scholarly journals Erratum: “Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing” [ASME J. Mech. Des. 2019, 141(10), p. 101101; DOI: 10.1115/1.4043587]

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Yi Xiong ◽  
Pham Luu Trung Duong ◽  
Dong Wang ◽  
Sang-In Park ◽  
Qi Ge ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Data Driven ◽  
Design For Additive Manufacturing ◽  
Exploration And Exploitation

Design Space Exploration of Split-Path Data Driven Dynamic Full Adder

2010 ◽  
Vol 6 (4) ◽  
pp. 469-481 ◽  
Author(s):  
Sohan Purohit ◽  
Marco Lanuzza ◽  
Martin Margala
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Full Adder ◽  
Data Driven

A Cost-Efficient Data-Driven Approach to Design Space Exploration for Personalized Geometric Design in Additive Manufacturing

2021 ◽  
pp. 1-15
Author(s):  
SungKu Kang ◽  
Xinwei Deng ◽  
Ran Jin
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Data Driven ◽  
Design Rules ◽  
Computation Cost ◽  
Physically Based ◽  
Efficient Data ◽  
Cost Efficient ◽  
Product Realization

Abstract Additive manufacturing (AM) is considered as a key to personalized product realization as it provides great design flexibility. As the flexibility radically expands the design space, current design space exploration methods for personalized geometric designs become time-consuming due to the use of physically-based computer simulations (e.g., finite element analysis or computational fluid dynamics). This poses a significant challenge in design for an efficient personalized product realization cycle, which imposes a tight computation cost constraint to timely respond to every new requirement. To address the challenge, we propose a cost-efficient data-driven design space exploration method for personalized geometric design in AM, enabling precise feasible design regions under the computation constraint. Specifically, the proposed method adopts surrogate modeling of efficient voxel model-based design rules to identify feasible design regions considering both manufacturability and personalized needs. Since design rules take much less time for evaluation than physically-based simulations, the proposed method can contribute to timely providing feasible design regions for an efficient personalized product realization cycle. Moreover, we develop a cost-based experimental design for surrogate modeling, which enables the evaluation of additional design points to provide more precise feasible design regions under the computation cost constraint. The merits of the proposed method are elaborated via additively manufactured microbial fuel cell (MFC) anode design.

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

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Yi Xiong ◽  
Pham Luu Trung Duong ◽  
Dong Wang ◽  
Sang-In Park ◽  
Qi Ge ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Design Process ◽  
Sampling Method ◽  
Design Space ◽  
Data Driven ◽  
Design Stage ◽  
Design For Additive Manufacturing ◽  
Detailed Design ◽  
Embodiment Design ◽  
Ankle Brace

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.

Design Space Exploration of a 512KB STT-Assisted SOT MRAM Cache

2020 ◽  
Author(s):  
Adrian G. Caburnay ◽  
Jonathan Gabriel S.A. Reyes ◽  
Anastacia P. Ballesil-Alvarez ◽  
Maria Theresa G. de Leon ◽  
John Richard E. Hizon ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration

scholarly journals Optode Design Space Exploration for Clinically-robust Non-invasive Fetal Oximetry

2019 ◽  
Vol 18 (5s) ◽  
pp. 1-22 ◽  
Author(s):  
Daniel D. Fong ◽  
Vivek J. Srinivasan ◽  
Kourosh Vali ◽  
Soheil Ghiasi
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Non Invasive
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