Please use this identifier to cite or link to this item:
Title: Multidisciplinary design optimization to identify additive manufacturing resources in customized product development
Authors: Yao, Xiling
Moon, Seung Ki
Bi, Guijun
Keywords: DRNTU::Engineering::Mechanical engineering
Additive Manufacturing
Customized Products
Issue Date: 2016
Source: Yao, X., Moon, S. K., & Bi, G. (2017). Multidisciplinary design optimization to identify additive manufacturing resources in customized product development. Journal of Computational Design and Engineering, 4(2), 131-142. doi:10.1016/j.jcde.2016.10.001
Series/Report no.: Journal of Computational Design and Engineering
Abstract: Additive manufacturing (AM) techniques are ideal for producing customized products due to their high design flexibility. Despite the previous studies on specific additive manufactured customized products such as biomedical implants and prostheses, the simultaneous optimization of components, materials, AM processes, and dimensions remains a challenge. Multidisciplinary design optimization (MDO) is a research area of solving complex design problems involving multiple disciplines which usually interact with each other. The objective of this research is to formulate and solve an MDO problem in the development of additive manufactured products customized for various customers in different market segments. Three disciplines, i.e. the customer preference modeling, AM production costing, and structural mechanics are incorporated in the MDO problem. The optimal selections of components, materials, AM processes, and dimensional parameters are searched with the objectives to maximize the functionality utility, match individual customers’ personal performance requirements, and minimize the total cost. A multi-objective genetic algorithm with the proposed chromosome encoding pattern is applied to solve the MDO problem. A case study of designing customized trans-tibial prostheses with additive manufactured components is presented to illustrate the proposed MDO method. Clusters of multi-dimensional Pareto-optimal design solutions are obtained from the MDO, showing trade-offs among the objectives. Appropriate design decision can be chosen from the clusters based on the manufacturer׳s market strategy.
ISSN: 2288-4300
DOI: 10.1016/j.jcde.2016.10.001
Rights: © 2016 Society for Computational Design and Engineering. Publishing Servies by Elsevier. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.