-factor-at-a-time tests to no avail, they turned to statistical software and a process called design of experiments. Optimizing based on this process let the engineers reduce the defect rate to zero.
Optimize Your Process-Optimization Efforts
What would you do it confronted with an "opportunity" to make a major change, involving many factors, but you need to do it quickly? The traditional approach to experimentation requires you to change only one factor at a time (OFAT). However, the OFAT approach doesn’t provide data on interactions of factors, a likely occurrence with chemical processes. An alternative approach called “two-level factorial design” can uncover critical interactions. This statistically based method involves simultaneous adjustment of experimental factors at only two levels, offering a parallel testing scheme that’s much more efficient than the serial approach of OFAT.
Talk by Pat Whitcomb and Mark Anderson that was presented at the 50th Annual Quality Congress.
Applying DOE to Microwave Popcorn
Design of experiments identifies which factors matter and which ones don't when microwaving popcorn, as well as helping find optimal settings.
A Simple Comparative Experiment with Paper Clips
Details and demonstrates a fun experiment to do at home or in class to build understanding of variation and how it can be handled with simple comparative designs. For teaching purposes it works best if each student breaks two brands of clips, thus providing data for a paired t-test, which blocks out variability due to the tester.
An updated version of paper-clip experiment is provided in the June 2009 Stat-Teaser posted at https://cdnm.statease.com/news/news0906.pdf.