Effect of Co addition on microstructural and mechanical properties of WC–B4C–SiC composites

In this study, the effect of Co addition on microstructural and mechanical properties of WC-B₄C–SiC composites sintered by spark plasma sintering (SPS) method was investigated. For this purpose, three batches of WC-B₄C–SiC with different contents of Co (10 vol%, 15 vol%, and 20 Vol %) were sintered at 1400 °C. The results of X-ray diffraction (XRD) analysis of the samples indicated the formation of W₂B₅, W₃CoB₃ as well as the remained C phases and unreacted SiC phase. It was observed that by increasing the Co content, the amount of W₂B₅ phase reduces and W₃CoB₃ and C contents increase. Therefore, W₂B₅ peaks were not detected in the sample containing 20vol% Co. Relative density values above 97% were obtained for all the composites. However, a decrease was observed in relative density by increasing the Co content in the composites. The highest flexural strength (510 ± 42 MPa), fracture toughness (10.34 ± 0.82 MPa m^1/2), and hardness (20.63 ± 0.75 GPa) were also obtained for the sample containing 10vol% Co compared to the other samples. In addition, Transgranular fracture of SiC as well as pulling out of W₃CoB₃ and W₂B₅ particles were observed in the fracture surface micrographs of the samples. The presence of micro-cracks in the SiC grains, fracture of W₃CoB₃ grains, and crack deflection was reported as dominant toughening mechanisms.     

Using Fuzzy Cost‐Based FMEA,GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service

This paper proposes an integrated approach to identify, evaluate and improve the potential failures in a service setting. This integrated approach combines Fuzzy cost‐based service‐specific FMEA (FCS‐FMEA), Grey Relational Analysis (GRA) and profitability theory for better prioritization of the service failures by considering cost as an important issue and using the profitability theory in a way that the corrective actions costs are taken into account. Considering profitability with FCS‐FMEA and GRA reduces the losses caused by failure occurrence. Besides, a maximization linear mathematical problem is used to select the best mix of failures to be repaired. We apply our approach to an academic example concerning the potential failures diagnosis of the Internal Medicine service of a hospital located in Seoul, Korea. We applied our approach and solved the associated maximization problem by a commercial solver, producing an optimal solution which indicates the most convenient mix of failures to be repaired by considering available budget. Copyright © 2013 John Wiley & Sons, Ltd.     

A novel multilayer AAA model for integrated applications

Neural Computing and Applications - Nowadays, one of the problems in current authentication, authorization and accounting (AAA) model is lack of accurate roadmap of access management in integrated...     

Prediction of heavy oil viscosity using a radial basis function neural network

Heavy oil and extra heavy oil resources comprise about 75% of petroleum resources. The most important characteristic of heavy oils is their viscosity. Consequently, to extract and prepare these kinds of crude oil for use, great emphasis should be put on viscosity. The present study highlights the application of intelligent model named radial basis function (RBF) network optimized by genetic algorithm for estimation of diluted heavy oil viscosity in presence on kerosene. The input parameters of model were temperature and mass fraction of kerosene. The output of model was viscosity of heavy oil. Genetic algorithm was utilized to optimize the tuning parameters of RBF model. The outcomes of this study showed that the proposed model is accurate in estimation of target data.