Designing coupling behaviors using compliant shape optimization

Ulu, Nurcan Gecer; Coros, Stelian; Kara, Levent Burak

doi:10.1016/j.cad.2018.03.008

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…Laplacian energy based deformation handles are used to manipulate designs using small number of variables for shape optimization in [25]. A Laplacian based order reduction has been Figure 1: Overview of our approach.…”

Section: Reduced Order Design Optimizationmentioning

confidence: 99%

Sliding Basis Optimization for Heterogeneous Material Design

Ulu¹,

Korneev²,

Ulu³

et al. 2020

Preprint

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We present the sliding basis computational framework to automatically synthesize heterogeneous (graded or discrete) material fields for parts designed using constrained optimization. Our framework uses the fact that any spatially varying material field over a given domain may be parameterized as a weighted sum of the Laplacian eigenfunctions. We bound the parameterization of all material fields using a small set of weights to truncate the Laplacian eigenfunction expansion, which enables efficient design space exploration with the weights as a small set of design variables. We further improve computational efficiency by using the property that the Laplacian eigenfunctions form a spectrum and may be ordered from lower to higher frequencies. Starting the optimization with a small set of weighted lower frequency basis functions we iteratively include higher frequency bases by sliding a window over the space of ordered basis functions as the optimization progresses. This approach allows greater localized control of the material distribution as the sliding window moves through higher frequencies. The approach also reduces the number of optimization variables per iteration, thus the design optimization process speeds up independent of the domain resolution without sacrificing analysis quality. While our method is useful for problems where analytical gradients are available, it is most beneficial when the gradients may not be computed easily (i.e., optimization problems coupled with external black-box analysis) thereby enabling optimization of otherwise intractable design problems. The sliding basis framework is independent of any particular physics analysis, objective and constraints, providing a versatile and powerful design optimization tool for various applications. We demonstrate our approach on graded solid rocket fuel design and multi-material topology optimization applications and evaluate its performance.

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Section: Reduced Order Design Optimizationmentioning

confidence: 99%

Sliding Basis Optimization for Heterogeneous Material Design

Ulu¹,

Korneev²,

Ulu³

et al. 2020

Preprint

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“…While WoC is observable in such domains where the participating individuals have experience or familiarity with the question at hand, it remains unclear how effective WoC is for domains that traditionally require deep expertise or sophisticated computational models to estimate objectively answers. This work explores how effective WoC is for engineering design problems that are esoteric in nature, that is, problems (1) whose solutions traditionally require expertise and specialized knowledge, (2) where access to experts can be costly or infeasible, and (3) in which previous WoC studies with the general population have been shown to be highly ineffective. The main hypothesis in this work is that in the absence of experts, WoC can be observed in groups that consist of practitioners who are defined to have a base familiarity with the problems in question but not necessarily domain experts.…”

Section: Wisdom Of Micro-crowds In Evaluating Solutions To Esoteric E...mentioning

confidence: 99%

“…This chapter is based on Ulu et al2018 [2]. as possible as a way to facilitate temporary affixing, quick assembly and maintenance, and general ease of use.…”

Section: Introductionmentioning

confidence: 99%

“…As one step toward addressing this gap, this work explores the effectiveness of WoC for engineering design problems that are esoteric in nature. Esoteric problems are defined as those (1) that traditionally require expertise and specialized knowledge, (2) where access to experts can be costly or infeasible, and (3) in which previous WoC studies with the general population have been shown to be highly ineffective [73]. The main hypothesis in this work is that in the absence of experts, WoC can be observed in groups that consist of practitioners who are defined to have a base familiarity with the domain and the problems in question, even though no single individual may have the expertise to correctly solve the problem.…”

Section: Introductionmentioning

confidence: 99%

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Computational Design and Evaluation Methods for Empowering Non-Experts in Digital Fabrication

Ulu

2020

Preprint

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Special thanks go to the committee Prof. Jessica Zhang, Prof. Kenji Shimada and Prof. Stelian Coros. Prof. Zhang, your input has been very helpful in shaping up this thesis. Prof. Shimada, you have been a great example for me to look up to in your classes as well as an expert in the field. Stelian, you have been an integral part of my PhD. I owe a great deal of my success to your involvement in my research.Thank you labmates and collaborators, this thesis would not come together without you. I am eternally grateful to my family for their endless support and love. My dear friends, you made my time here enjoyable.Very few people get to work with their spouses and even fewer like the idea of it. Erva, I consider myself the luckiest person in the world for sharing the whole experience with you and having your support along the way.

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“…Due to the unique capabilities of AM, new methods and tools are needed to improve design for AM (DFAM) to maximize product performance [26]. One class of methods ripe for adaptation for DFAM is topology optimization-a powerful technique in structural design that optimizes the shape and material connectivity of a domain through the use of finite element methods together with various optimization techniques [8,27].…”

Section: Topology Optimizationmentioning

confidence: 99%

Concurrent Structure and Process Optimization for Minimum Cost Metal Additive Manufacturing

Ulu

Huang

Kara

et al. 2019

Journal of Mechanical Design

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Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental and economic benefits in many different industries. However, the total production costs of MAM will need to be reduced substantially before it will be widely adopted across the manufacturing sector. Current topology optimization approaches focus on reducing total material volume as a means of reducing material costs, but they do not account for other production costs that are influenced by a part's structure such as machine time and scrap. Moreover, concurrently optimizing MAM process variables with a part's structure has the potential to further reduce production costs. This paper demonstrates an approach to use process-based cost modeling (PBCM) in MAM topology optimization to minimize total production costs, including material, labor, energy, and machine costs, using cost estimates from industrial MAM operations. The approach is demonstrated on various 3D geometries for the electron beam melting (EBM) process with Ti64 material. Concurrent optimization of the part structures and EBM process variables is compared to sequential optimization, and to optimization of the structure alone. The results indicate that, once process variables are considered concurrently, more cost effective results can be obtained with similar amount of material through a combination of (1) building high stress regions with lower power values to obtain larger yield strength and (2) increasing the power elsewhere to reduce the number of passes required, thereby reducing build time. In our case studies, concurrent optimization of the part's structure and MAM process parameters lead to up to 15% lower estimated total production costs and 21% faster build time than optimizing the part's structure alone.

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Designing coupling behaviors using compliant shape optimization

Cited by 9 publications

References 36 publications

Sliding Basis Optimization for Heterogeneous Material Design

Sliding Basis Optimization for Heterogeneous Material Design

Computational Design and Evaluation Methods for Empowering Non-Experts in Digital Fabrication

Concurrent Structure and Process Optimization for Minimum Cost Metal Additive Manufacturing

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