Based on a survey done recently in Japan, 30 percent of the serious accidents occurred in oral implant surgery were concerned with the mandibular canal and 3/4 of them were related to drilling. One of the reasons lies in the lack of the education system. To overcome this problem, a new educational system focusing on drilling the mandibular trabecular bone has been developed mainly for dental college students in the form of an oral implant surgery training simulator that enables student to sense the reaction force during drilling. On the other hand, the conventional system uses polymeric model. Based on these systems, two approaches were proposed; the evaluation by experienced clinicians using the simulator, and experimental works on the polymeric model. Focusing on the combination of the drilling force sensed and drilling speed obtained through both approaches, the results were compared. It was found that the polymeric models were much softer especially near the mandibular canal. In addition, the study gave us an insight of the understanding in bone quality through tactile sensation of the drilling force and speed. Furthermore, the clinicians positively reviewed the simulator as a valid tool.

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The purpose of this work is to simulate uncertainties existing in microscopic field of spherical porous material so that the homogenized property of interest can be predicted with high reliability. Moreover, the final goal is to build a bridge of feedback between microstructure design and fabrication to predict microstructure morphology by limited measurement data of macroscopic property. The uncertainties are identified as parametric variables in constituent material property and nonparametric variables in morphological fluctuation such as disordering and clustering in microstructure. First-order perturbation, based stochastic homogenization (FPSH) method together with mixture distribution technique is employed for probabilistic prediction. Furthermore, the update of prediction is accomplished in the case of an assumed virtual experimental trial. Two numerical examples show that the probabilistic prediction has given a better decision in microstructure design than deterministic prediction. The main conclusion coming from the new method derived by gap between measured data and prediction showed that, when the update is used for morphology prediction of microstructure, it is almost perfect agreement with parameters' setup of virtual experiment. After it is applied for update of probabilistic homogenized property, it could make the updated homogenized property closer to measurement data so that it becomes more realistic.

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