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A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method
Joint Authors
Moeini, Asghar
Bastan, Mahdi
Salmasnia, Ali
Source
International Journal of Quality, Statistics, and Reliability
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-07-26
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Economics & Business Administration
Economy
Abstract EN
An important problem encountered in product or process design is the setting of process variables to meet a required specification of quality characteristics (response variables), called a multiple response optimization (MRO) problem.
Common optimization approaches often begin with estimating the relationship between the response variable with the process variables.
Among these methods, response surface methodology (RSM), due to simplicity, has attracted most attention in recent years.
However, in many manufacturing cases, on one hand, the relationship between the response variables with respect to the process variables is far too complex to be efficiently estimated; on the other hand, solving such an optimization problem with accurate techniques is associated with problem.
Alternative approach presented in this paper is to use artificial neural network to estimate response functions and meet heuristic algorithms in process optimization.
In addition, the proposed approach uses the Taguchi robust parameter design to overcome the common limitation of the existing multiple response approaches, which typically ignore the dispersion effect of the responses.
The paper presents a case study to illustrate the effectiveness of the proposed intelligent framework for tackling multiple response optimization problems.
American Psychological Association (APA)
Salmasnia, Ali& Bastan, Mahdi& Moeini, Asghar. 2012. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method. International Journal of Quality, Statistics, and Reliability،Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-476173
Modern Language Association (MLA)
Salmasnia, Ali…[et al.]. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method. International Journal of Quality, Statistics, and Reliability No. 2012 (2012), pp.1-11.
https://search.emarefa.net/detail/BIM-476173
American Medical Association (AMA)
Salmasnia, Ali& Bastan, Mahdi& Moeini, Asghar. A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method. International Journal of Quality, Statistics, and Reliability. 2012. Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-476173
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-476173