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Research Papers: Design Theory and Methodology

Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing

[+] Author and Article Information
Yi Xiong

Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: yi_xiong@sutd.edu.sg

Pham Luu Trung Duong

Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: luu_pham@sutd.edu.sg

Dong Wang

Robotics Institute,
School of Mechanical Engineering,
State Key Laboratory of Mechanical System and Vibration,
Shanghai Jiao Tong University,
Shanghai 200241, China
e-mail: wang_dong@sjtu.edu.cn

Sang-In Park

Department of Mechanical Engineering and Robotics,
Incheon National University,
Incheon 22012, South Korea
e-mail: sangin.park@inu.ac.kr

Qi Ge

Science and Math Cluster and Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: ge_qi@sutd.edu.sg

Nagarajan Raghavan

Engineering Product Development Pillar and Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
Singapore 487372, Singapore
e-mail: nagarajan@sutd.edu.sg

David W. Rosen

Digital Manufacturing and Design Centre,
Singapore University of Technology and Design,
Singapore 487372, Singapore;
The G. W. Woodruff School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: david_rosen@sutd.edu.sg

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received January 8, 2019; final manuscript received April 10, 2019; published online May 23, 2019. Assoc. Editor: Ying Liu.

J. Mech. Des 141(10), 101101 (May 23, 2019) (12 pages) Paper No: MD-19-1014; doi: 10.1115/1.4043587 History: Received January 08, 2019; Accepted April 20, 2019

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.

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Copyright © 2019 by ASME
Topics: Design
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Figures

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Fig. 1

(Top) The process-structure-property-based design problem formulation for a computer-aided design for AM system proposed in Ref. [1]; (bottom) the design process for AM-fabricated products that adapt from Ref. [3]; and (middle) the proposed design method for design exploration in embodiment design and for design exploitation in detailed design

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Fig. 2

(a) Training of data-driven models and (b) examples of data-driven models: (i) classification: Bayesian network classifiers that classify the design space into feasible and infeasible parts based on the dataset of observed inputs and responses and (ii) regression: Gaussian process regression models that predict the responses at untested design sets based on the observed dataset. Also, the 95% prediction interval is given as dash lines.

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Fig. 3

The proposed data-driven framework for design exploration and exploitation. Design exploration (item 1–2) is discussed in Sec. 4.1 and design exploitation (item 3–5) is discussed in Sec. 4.2.

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Fig. 4

An example of using BNC to examine the influence of changes in the satisfactory region on feasible design regions: (a) defined satisfactory regions for the bead height, range 1: 1.7–2.3 mm, range 2: 1.8–2.2 mm, range 3: 1.9–2.1 mm and (b) corresponding feasible design regions

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Fig. 5

An example of using BNC for design exploration for a multiobjective design problem: (a) design targets for bead heights and widths; (b) designs satisfy height targets; (c) designs satisfy width targets; and (d) designs satisfy both targets

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Fig. 6

(a) A scanned foot model and its coordinate system and (b) a 2D ankle brace that is flattened from the scanned foot model

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Fig. 7

(a) A quad horseshoe structure unit cell and its five design variables and (b) stress–strain responses of a quad horse shoe structure assembly in two directions

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Fig. 8

Parallel coordinates plots for identified design regions that satisfy design targets of zone 1

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Fig. 9

FE analysis simulated stress–strain responses of identified optimal designs using initial GPR models and refined GPR models

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Fig. 10

Fabricated ankle brace with optimized geometric structures

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