Nanomanufacturing promises to increase quality, productivity and efficiency of existing technologies and has the potential to accelerate commercialisation of products and benefit various industries. As this technology is still in its discovery stage, there is a tremendous amount of experimentation occurring every day. Often in a nanomanufacturing setup, a large number of factors can be listed as possible sources of effects and among those, only a few are actually significant. A problem frequently encountered in nanomanufacturing is how to reduce the total number of experiments while estimating a large number of effects. Realising this challenge and the growing application of statistical techniques to discover relationships at the nanoscale, we advocate the use of supersaturated designs to nanomanufacturing settings. Here, we develop a supersaturated design, which is effective in identifying significant effects with minimal experimental runs for a process for the fabrication of ZnO nanorods, and discuss the analysis techniques.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering