Development of gas tungsten arc welding using current pulsing technique to preclude chromium carbide precipitation in aerospace-grade alloy 80A

Weldments were produced using gas tungsten arc welding(GTAW) and pulsed current gas tungsten arc welding(PCGTAW) techniques with ERNiCr-3 filler wire. Macro examination revealed that the resultant weldments were free from defects. A refined microstructure was observed in the weldment fabricated through PCGTAW. Scanning electron microscopy(SEM) analysis revealed secondary phases in the grain boundaries. Energy-dispersive X-ray spectroscopy(EDS) analysis revealed that microsegregation of Cr carbide precipitates was completely eradicated through PCGTAW. The microsegregation of Nb precipitates was observed in the GTA and PCGTA weldments. X-ray diffraction(XRD) analysis revealed the existence of M_(23)C?_6 Cr-rich carbide and Ni_8Nb phases in the GTA weldments. By contrast, in the PCGTA weldments, the Ni_8Nb phase was observed. The Cr_2Ti phase was observed in both the GTA and the PCGTA weldments. Tensile tests showed that the strength and ductility of the PCGTA weldments were slightly higher than those of the GTA weldments.

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network.This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms.The efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors.Network management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of services.The topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process effectively.More solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still exists.This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks.The memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor networks.The proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns.Multiple factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision making.The radial basis function network has been used for irrigation prediction.However,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision making.To handle the above mentioned issues,an efficient method for irrigation prediction is used in this work.The factors considered for decision making are soil moisture,temperature,plant height,root depth.The above-mentioned data will be gathered from the sensors that are attached to the cropfield.Sensed data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the cloud.Then the data stored in the cloud will be decrypted key before being given to the deci-sion-making module.Finally,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking module.Thefinal decision regarding the level of water required for cropfields would be taken.Based on this outcome,the water volve opening duration and the level of fertilizers required will be considered.Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey(USGS)database in terms of precision,accuracy,recall,and packet delivery ratio.

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.

Provisioning Intelligent Water Wave Optimization Approach for Underwater Acoustic Wireless Sensor Networks

In the Acoustics channel,it is incredibly challenging to offer data transfer for time-sourced applications in an energy-efficient manner due to higher error rate and propagation delay.Subsequently,conventional re-transmission over any failure generally initiates significantly larger end-to-end delay,and therefore it is not probable for time-based services.Moreover,standard techniques without any re-transmission consume enormous energy.This investigation proposes a novel multi-hop energy-aware transmission-based intelligent water wave optimization strategy.It ensures reduced end-to-end while attaining potential amongst overall energy efficiency end-to-end packet delay.It merges a naturally inspired meta-heuristic approach with multi-hop routing for data packets to reach the destination.The appropriate design of this Meta heuristic-based energy-aware scheme consumes lesser energy than the conventional one-hop transmission strategy without re-transmission.However,there is no hop-by-hop re-transmission facilitated.The proposed model shows only lesser delay than conventional methods with re-transmission.This work facilitates extensive work to carry out the proposed model performance with the MATLAB simulation environment.The results illustrate that the model is exceptionally energyefficient with lesser packet delays.With 500 nodes,the packet delivery ratio of proposed model is 100%,average delay is reduced by 2%,total energy consumption is 8 J,average packet redundancy is 1.856,and idle energy is 6.9Mwh.The proposed model outperforms existing approaches like OSF,AOR,and DMR respectively.

Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

In this work,an Artificial Neural Network(ANN)based technique is suggested for classifying the faults which occur in hybrid power distribution systems.Power,which is generated by the solar and wind energy-based hybrid system,is given to the grid at the Point of Common Coupling(PCC).A boost converter along with perturb and observe(P&O)algorithm is utilized in this system to obtain a constant link voltage.In contrast,the link voltage of the wind energy conversion system(WECS)is retained with the assistance of a Proportional Integral(PI)controller.The grid synchronization is tainted with the assis-tance of the d-q theory.For the analysis of faults like islanding,line-ground,and line-line fault,the ANN is utilized.The voltage signal is observed at the PCC,and the Discrete Wavelet Transform(DWT)is employed to obtain different features.Based on the collected features,the ANN classifies the faults in an effi-cient manner.The simulation is done in MATLAB and the results are also validated through the hardware implementation.Detailed fault analysis is carried out and the results are compared with the existing techniques.Finally,the Total harmonic distortion(THD)is lessened by 4.3%by using the proposed methodology.

Improvement of Microstructure and Mechanical Behavior of Gas Tungsten Arc Weldments of Alloy C-276 by Current Pulsing

Superalloy C-276 is known to be prone to hot cracking during fusion welding by Gas Tungsten Arc method. Microsegregation occurring during cooling of fusion zone with consequent appearance of topologically close-packed phases P and IX has been held responsible for the observed hot cracking. The present work investigated the possibility of suppressing the microsegregation in weldments by resorting to current pulse. Weldments were made by continuous current gas tungsten arc welding and pulsed current gas tungsten arc welding using ERNiCrMo-4 filler wire. The weld joints were studied with respect to microstructure, microsegregation, and mechanical properties. Optical microscopy and scanning electron microscopy were employed to study the microstructure. Energy-Dispersive X-ray Spectroscopy was carried out to evaluate the extent of microsegregation. Tensile testing was carried out to determine the strength and ductility. The results show that the joints fabricated with pulsed current gave rise to narrower welds with practically no heat affected zone, a refined microstructure in the fusion zone, reduced microsegregation, and superior combination of mechanical properties.