Engineers at a major medical device manufacturer used RSM to successfully model a key process for their flagship product. The RSM model then became the foundation for development of robust specifications to ensure quality at six sigma levels.
A custom rubber molder used DOE to uncover a combination of material selection and manufacturing protocol that created unacceptable results. Armed with this process knowledge, they achieved breakthrough quality improvements.
This article demonstrates how to uncover "sweet spots" where multiple fab-process specifications can be met in a most desirable way.
In this mini-paper, Mark Anderson details an in-class experiment illustrating the power of two-level factorial design. Also learn how to shoot a wicked slap shot!
This paper details the fabrication of nanoparticles as an example for showing a statistically-rigorous approach to design and analysis of pharmaceutical experiments.
This article starts with the basics on RSM before introducing two enhancements that focus on robust operating conditions: Modeling the process variance as a function of the input factors and Propagation of error(POE) transmitted from input factor variation. It discusses how to find the find the flats high plateaus for maximum yield and broad valleys that minimize defects. Proceeding from International SEMATECH Manufacturing Initiative (ISMI) Symposium on Manufacturing Effectiveness.
This is the third article of a series on design of experiments (DOE). The first publication provided tools for process breakthroughs via two-level factorial designs. The second article illustrated how to re-formulate rubbers or plastics using powerful statistical methods for mixture design and analysis. Via two case studies, the author now brings the focus back to process improvement. The key is in-depth DOE aimed at producing statistically-validated predictive models. Response maps made from these models point the way to pinnacles of process performance--sweet spots at high yield of in-specification products made at lowest possible cost.
This article deals with thorny issues that confront every experimenter, i.e., how to handle individual results that do not appear to fit with the rest of the data - damaging outliers and/or a need for transformation. The trick is to maintain a reasonable balance between two types of errors: (1) deleting data that very only due to common causes, thus introducing bias to the conclusions. (2) not detecting true outliers that occur due to special causes. Such outliers can obscure real effects or lead to false conclusions. Furthermore, an opportunity may be lost to learn about preventable causes for failure or reproducible conditions leading to break-through improvements (making discoveries more or less by accident).
This article details the advantages of design of experiments (DOE) over the OFAT (changing only one factor at a time) approach to experimentation. By varying factors at two levels each, but simultaneously rather than one at a time, experimenters can uncover important interactions.