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In the present study, non-equiatomic nanocrystalline high entropy alloys (Fe, Al, Cr, Mn, and Ni) powder was synthesized using a high-energy planetary ball mill. During milling, a small amount of powder was collected after a different interval of milling time (2 h, 5 h, 10 h, and 20 h). The milled powders after different milling times were characterized using X-ray diffraction (XRD), and Differential Scanning Calorimetry (DSC). The crystallite size of the milled powder was calculated by analyzing XRD results using the Williamson-Hall method. XRD analysis exhibits that the crystallite size of 20 h milled powder particles is in the nano regime. Besides, lattice parameter, micro-strain and dislocation density of the milled powders were calculated. The melting point of milled powder was analyzed using DSC. The characterization of milled powder suggests the successful formation of nanocrystalline high entropy alloys powder using high-energy ball milling.

For photography, remote sensing, aerial surveillance disaster relief and motion picture filmmaking quadcopters are very useful today, and it is essential to find out its limits, so that we can predict in which atmospheric conditions, it works with perfection. And another important factor for quad copter is to know about its effect in order to run smoothly. To overcome these problems, A 3D model of the drone is created on SolidWorks and CFD is performed on Ansys. This simulation results help researchers to see the after effects of different flight condition.

The present study primarily focuses on the synthesis of nanocrystalline (NC) Ni-10Cr-10Al (wt%) alloy powder using a high energy ball mill. The composition of Ni-10Cr-10Al alloy was selected in such a way that the complete Al2O3 layer would form on the outer surface of the NC Ni-Cr-Al alloy at high temperatures. For the synthesis of nanocrystalline alloy, powder metallurgy route was preferred in which the as-received powders of required composition were milled using planetary high energy ball milling for 20 h. The milled powders were collected from the mill at milling time of 0 h, 2 h, 5 h, 10 h, 15 h, and 20 h for studying the effect of milling on the powder. The milled powder was characterized using X-ray diffraction (XRD) and Differential Scanning Calorimetry (DSC). The crystallite size was calculated using the Williamson-Hall method and found to be near about 21 nm. Also, the effect of milling on the lattice parameter, micro-strain, dislocation density developed in the milled powder was analyzed using the XRD results. DSC was used to estimate the melting temperature of Ni-10Cr-10Al alloy. The characterization of milled powder suggests the successful formation of NC Ni-10Cr-10Al alloy powder using high-energy ball milling.

The Inconel alloy is enormously used as machined parts in the automobile and aerospace industries. The optimisation of machining parameters is imperative to strengthen the process performance outcomes concerning the quality and cost of the product. In general, machining is subjected to various process variables specifically feed (f), speed (S), depth of cut (t) and nose radius. The cutting force (Rf), material removal rate (MRR), and surface roughness (Ra) are relevant output performance indices. This paper evaluates the extent of process performance during the machining of Inconel 825 alloy. The development of a nonlinear regression model is done which articulates performance indices as a function of the aforementioned input variables. As a fitness function, the optimisation of the regression model is done employing the Rao1, Rao2, Rao3 JAYA and TLBO algorithm.

The detection of material with high accuracy is a hot research area in material science nowadays. Although, there is significant advancement in modeling new material but there is uncertainty in bringing new material. An intelligent computing system using machine learning algorithms plays an important role in the detection and discovery of new material from concept to reality. The machine learning algorithm can be applied from design phase to material synthesis phase and also in development and validation of new material. This paper deals with image processing, machine learning and material science concept that affect properties, research, problems, challenges and probable solutions to detect and discover material. Different types of filters are utilized for detection of new material. Further, features of materials are extracted by using histogram, wavelet transform and color moments. The k-Nearest Neighbor (k-NN) and multi-Support Vector Machine (SVM) machine learning algorithms are utilized to classify the materials and their performances are also compared. The accuracy obtained from multi-SVM outperforms k-NN.

Engineers, material scientists are always striving to come up with improved performance of existing materials or completely new materials. The continuing quest for the materials with lower cost, higher strength and lighter in weight is the reason behind this. Polymer composites are the results of such extensive research and development in the field of materials. Stringent environmental guidelines by government bodies and global energy crisis have paved the way for natural fiber reinforcement in place of synthetic fiber reinforcement for polymeric composites. Tribological study of these composites is the new research area for researchers. As various mechanical systems fail due to the tribology involved in the system. The focus of this study is on the tribological behavior of natural fiber reinforced epoxy based composites. As epoxy resin is most widely used resin in polymeric composites due to its excellent mechanical properties and ease in processing. Therefore, study focuses only on the works carried out by various researchers in the field of tribology on epoxy based composites. This work will help those researchers which are interested in tribology of natural fibre reinforced epoxy based composites.

The primary objective of today’s manufacturing industries is to manufacture the high-quality products at low cost. With a view to enhance product quality, the selection of machining parameters plays a vital role. The present work assesses the influence of feed rate as well as nose radius over a number of machinability criteria namely machined multiple surface roughness parameters, cutting force, chip thickness ratio, specific cutting energy, and apparent coefficient of friction amid machining of Inconel 825 in dry condition. Turning operation has been executed by means of coated carbide inserts with a constant spindle speed of 371 RPM and depth of cut of 0.8 mm. Results indicated that minimum nose radius insert with low feed provides a better surface finish, whereas sharp nose with low feed generates low resultant machining force. It has been also observed that the chip thickness ratio declines when feed increase, whereas the apparent coefficient of friction upsurges with a rise in feed and nose radius.

