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Most structural products have complex geometry to meet customer’s demand of high functionality. Since manufacturing those products in one piece is either impossible or uneconomical, most structural products are assemblies of components with simpler geometries. The conventional way to design structural assemblies is to design overall geometry first, and then decompose the geometry to determine the part boundary and joint locations. This two-step process, however, can lead to sub-optimal designs since the product geometry, even if optimized as one piece, would not be optimal after decomposition. This paper presents a method for synthesizing structural assemblies directly from the design specifications, without going through the two-step process. Given an extended design domain with boundary and loading conditions, the method simultaneously optimizes the topology and geometry of an entire structure and the location and configuration of joints, considering structural performance, manufacturability, and assembleability. As a relaxation of our previous work utilizing a beam-based ground structure [1], this paper presents a new formulation in a continuum design domain, which greatly enhances the ability to represent complex structural geometry observed in real-world products. A multi-objective genetic algorithm is used to obtain Pareto optimal solutions that exhibits trade-offs among stiffness, weight, manufacturability, and assembleability.Copyright © 2008 by ASME

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.