Cellular neural network based deformation simulation with haptic force feedback
This paper presents a new methodology fordeformable object modelling by drawing an analogybetween cellular neural network (CNN) and elasticdeformation. The potential energy stored in an elasticbody as a result of a deformation caused by anexternal force is propagated among mass points by thenon-line...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/10042 |
| _version_ | 1848746120726970368 |
|---|---|
| author | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author2 | Unknown |
| author_facet | Unknown 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 fordeformable object modelling by drawing an analogybetween cellular neural network (CNN) and elasticdeformation. The potential energy stored in an elasticbody as a result of a deformation caused by anexternal force is propagated among mass points by thenon-linear CNN activity. An improved CNN model isdeveloped for propagating the energy generated bythe external force on the object surface in the naturalmanner of Poisson equation. The proposedmethodology models non-linear materials with nonlinearCNN rather than geometric non-linearity in themost existing deformation methods. It can not onlydeal with large-range deformations, but it can alsoaccommodate isotropic, anisotropic andinhomogeneous materials by simply modifyingconstitutive constants. |
| first_indexed | 2025-11-14T06:28:12Z |
| format | Conference Paper |
| id | curtin-20.500.11937-10042 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:28:12Z |
| publishDate | 2006 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-100422018-03-29T09:05:55Z Cellular neural network based deformation simulation with haptic force feedback Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. Unknown This paper presents a new methodology fordeformable object modelling by drawing an analogybetween cellular neural network (CNN) and elasticdeformation. The potential energy stored in an elasticbody as a result of a deformation caused by anexternal force is propagated among mass points by thenon-linear CNN activity. An improved CNN model isdeveloped for propagating the energy generated bythe external force on the object surface in the naturalmanner of Poisson equation. The proposedmethodology models non-linear materials with nonlinearCNN rather than geometric non-linearity in themost existing deformation methods. It can not onlydeal with large-range deformations, but it can alsoaccommodate isotropic, anisotropic andinhomogeneous materials by simply modifyingconstitutive constants. 2006 Conference Paper http://hdl.handle.net/20.500.11937/10042 10.1109/AMC.2006.1631688 IEEE restricted |
| spellingShingle | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. Cellular neural network based deformation simulation with haptic force feedback |
| title | Cellular neural network based deformation simulation with haptic force feedback |
| title_full | Cellular neural network based deformation simulation with haptic force feedback |
| title_fullStr | Cellular neural network based deformation simulation with haptic force feedback |
| title_full_unstemmed | Cellular neural network based deformation simulation with haptic force feedback |
| title_short | Cellular neural network based deformation simulation with haptic force feedback |
| title_sort | cellular neural network based deformation simulation with haptic force feedback |
| url | http://hdl.handle.net/20.500.11937/10042 |