Doğrusal olmayan viskoelestik nesnelerin deneysel veriye dayalı parçacık tabanlı modellenmesi

Simulation-based training using Virtual Reality techniques is a promisingalternative to traditional training in minimally invasive surgery. Surgical simulators let thetrainee touch, feel, and manipulate virtual tissues and organs through the same surgical toolhandles used in actual minimally invasive surgery while viewing images of tool-tissueinteractions on a monitor as in real laparoscopic procedures. Developing realistic organforcemodels for simulating soft-tissue behavior is an integral part of a surgical simulator.The particle system approach provides a better solution than mesh-based methods tothe topological changes encountered in simulation of surgical cutting and tearing. Inaddition, they are computationally less expensive and easier to implement than the meshbasedmethods. However, the material coefficients of each individual mesh element shouldbe calculated and fine tuned to integrate the realistic tissue properties into particle models,which is not trivial.This thesis presents an end-to-end solution to realistic particle-based simulation ofnonlinear viscoelastic tissue behavior based on the experimental data collected by a roboticindenter. First, the strain-dependent nonlinear elastic response and time-dependentviscoelastic response of a tissue-like silicon phantom is measured via static loading andstress relaxation experiments performed by a robotic indenter. The collected experimentaldata is used to construct a lumped model of the tissue phantom represented by a nonlinearviscoelastic Maxwell Solid. Then, a 3-dimensional particle-based network is developed tomimic the behavior of the lumped Maxwell model. The material coefficients of theindividual Maxwell elements connecting the particles are estimated through a set of noveloptimization algorithms.