Effect of CNT and TiC hybrid reinforcement on the micro-mechano-tribo behaviour of aluminium matrix composites
Abstract In the present study Aluminium-3003 alloy metal matrix reinforced with single walled CNTs and TiC were fabricated with stir casting process. Aluminium matrix composites found many applications in aerospace and structural engineering. In this study wt.% of CNT is fixed as 0.5 wt.% and TiC content is varied from (0.5 wt.%–2 wt.%) at an interval of 0.5 wt.% Microstructural, Mechanical and Tribological behaviour of composites were investigated. It is found that with increase in reinforcement content uniform dispersion of particles is found. Density of composites is decreased due to volatile nature of reinforcement particles. Hardness of composites is increased with increase in reinforcement content. Wear rate of composites is lower at lower loads and also at higher reinforcement contents.
Automated understanding of spatio-temporal usage patterns of real-world applications are significant in urban planning. With the capability of smartphones collecting various information using inbuilt sensors, the smart city data is enriched with multiple contexts. Whilst tensor factorization has been successfully used to capture latent factors (patterns) exhibited by the real-world datasets, the multifaceted nature of smart city data needs an improved modeling to utilize multiple contexts in sparse condition. Thus, in our ongoing research, we aim to model this data with a novel Context-Aware Nonnegative Coupled Sparse Matrix Tensor (CAN-CSMT) framework which imposes sparsity constraint to learn the true factors in sparse data. We also aim to develop a fast and efficient factorization algorithm to deal with the scalability problem persistent in the state-of-the-art factorization algorithms.