A Study of Added SiC Powder in Kerosene for the Blind Square Hole Machining of CFRP Using Electrical Discharge Machining

Silicon - Carbon Fiber Reinforced Polymers (CFRPs) have been applied potentially for various application components owing to their lightweight and better mechanical properties. However, the...     

Microstructure,Mechanical Properties,and Corrosion Behavior of Co-Based Stellite 6 Multilayer Overlays Deposited on ASTM A36 Steel by Gas Metal Arc Welding Process

Hardfacing is an effective method for restoring the damaged part. Herein, cobalt-based superalloy Stellite-6 is deposited on A36 substrate using gas metal arc welding process. The mechanical properties, microstructure, and corrosion behavior of Stellite-6 overlays on A36 substrate are examined. Defect-free hard overlays are obtained, and a dilution ratio of 19.9% is achieved with weaving technique. X-ray diffraction analysis reveals the presence of M₇C₃, M₂₃C₆, M₆C, and martensite in the Stellite-6 hard overlays. The microstructure of the deposit contains martensite, carbides, and interdendritic precipitates while the mean hardness is 471 HV_0.5. Tensile testing with specimens extracted on the longitudinal (LD) and transverse (TD) orientation of the stellite-6 deposit shows a strength above 950 MPa along with the brittle nature of fracture. Grain growth is epitaxial and shows dendritic morphologies. The higher fraction of high-angle grain boundaries improves the tensile strength of the deposit and interface region. The localized corrosion behavior of stellite-6 hard overlays is found to be excellent, and the corrosion analysis exhibits a corrosion rate of 0.10 mils per year (mpy). The outcomes of the study highlight the feasibility of Stellite-6 hard overlays for extending the service life of A36 steel in chloride environments.     

Disturbance rejection based tracking control design for fuzzy switched systems with time-varying delays and disturbances

The disturbance rejection and tracking problem of T-S fuzzy switched systems with uncertainties, input time-varying delays and disturbances is addressed in this article. For that cause, a modified repetitive control protocol based on the improved-equivalent-input-disturbance (IEID) estimator and extended Smith predictor approach has been proposed, which guarantees the perfect disturbance estimation and tracking performances with high precision. Specifically, by incorporating the transfer function of main feedback path in conventional Smith predictor block, the input time-varying delays are effectively compensated. In the direction of estimating the disturbances, an active disturbance rejection technique called IEID estimator approach is precisely considered. By exploiting the output of the IEID estimator and parallel distributed compensation strategy, the fuzzy rule based modified repetitive control system is formulated. Further, by making use of Lyapunov method together with average dwell-time approach, a set of sufficient conditions in the form of matrix inequalities are established. More respectively, by solving the developed matrix inequalities, the controller and observer gain matrices are determined. At last, the method proposed in this work is validated in terms of presenting the simulation results of two numerical examples, including boiler-turbine system.     

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.