A cellular neural network methodology for deformable object simulation
This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/39838 |
| _version_ | 1848755702878699520 |
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| author | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author_facet | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author_sort | Zhong, Yongmin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extra polating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation. |
| first_indexed | 2025-11-14T09:00:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-39838 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:00:31Z |
| publishDate | 2006 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-398382017-09-13T15:05:23Z A cellular neural network methodology for deformable object simulation Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. haptic feedback Analogy systems (CNNs) cellular neural networks deformation This paper presents a new methodology to simulate soft object deformation by drawing an analogy between a cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by a nonlinear CNN. The novelty of the methodology is that: 1) CNN techniques are established to describe the potential energy distribution of the deformation for extra polating internal forces and 2) nonlinear materials are modeled with nonlinear CNNs rather than geometric nonlinearity. Integration with a haptic device has been achieved for deformable object simulation with force feedback. The proposed methodology not only predicts the typical behaviors of living tissues, but it also accommodates isotropic, anisotropic, and inhomogeneous materials, as well as local and large-range deformation. 2006 Journal Article http://hdl.handle.net/20.500.11937/39838 10.1109/TITB.2006.875679 IEEE restricted |
| spellingShingle | haptic feedback Analogy systems (CNNs) cellular neural networks deformation Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. A cellular neural network methodology for deformable object simulation |
| title | A cellular neural network methodology for deformable object simulation |
| title_full | A cellular neural network methodology for deformable object simulation |
| title_fullStr | A cellular neural network methodology for deformable object simulation |
| title_full_unstemmed | A cellular neural network methodology for deformable object simulation |
| title_short | A cellular neural network methodology for deformable object simulation |
| title_sort | cellular neural network methodology for deformable object simulation |
| topic | haptic feedback Analogy systems (CNNs) cellular neural networks deformation |
| url | http://hdl.handle.net/20.500.11937/39838 |