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Bayesian Topology Optimization for Efficient Design of Origami Folding Structures

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2020, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Bayesian optimization (BO) is a popular method for solving optimization problems involving expensive objective functions. Although BO has been applied across various fields, its use in structural optimization area is in its early stages. Origami folding structures provide a complex design space where the use of an efficient optimizer is critical. In this research work for the first time the ability of BO to solve origami-inspired design problems is demonstrated. A Gaussian process (GP) is used as the surrogate model that is trained to mimic the response of the expensive finite element (FE) objective function. The ability of this BO-FE framework to find optimal designs is verified by applying it to two well known origami design problems: chomper and twist chomper. The performance of the proposed approach is compared to traditional gradient-based optimization techniques and genetic algorithm methods in terms of ability to discover designs, computational efficiency and robustness. BO has many user-defined components and parameters, and intuitions for these for structural optimization are currently limited. In this work, the role of hyperparameter tuning and the sensitivity of Bayesian optimization to the quality and size of the initial training set is studied. Taking a holistic view of the computational expense, various heuristic approaches are proposed to reduce the overall cost of optimization. A methodology to include derivative information of the objective function in the formulation of the GP surrogate is described, and its advantages and disadvantages are discussed. Additionally, an anisotropic GP surrogate model with independent length scales for each design variable is studied. A procedure to reduce the overall dimension of the problem using information from anisotropic models is proposed. The results show that Bayesian optimization is an efficient and robust alternative to traditional methods. It allows for the discovery of optimal designs using fewer finite element solutions, which makes it an attractive choice for the non-convex design space of origami fold mechanics.
Kumar Vemaganti, Ph.D. (Committee Chair)
Philip Buskohl, Ph.D. (Committee Member)
Manish Kumar, Ph.D. (Committee Member)
127 p.

Recommended Citations

Citations

  • Shende, S. (2020). Bayesian Topology Optimization for Efficient Design of Origami Folding Structures [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592170569337763

    APA Style (7th edition)

  • Shende, Sourabh. Bayesian Topology Optimization for Efficient Design of Origami Folding Structures. 2020. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592170569337763.

    MLA Style (8th edition)

  • Shende, Sourabh. "Bayesian Topology Optimization for Efficient Design of Origami Folding Structures." Master's thesis, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592170569337763

    Chicago Manual of Style (17th edition)