Process Optimization via Robust Parameter Design when Categorical Noise Factors are Present

TL;DR: A new mathematical program to simultaneously optimize multiple quality characteristics in multiple stage systems using Multivariate form response surface methodology with iterative seemingly unrelated regression as the estimation method to extract the relationships between the outputs and inputs in each stage.

TL;DR: The paper discusses the use of the copula theory in the point estimate method for computing the statistical moments of a function involving random variables and shows how this approach can significantly improve the quality of the results when a nonlinear relationship exists between the parameters.

TL;DR: In this article, a new technology implemented by GE Oil & Gas called superfinishing is studied through statistical methods in order to achieve a minimization of the final roughness according to the best set of levels for the abrasive component mixture and the time process.

Abstract: because the experimenter can use standard computer software that may not have the generalized least-squares capability to fit the underlying models. Their methodology can be applied to many standard response surface designs, including the central composite design, the small composite design, equiradial designs, and the Box–Behnken design. Liang et al . 2 show how fraction of design space (FDS) plots can be extended to evaluate the properties of SPDs. The FDS plot is a graphical representation of the prediction variance from the model that has been fit to the data from the experimental design. It plots the prediction variance (or the scaled prediction variance) versus the fraction of the design space that has prediction variance values at or below a given value. These plots are very valuable in studying the potential performance of a design, and they have been completely randomized designs have been studied extensively with FDS plots and other graphical methods. Their extension to SPDs is a development that has significant practical applications in design selection. The FDS plot can provide guidance on how many WPs to use and how to allocate the available experimental resources between the WPs and the sub-plots. Software developers are planning to include FDS plots in upcoming releases of their products.

TL;DR: A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it and the importance of parameter-design principles with well-established statistical techniques.

TL;DR: Economical experimentation methods for robust design using combined arrays are presented. This approach reduces the number of experiments required compared to Taguchi's product array method.

TL;DR: Parameter design is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes as mentioned in this paper, where the assumption is that there are both controllable factors (control variables) and uncontrollable/difficult to control factors (noise variables) that operate on the quality characteristic of a process.