Optimization in science and engineering
Amir Parnianifard; Hamidreza Izadbakhsh
Abstract
Up to now, different tools have been introduced for analyses of process capability such as process capability indexes of Cp, Cpk and Cpm that developed for analyzing the capability of process and adapting the quality specifications of product. Most studies in the literature only consider the process ...
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Up to now, different tools have been introduced for analyses of process capability such as process capability indexes of Cp, Cpk and Cpm that developed for analyzing the capability of process and adapting the quality specifications of product. Most studies in the literature only consider the process with single capability variable, while there is a lack of studies that consider multi-variable processes. Cpm index has been defined with Taguchi overview over robust design. In this research, the metric Lp model has introduced to investigate the optimum decision variables by considering nominal is better quality specification and reparation Cpm index. We also expand the proposed model for such a processes with considering overall cost as well as process quality. At the end of research, numerical example has been presented to exhibit usage of proposed model for obtaining the best levels of process decision variables.
Optimization in science and engineering
Amir Parnianifard
Abstract
The quality loss function is conventional techniques in robust design terminology that consider the deviation of output from ideal point and variability as well. Mostly in practice, processes are affected by uncontrollable external factors that cause output of process to be far from ideal points with ...
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The quality loss function is conventional techniques in robust design terminology that consider the deviation of output from ideal point and variability as well. Mostly in practice, processes are affected by uncontrollable external factors that cause output of process to be far from ideal points with variability around its exact value. In this research, the common Taguchi quality loss function is applied to propose a new robust optimization model that able to choose optimal results of input variables. In this model, the quality loss function is expanded and a nonlinear optimization model is introduced in order to minimize the effect of environmental noise variables. In the end, a numerical example is presented to show the applicability of the proposed model for investigating the best levels of input variables in the noisy process.