Real-time deformation of structure using finite element and neural networks in virtual reality applications

Authors:
Ridha Hambli;Abdessalam Chamekh;Hédi Bel Hadj Salah
Affiliations:
ISTIA, LASQUO, Angers, France;Laboratoire Génie Mécanique, Ecole Nationale d'Ingénieurs de Monastir, Tunisia;Laboratoire Génie Mécanique, Ecole Nationale d'Ingénieurs de Monastir, Tunisia
Venue:
Finite Elements in Analysis and Design
Year:
2006

Citing 6
Cited 2

Multilayer feedforward networks are universal approximators

Neural Networks
Approximation capabilities of multilayer feedforward networks

Neural Networks
3D chainmail: a fast algorithm for deforming volumetric objects

Proceedings of the 1997 symposium on Interactive 3D graphics
Interactive Simulation of Surgical Cuts

PG '00 Proceedings of the 8th Pacific Conference on Computer Graphics and Applications
ArtNova: Touch-Enabled 3D Model Design

VR '02 Proceedings of the IEEE Virtual Reality Conference 2002
Virtual Cutting with Force Feedback

VRAIS '98 Proceedings of the Virtual Reality Annual International Symposium

A physics-driven neural networks-based simulation system (phynness) for multimodal interactive virtual environments involving nonlinear deformable objects

Presence: Teleoperators and Virtual Environments
Meshes simplification based on reverse subdivision

ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an approach for 'virtual' real-time deformation of complex structures is developed. The approach combines the finite element method and Neural Network calculations to allow the user to perform interactive shape changes and view the resultant deformation changes in a virtual environment.A tennis racket and ball are used to illustrate the capability of the proposed method.Because real time deformation simulation is a time consuming repeated analyses, the neural networks are employed in this investigation as numerical devices for substituting the finite element code needed for the tennis racket and ball deformation.The input data for the artificial neural network are a set of parameters generated randomly (ball impact velocity, impact angle and the zone impact). The output data are the deformation of the ball/strings and the impact force-feedback value.The work contribute toward the development of real virtual simulator which uses a haptic device that "feels" the feedback force generated by the deformation.