Abstract
This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network (CNN) and elastic deformation. An improved CNN model is developed to simulate the deformation of soft objects. A finite volume based method is presented to derive the discrete differential operators over irregular nets for obtaining the internal elastic forces. The proposed methodology not only models the deformation dynamics in continuum mechanics, but it also simplifies the complex deformation problem with simple setting CNN templates.
Keywords
- Finite Volume
- Boundary Element Method
- Internal Force
- Finite Volume Method
- Cellular Neural Network
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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7. References
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Zhong, Y., Shirinzadeh, B., Yuan, X., Alici, G., Smith, J. (2006). A Cellular Neural Network for Deformable Object Modelling. In: Information Technology For Balanced Manufacturing Systems. BASYS 2006. IFIP International Federation for Information Processing, vol 220. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36594-7_35
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DOI: https://doi.org/10.1007/978-0-387-36594-7_35
Publisher Name: Springer, Boston, MA
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