Generative Design of Origami-Inspired Mechanisms With a Variational Level Set Approach

Abstract Origami has inspired the engineering design of self-assemble and re-configurable devices. Under particular crease patterns, a 2D flatten object can be transformed into a complex 3D structure. This work intends to find out a systematic solution for topology optimization of origami structures. The origami mechanism is simulated using shell models where the in-plane membrane, out of plane bending, and shear deformation can be well captured. Moreover, the pattern of the folds is represented implicitly by the boundaries of the level set function. The topology of the folds is optimized by minimizing a new multiobjective function, aiming to balance the kinematic performance with the structural stiffness as well as the geometric requirements. Besides regular straight folds, our proposed model can mimic crease patterns with curved folds. With the folding curves implicitly represented, the curvature flow are utilized to control the complexity of the generated folds. The effectiveness of the proposed method is demonstrated through the computational generation and physical validation of a thin-shell origami gripper.

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A variational level set approach for surface area minimization of triply-periodic surfaces

Journal of Computational Physics ◽

10.1016/j.jcp.2006.10.007 ◽

2007 ◽

Vol 223 (2) ◽

pp. 711-730 ◽

Cited By ~ 41

Author(s):

Y. Jung ◽

K.T. Chu ◽

S. Torquato

Keyword(s):

Surface Area ◽

Level Set ◽

Periodic Surfaces ◽

Triply Periodic ◽

Area Minimization ◽

Variational Level Set ◽

Level Set Approach

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Variational level set approach for automatic correction of multiplicative and additive intensity inhomogeneities in brain MR Images

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2012.6345880 ◽

2012 ◽

Author(s):

N. Verma ◽

M. C. Cowperthwaite ◽

M. K. Markey

Keyword(s):

Level Set ◽

Automatic Correction ◽

Mr Images ◽

Variational Level Set ◽

Brain Mr Images ◽

Level Set Approach

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A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/9174275 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Jian Tang ◽

Xiaoliang Jiang

Keyword(s):

Image Segmentation ◽

Level Set ◽

Energy Function ◽

Bias Field ◽

Superior Performance ◽

Local Entropy ◽

Distribution Fitting ◽

Field Estimation ◽

Variational Level Set ◽

Level Set Approach

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

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