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Kon-Well Wang

Researcher at University of Michigan

Publications -  204
Citations -  5435

Kon-Well Wang is an academic researcher from University of Michigan. The author has contributed to research in topics: Bistability & Nonlinear system. The author has an hindex of 33, co-authored 191 publications receiving 4128 citations. Previous affiliations of Kon-Well Wang include Pennsylvania State University.

Papers
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A review of the recent research on vibration energy harvesting via bistable systems

TL;DR: A review of the major efforts and findings documented in the literature can be found in this article, where a common analytical framework for bistable electromechanical dynamics is presented, the principal results are provided, the wide variety of bistably energy harvesters are described, and some remaining challenges and proposed solutions are summarized.
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Architected Origami Materials: How Folding Creates Sophisticated Mechanical Properties.

TL;DR: Recent studies on the different aspects of origami materials-geometric design, mechanics analysis, achieved properties, and fabrication techniques-are highlighted and the challenges ahead discussed.
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Programmable Self-Locking Origami Mechanical Metamaterials.

TL;DR: It is shown that nonflat-foldable origami provides a new platform to achieve programmability via its intrinsic self-locking and reconfiguration capabilities, and the n-layer metamaterial's stiffness is controllable among 2n target stiffness values.
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Variable stiffness structures utilizing fluidic flexible matrix composites

TL;DR: In this paper, a 3D analytical model is developed to characterize the axial stiffness behavior of a single F 2MC tube, and the test results show good agreement with the model predictions.
Book

Harnessing Bistable Structural Dynamics: For Vibration Control, Energy Harvesting and Sensing

TL;DR: In this article, the authors provide a coherent understanding of how harnessing the dynamics of bistable structures may enhance the technical fields of vibration control, energy harvesting, and sensing. But, their focus is on nonlinearity.