in a robust parameter design (rpd) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. response sur-face methodology (rsm) is one of the effective methods that can be employed for this purpose. since quality of products or processes is usually evaluated through several quality ...

In a robust parameter design (RPD) problem, the experimenter is interested to determine the values of con-trol factors such that responses will be robust or insensitive to variability of the noise factors. Response sur-face methodology (RSM) is one of the effective methods that can be employed for this purpose. Since quality of products or processes is usually evaluated through several quality ...

Robust parameter design (RPD) is considered as a cost effective tool for reducing process variability. Robust parameter control, integrating RPD with automatic process control, will performance better than the traditional RPD approach. This paper proposed a strategy of robust process control based on selecting online controllable variables with consideration of generalized quality cost, which i...

Two new Bayesian approaches to Robust Parameter Design (RPD) are presented that recompute the optimal control factor settings based on on-line measurements of the noise factors. A dual response model approach to RPD is taken. The first method uses the posterior predictive density of the responses to determine the optimal control factor settings. A second method uses in addition the predictive d...

The following article presents the application of an evolutionary strategy to produce nearly optimal design matrices, containing the parameter settings to execute an experiment. The properties of such matrices significantly determine the precision of the optimization models used in Robust Parameter Design (RPD). The methodology presented allows the user to produce new experimental design matric...

Robust Parameter Design (RPD) has been used as the primary technique to reduce process and product variability. The offline choice of appropriate control factor settings allows RPD to ensure that noise factors have a minimum influence on responses. In this article, an alternative methodology of automatic process control is proposed, that is, controllable factors are adjusted online based on inp...

Multi-objective optimization Genetic Algorithms Robust Design The following paper describes the main objective to follow the methodology used and proposed to obtain the optimal values of WEDM process operation on the machine Robofil 310 by robust parameter design (RPD) of Dr. G. Taguichi [TAGUCHI, G. 1993], through controllable factors which result in more inferences regarding the problem to no...

In this study, the optimal factor value estimates of the effect of Zirconium Oxychloride (ZrOCl2) with different concentrations of citric and formic acids on the flame-retardant properties of wool which is assessed by thermal analysis, mass loss, the limiting oxygen index and vertical flame is considered and compared by two different optimization approaches, namely, with and without Robust Para...

Normal Boundary Intersection (NBI) is traditionally used to generate equally spaced and uniformly spread Pareto Frontiers for multi-objective optimization programming (MOP). This method tends to fail, however, when correlated objective functions must be optimized using Robust Parameter Designs (RPD). In such multi-objective optimization programming, there can be reached impractical optima and n...

Motivation-Two decision-making processes have been identified: an analytical process and an intuitive process. One conceptual model of the latter is the Recognition Primed Decision (RPD) model (Klein, 2008). According to this model, decision making in naturalistic contexts entails a situational patternrecognition process which, if subsequent expectancies are confirmed, lead the decision maker t...