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dc.contributor.advisorMarkus J. Buehler.en_US
dc.contributor.authorGu, Grace Xiangen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2018-11-28T15:44:03Z
dc.date.available2018-11-28T15:44:03Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119343
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 189-206).en_US
dc.description.abstractAfter billions of years of evolution, it comes as no surprise that biological materials are identified as invaluable sources of inspiration in the search for new materials. Bone, teeth, and spider silk, to name a few, are high-performing biological composites that possess impressive mechanical properties unmatched by their engineering counterparts. Many required mechanical properties in engineering practice are inherently conflicting. In contrast, natural materials can often avoid these fundamental compromises through sophisticated hierarchical structures. Additive manufacturing, with its layer-by-layer fabrication capabilities, facilitates leveraging natural material design to create complex bioinspired architectures. Expanding the design space, however, is indiscriminate in terms of specific material property optimization. In order to best use the templates derived from nature, there needs to be a process to match form to function. This thesis provides a framework that focuses on emulating the simple, yet elusive, design paradigms of nature - simple in their constituent building blocks and elusive in their underlying complexity. At the same time, using simulation and experiments we elucidate the mechanisms that generate their superior properties. A main undertaking of this thesis is to further improve and adapt biological designs for engineering requirements through algorithms and machine learning. Specifically, this thesis takes nacre and conch as model natural materials and deconstructs them in the bioinspired algorithmic-driven design (BADD) framework to build up rationally designed engineering composites. In this work, we show that structural feature placement introduces hierarchy and can amplify mechanical material properties, especially at the interfaces of composites. Heterogeneous material interfaces allow for diffuse load transfer at the interface, leading to a more distributed strain field enhancing overall toughness and strength of composites. Dynamic and static test loading cases, together with simulation provide validation, demonstrating the effects of hierarchy and heterogeneity on composite impact performance. Furthermore, microcracks can be exploited as an impact-enhancing mechanism for hierarchical composites under dynamic loading. Incorporation of machine learning generates new superior designs with a minimal training set and provides orders-of-magnitude faster exploration of design space compared with traditional methods. These concepts lay the foundation for our approach to designing rationally toughened composites to be used in protective gear, energy applications, industrial components, and beyond. The BADD framework can be further used to study other natural or synthetic materials of interest. In the future, this bioinspired machine learning approach will enable materials-by-design of complex architectures to meet demanding engineering challenges.en_US
dc.description.sponsorshipFunded by a National Defense Science and Engineering Graduate (NDSEG) fellowship and BASF-NORAen_US
dc.description.statementofresponsibilityby Grace X. Gu.en_US
dc.format.extent206 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleBioinspired algorithmic-driven design of additively manufactured compositesen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.identifier.oclc1065536897en_US


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