Microstructure and Properties of Aluminum–Graphene–SiC Matrix Composites after Friction Stir Processing

Enhancing the mechanical properties of conventional ceramic particles-reinforced aluminum (Al 1060) metal matrix composites (AMCs) with lower detrimental phases is difficult. In this research work, AMCs are reinforced with graphene nanosheet (GNS) and hybrid reinforcement (GNS combined with 20% SiC, synthesized by shift-speed ball milling (SSBM), and further fabricated by two-pass friction stir processing (FSP). The effect of GNS content and the addition of SiC on the microstructure and mechanical properties of AMCs are studied. The microstructure, elemental, and phase composition of the developed composite are examined using SEM, EDS, and XRD techniques, respectively. Mechanical properties such as hardness, wear, and tensile strength are analyzed. The experimental results show that the GNS and the SiC are fairly distributed in the Al matrix via SSBM, which is beneficial for the mechanical properties of the composites. The maximum tensile strength of the composites is approximately 171.3 MPa in AMCs reinforced by hybrid reinforcements. The tensile strength of the GNS/Al composites increases when the GNS content increases from 0 to 1%, but then reduces with the further increase in GNS content. The hardness increases by 2.3%, 24.9%, 28.9%, and 41.8% when the Al 1060 is reinforced with 0.5, 1, 2% GNS, and a hybrid of SiC and GNS, respectively. The SiC provides further enhancement of the hardness of AMCs reinforced by GNS. The coefficient of friction decreases by about 7%, 13%, and 17% with the reinforcement of 0.5, 1, and 2% GNS, respectively. Hybrid reinforcement has the lowest friction coefficient (0.41). The decreasing friction coefficient contributes to the self-lubrication of GNSs, the reduction in the contact area with the substrate, and the load-bearing ability of ceramic particles. According to this study, the strengthening mechanisms of the composites may be due to thermal mismatch, grain refinement, and Orowan looping. In summary, such hybrid reinforcements effectively improve the mechanical and tribological properties of the composites

A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization