Case Studies and White Papers
Cost-Effective and Information-Efficient Robust Design for Optimizing Processes and Accomplishing Six Sigma Objectives
Standard factorial designs (one array) offer a cost-effective and information-efficient robust design alternative to parameter designs (two-array) made popular by Taguchi. This paper compares these two methods (one-array versus two-array) in depth via an industrial case study. It then discusses advanced tools for robust design that involve application of response surface methods (RSM) and measurement of propagation of error (POE).
Enhancing Solid-Phase Disk Extraction Performance with Design of Experiments
Scientists at 3M in St. Paul, Minnesota wanted a durable solid-phase extraction disk to rapidly extract analytes without common problems associated with SPE products. Using a fractional factorial design, the scientists reviewed four factors to reach their conclusions.
Power tells us the probability of rejecting the null hypothesis for an effect of a given size and helps us select an appropriate design prior to running the experiment. This paper describes a general approach for sizing effects that covers a wide variety of designs. Quality and Reliability Engineering International.
After a new hardcoat raised the protection of polycarbonate to a new level, chemists used DOE tools to survey the product and process design spaces. By modeling the critical factors for weatherability and abrasion resistance, they re-optimized their product. (This case study on DOE won the Best Paper Award at the 2001 International Coatings for Plastics Symposium.)
How to Save Runs, Yet Reveal Breakthrough Interactions, By Doing Only a Semifoldover on Medium-Resolution Screening Designs
Via case studies, this paper reviews the strategy of foldover on low-resolution (III) two-level fractional factorials and demonstrates how to reduce experimental runs by making use of semifoldover methods to augment medium-resolution (IV) designs.
Achieving Six Sigma Objectives for Variability Reduction in Formulation and Processing
Apply powerful design of experiments (DOE) tools to make your system more robust to variations in component levels and processing factors.
Mary Kay Inc., the Dallas-based global direct-selling manufacturer of skin care and beauty products, used Design-Expert software from Stat-Ease (Minneapolis, MN) to help avoid product launch delays and unnecessary rework costs. This case study on personal care packaging demonstrates the benefits of multifactor testing. Mary Kay saved over $100,000 in this one application of DOE alone.
Design Experiments that Combine Mixture Components with Process Factors
This article shows how to do a comprehensive experiment that combines mixture components with process factors in one crossed design, thus revealing interactions that would remain hidden by not combining all the variables in one study.
Energy Project Relies on DOE
A 3-year study by the National Renewable Energy Laboratory relied on design of experiments software to plan the experiments. With the aid of this powerful tool, the researchers worked to find the most cost-effective materials for solar air heaters.
PRI Automation has been meeting wafer transport traffic challenges through the manufacturing processes by using designed experiments software. The firm designed and built overhead automated material handling systems (AMHSs) that transport wafers through the manufacturing lines of Intel, Motorola, and other global semiconductor giants. PRI has succeeded partly by discovering how rapidly transport vehicles ought to travel in order to minimize transit time between process steps while increasing AMHS throughput using Design-Expert.