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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October 2019
Research-Article
Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing
Yi Xiong,
Yi Xiong
1
Digital Manufacturing and Design Centre,
Singapore 487372,
e-mail: yi_xiong@sutd.edu.sg
Singapore University of Technology and Design
,Singapore 487372,
Singapore
e-mail: yi_xiong@sutd.edu.sg
1Corresponding author.
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Pham Luu Trung Duong,
Pham Luu Trung Duong
Digital Manufacturing and Design Centre,
Singapore 487372,
e-mail: luu_pham@sutd.edu.sg
Singapore University of Technology and Design
,Singapore 487372,
Singapore
e-mail: luu_pham@sutd.edu.sg
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Dong Wang,
Dong Wang
Robotics Institute,
School of Mechanical Engineering,
State Key Laboratory of Mechanical System and Vibration,
Shanghai 200241,
e-mail: wang_dong@sjtu.edu.cn
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
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Sang-In Park,
Sang-In Park
Department of Mechanical Engineering and Robotics,
Incheon 22012,
e-mail: sangin.park@inu.ac.kr
Incheon National University
,Incheon 22012,
South Korea
e-mail: sangin.park@inu.ac.kr
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Qi Ge,
Qi Ge
Science and Math Cluster and Digital Manufacturing and Design Centre,
Singapore 487372,
e-mail: ge_qi@sutd.edu.sg
Singapore University of Technology and Design
,Singapore 487372,
Singapore
e-mail: ge_qi@sutd.edu.sg
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Nagarajan Raghavan,
Nagarajan Raghavan
Engineering Product Development Pillar and Digital Manufacturing and Design Centre,
Singapore 487372,
e-mail: nagarajan@sutd.edu.sg
Singapore University of Technology and Design
,Singapore 487372,
Singapore
e-mail: nagarajan@sutd.edu.sg
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David W. Rosen
David W. Rosen
Digital Manufacturing and Design Centre,
Singapore 487372,
Singapore University of Technology and Design
,Singapore 487372,
Singapore
;The G. W. Woodruff School of Mechanical Engineering,
Atlanta, GA 30332
e-mail: david_rosen@sutd.edu.sg
Georgia Institute of Technology
,Atlanta, GA 30332
e-mail: david_rosen@sutd.edu.sg
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Yi Xiong
Digital Manufacturing and Design Centre,
Singapore 487372,
e-mail: yi_xiong@sutd.edu.sg
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 487372,
e-mail: luu_pham@sutd.edu.sg
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 200241,
e-mail: wang_dong@sjtu.edu.cn
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 22012,
e-mail: sangin.park@inu.ac.kr
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 487372,
e-mail: ge_qi@sutd.edu.sg
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 487372,
e-mail: nagarajan@sutd.edu.sg
Singapore University of Technology and Design
,Singapore 487372,
Singapore
e-mail: nagarajan@sutd.edu.sg
David W. Rosen
Digital Manufacturing and Design Centre,
Singapore 487372,
Singapore University of Technology and Design
,Singapore 487372,
Singapore
;The G. W. Woodruff School of Mechanical Engineering,
Atlanta, GA 30332
e-mail: david_rosen@sutd.edu.sg
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. Oct 2019, 141(10): 101101 (12 pages)
Published Online: May 23, 2019
Article history
Received:
January 8, 2019
Revision Received:
April 10, 2019
Accepted:
April 20, 2019
Connected Content
Citation
Xiong, Y., Duong, P. L. T., Wang, D., Park, S., Ge, Q., Raghavan, N., and Rosen, D. W. (May 23, 2019). "Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing." ASME. J. Mech. Des. October 2019; 141(10): 101101. https://doi.org/10.1115/1.4043587
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