A major Tier 1 automotive supplier wanted to improve the already high level of quality in their manufacturing process welding nuts to a metal seat frame. They contacted Middle Tennessee State University (MTSU) and requested a design of experiments (DOE) study to determine root causes of torque failures and areas of improvement.
This article demonstrates how to uncover "sweet spots" where multiple fab-process specifications can be met in a most desirable way.
Design of experiments was used for a series of computer simulations to design a new generation of muzzle brakes. DOE saves time by reducing the amount of simulations required and makes it possible to optimize the design with a higher level of certainty.
Mixture DOE catalyzed development of a primer-paint remover that met the requirements of an aircraft manufacturer for environmental safety and speed. The new formulation took only 2 hours to strip off coatings, a huge improvement over competitive products that needed over 8 hours.
MannKind Corporation used designed experiments to identify and optimize critical process variables involved in producing a small molecule substrate for use in pulmonary drug delivery.
This paper details the fabrication of nanoparticles as an example for showing a statistically-rigorous approach to design and analysis of pharmaceutical experiments.
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!
Researchers successfully used DOE to evaluate potential methods for removing three common endocrine disrupters. The researchers treated solutions containing the EDCs nonlyphenol (NP), bisphenol A (BPA) and triclosan (TCS) with an enzyme preparation from the white rot fungus Coriolopsis polyzona.
This presentation in Italian details a DOE case study on a filter cigarette packaging machine design using Design-Expert software.
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.