作者: Ya Zhang, Jian Zhong Fu, Zi Chen Chen

摘要: Measurement process has an important impact on the reliability of measurement. The reliability of measurement is weighed by measurement uncertainty. It is very difficult to estimate the uncertainty in the indirect measurements according to the transfer formula given by GUM (Guide to the Expression of Uncertainty in Measurement). Monte Carlo method was proposed to solve the problem of uncertainty estimation and seek suitable measurement process in the indirect measurements. The mathematical relation between the measurand and the direct measures is established firstly. Then Monte Carlo method was adopted to conduct the sampling and synthesis of measurement uncertainty contributors. At last, the measurement method was evaluated and improved according to Procedure for Uncertainty Management, which is given by next generation of GPS (Geometrical Product Specification). Experimental result shows that Monte Carlo Simulation method has a good application foreground in the uncertainty estimation and measurement process design.

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作者: Da Wei Li, Zhen Zhou Lu, Zhang Chun Tang

摘要: An efficient numerical technique, namely the Local Monte Carlo Simulation method, is presented to assess the reliability sensitivity in this paper. Firstly some samples are obtained by the random sampling, then the local domain with a constant probability content corresponding to each sample point can be defined, finally the conditional reliability and reliability sensitivity corresponding to every local region can be calculated by using linear approximation of the limit state function. The reliability and reliability sensitivity can be estimated by the expectation of all the conditional reliability and reliability sensitivity. Three examples testify the applicability, validity and accuracy of the proposed method. The results computed by the Local Monte Carlo Simulation method and the Monte Carlo method are compared, which demonstrates that, without losing precision, the computational cost by the former method is much less than the later.

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作者: Ying Chun Liang, Xing Lei Hu, Jia Xuan Chen

摘要: Monte Carlo (MC) method is adopted to simulate the morphology evolution of machined surface. One specimen is first scratched in MD simulation, and then the machined surface is used in MC simulation. It is found that the atoms stacking on both sides of the groove in the process of nanoscratching have relative obvious migration with time, because these atoms are in high energy, unstable status. The atoms are moved to the minimum energy position by repeated Markov moves. These atoms have an average one-atom-high migration, so quality of machined surface is definitely influenced by the factor of time. To simulate morphology of machined surface by MC simulation is both practical and meaningful.

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