Multidisciplinary design optimization foradditive manufactured customized products

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Yao, Xiling, Moon, Seung Ki, Bi, GuiJun
Other Authors: School of Mechanical and Aerospace Engineering
Format: Conference or Workshop Item
Language:English
Published: 2016
Subjects:
Online Access:https://hdl.handle.net/10356/84392
http://hdl.handle.net/10220/41761
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary: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.