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Neuro-Genetic Design Optimization Framework to Support the Integrated Robust Design Optimization Process in CE

Uluda University, Industrial Engineering Department, Görükle Campus, 16059 Bursa, Turkey

Ali R. Yldz

Uluda University, Mechanical Engineering Department, Görükle Campus, 16059 Bursa, Turkey

Necmettin Kaya

Uluda University, Mechanical Engineering Department, Görükle Campus, 16059 Bursa, Turkey

Ferruh Öztürk

Uluda University, Mechanical Engineering Department, Görükle Campus, 16059 Bursa, Turkey, ferruh{at}uludag.edu.tr

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.

Key Words: concurrent engineering • neural networks • genetic algorithm • Taguchi’s method

Concurrent Engineering, Vol. 14, No. 1, 5-16 (2006)
DOI: 10.1177/1063293X06063314


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