Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/21540
Title: Neuro-genetic design optimization framework to support the integrated robust design optimization process in CE
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
Uludağ Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü.
0000-0002-8297-0777
0000-0003-1790-6987
Öztürk, Nursel
Yıldız, Ali R.
Kaya, Necmettin
Öztürk, Ferruh
F-7426-2011
AAG-9336-2021
R-4929-2018
AAG-9923-2021
7005688805
7102365439
7005013334
56271685800
Keywords: Computer science
Engineering
Operations research & management science
Taguchi's method
Genetic algorithm
Neural networks
Concurrent engineering
Database
Implementation
System
Algorithm
Network
Shape
Topology
Image interpretation
Concurrent design
Computational complexity
Optimization
Product design
Integrated robust design optimization process
Neuro-genetic design optimization framework
Taguchi's method
Issue Date: 2006
Publisher: Sage Publications
Citation: Öztürk, N. vd. (2006). ''Neuro-genetic design optimization framework to support the integrated robust design optimization process in CE''. Concurrent Engineering Research and Applications, 14(1), 5-16.
Abstract: This article describes an integrated and optimized product design framework to support the design optimization applications in concurrent engineering (CE). The significant consideration is given to show the effectiveness of hybrid approaches and how they can be used to improve the performance of integrated design optimization applications. The proposed approach is based on two-stages which are (1) the use of neural networks (NNs) and genetic algorithm (GA) with feature technology for integrated design activities and (2) the use of Taguchi's method and GA for design parameters optimization. The first stage resulted in better integrated design solutions in terms of computational complexity and later resulted in a solution, which leads to better and more robust parameter values for multi-objective shape design optimization. The effectiveness and validity of the proposed approach are evaluated with examples.
URI: https://doi.org/10.1177/1063293X06063314
https://journals.sagepub.com/doi/10.1177/1063293X06063314
http://hdl.handle.net/11452/21540
ISSN: 1063-293X
1531-2003
Appears in Collections:Scopus
Web of Science

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