Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84392
Title: Multidisciplinary design optimization foradditive manufactured customized products
Authors: Yao, Xiling
Moon, Seung Ki
Bi, GuiJun
Keywords: Additive manufacturing
Multidisciplinary design optimization
Issue Date: 2016
Source: Yao, X., Moon, S. K., & Bi, G. (2016). Multidisciplinary design optimization foradditive manufactured customized products. Proceedings of the 2nd International Conference on Progress in Additive Manufacturing (Pro-AM 2016), 216-221.
Abstract: Multidisciplinary design optimization (MDO) is an area of mathematical research to solve complex engineering design problems involving multiple disciplines which usually interact with each other. Previous MDO studies have mainly focused on aircraft and energy system design. However, MDO has not been explored in the concurrent engineering of additive manufactured products. In this paper, an MDO problem is formulated to optimize additive manufactured customized products, aiming to satisfy customization requirements, reduce costs, and guarantee structural integrity of mechanical components. Therefore, disciplines that are incorporated into the proposed MDO problem include consumer preference modeling, production costing, and structural mechanics. Additive manufacturing (AM) process-specific design constraints are expressed in the constraint functions of the MDO. Component, AM process, and material selection as well as product geometric parameters are chosen as design variables, and their optimal values are identified by the MDO simultaneously. Metamodels generated by data obtained from high-fidelity finite element models (FEM) are applied in the proposed MDO to speed up the solving process. Multi-objective genetic algorithm (GA) is adapted to solve the MDO problem. A case study in designing customized trans-tibial (TT) prosthesis with additive manufactured components is presented to illustrate the proposed MDO method. A multi-dimensional Pareto optimal set of design variables can be successfully calculated from the MDO.
URI: https://hdl.handle.net/10356/84392
http://hdl.handle.net/10220/41761
Rights: © 2016 by Pro-AM 2016 Organizers. Published by Research Publishing, Singapore
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers
Pro-AM Conference Papers
SIMTech Conference Papers

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