We study the failure mode transition in double-notched and single-notched steel plates under impact loading. For low impact velocities, brittle crack growths is observed while shear bands occur when the speed of the impactor is increased. We employ the meshless reproducing Kernel particle method (RKPM) that is well suited for problems involving large deformation and localization. The key feature of the proposed methodology is that it (1) treats brittle and ductile failure in the same manner and (2) employs the same constitutive model and failure criterion for both brittle and ductile failure. Therefore, cracks and shear bands are both treated as strong discontinuity in the context of the RKPM. Material stability analysis performed at every sampling point determines automatically the failure mode. We demonstrate the validity of the method by comparison to experimental data.
To increase the reliability of cycloidal wheel grinding machines, reduce the failure rate of machine tools, and shorten maintenance times, a reliability modeling method for small-sample fault data is proposed based on the Bootstrap-Bayes method. The mean time between failures (MTBF) of a machine tool generally conforms to a Weibull distribution. Based on the historical fault information and similar fault information, the distribution function for the mean time between failures of a machine tool is determined. The fault information is expanded by the self-help method, and the two-parameter distribution interval of the distribution function is calculated by the Bayesian formula. An example reliability calculation for a cycloid gear grinding machine is given, including the failure analysis and maintenance methods of the gear grinding machine. This approach can also be used to simulate and analyze the reliability of other computer numerical control (CNC) machine tools.
Proper and rapid identification of malfunctions is of premier importance for the safe operation of Nuclear Power Plants (NPP). Many monitoring or/and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid operator to understand system problems, perform trouble-shooting action and reduce human error under serious pressure. However, because no single method is adequate to handle all requirements for diagnostic system, hybrid approaches where different methods work in conjunction to solve parts of the problem interest researchers greatly. In this study, Multilevel Flow Models (MFM) and Artificial Neural Network (ANN) are proposed and employed to develop a fault diagnosis system with the intention of improving the success rate of recognition on the one hand, and improving the understandability of diagnostic process and results on the other hand. Several simulation cases were conducted for evaluating the performance of the proposed diagnosis system. The simulation results validated the effectiveness of the proposed hybrid approach.
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