Friction Stir Processing (FSP) is an emerging technology which was first developed by using the principle of Friction Stir Welding. FSP has proved itself as a technique for the fabrication of surface composites and increase the mechanical properties of the material. Aluminum alloys are among those materials over which the friction stir processing can be performed successfully. In this study, the existing understanding and the current status of work by Friction stir processing on Aluminum 6000 series alloys are discussed. The main focus is given on the hardness of surface composites.

In the WSN, a huge set of wireless nodes spread to protect a definate field. These nodes are running on smaller batteries which die out soon. These networks provide a backbone support to the things in internet. Clustering has become a way to increase the longevity of its network. This paper proposes a clustering technique based on optimal cluster head selection and using mobile agents for the collection of cluster head data. The proposed protocol is called the Mobile Agent based Improved implemented protocol for mobile agencies. This approach is an enhancement to the I-LEACH already in use. The efficiency of the protocol was compared to the set for live nodes, the set for dead nodes, the throughput and the remaining energy of the system. The suggested protocol has shown improvement against the existing I-LEACH protocol.

In order to avoid, double link failure in the optical network, we proposed a rapid connection recovery scheme for the survivable double link in elastic optical network (EON). The elastic optical network provides very high and flexible bandwidth as per the user’s requirement. Larger network have more number of failure probability. To control such failure, our proposed strategy provides a alternate backup light path by using minimum resources. Our proposed recovery scheme (PRS) provide better results in terms of recovery time, bandwidth blocking probability (BBP), and bandwidth provisioning ratio, when compared with the existing shared path protection (SPP) and dedicated path protection (DPP) schemes. ARPANET and COST239 topologies are considered for simulation.

This investigation describes and presents the analysis of the experimental results on the penetration of AA 7017 plates by high hardness steel projectiles at a velocity of 850±15 m/s at room temperature and at -40°C. From the ballistic testing experiments it is observed that the AA 7017 plates display better ballistic resistance at -40°C than at room temperature. The ballistic testing results have been interpreted in terms of mechanical properties of the material at room temperature and at -40°C. The damage patterns of the plates after ballistic testing have been studied in order to understand the change in ballistic behavior of the material.

The objective of this study is to investigate the effects of different turning parameters like spindle speed (N), feed rate (f) and depth of cut (d) on different output performance parameters measures during dry turning of AISI D2 Steel using PVD coated carbide tool. An experiment has been conducted according to L9 orthogonal array in order to evaluate the turning performance characteristics like material removal rate (MRR) and surface roughness (Ra). An Effort was put to convert the multi responses into equivalent single response i.e. overall grey relation grade by using grey relational analysis. The present study also developed a mathematical model for overall grey relation grade in terms of the machining parameters. Here, improved version of the latest evolutionary algorithm such as harmony search (HS) has been implemented in order to evaluate the optimal machining condition. The results are also compared to genetic algorithm to validate the efficiency of aforesaid approach.

The problem of friction and wear are encountered in almost all the engineering applications. Growing energy consciousness and environment friendliness demands the efficient performance of the mechanical systems. The involvement of corrosive elements further worsens the performance of different materials, especially in sliding contacts. The widespread involvement of machinery in offshore equipments has lead to the development of Ocean Tribology. This field deals with the development and testing of materials under sea water lubrication. A review on the behavior of different materials comprising of metals and metal alloys and polymer composites have been discussed in this paper.

Natural systems have played a vital role in the development of science and technology since the evolution of civilizations. Natural systems exhibit a diverse frictional surface from very low friction in case of lubricated systems to very high friction in case of adhesives and in some cases a unique adaptable frictional characteristic. Recent advancements in green tribology and the global quest for sustainability and energy efficiency have led the researchers to the development of alternative approaches. Tribology being a surface phenomenon, there are indeed many analogies in natural systems wherein friction, wear and lubrication can be a benchmark for the design of new tribological systems. This paper presents a discussion on various tribology related natural systems studied by different researchers. The paper is structured in a manner to focus on all the three parts of tribology viz. Friction, Wear and lubrication. Being an interdisciplinary and vibrant field the authors feel that the current review will serve as a catalyst for its further advancement.

The Hastelloy C-276 alloy in cold rolled and solution annealed condition displays two slopes in true plastic stress - strain curve and follows a Ludwigson relation. The lower values of Ludwigson flow curve parameters such n 1 , n 2 (less negative) and e L indicate less uniform elongations for tensile samples along 45° / 67.5° directions in comparison to those of L, 22.5° and T directions. The relatively low ductility and low work hardening exponent of 45° and 67.5° orientation samples have been explained based on dislocation storage capacity and dynamic recovery coefficient using Kock-Mecking-Estrin analysis.

Present work describes microstructure-texture-mechanical property anisotropy correlation of Ni-16Cr and Ni-16Cr-16Mo alloys in hot rolled and annealed condition. The high and low SFE values associated with Ni-16Cr and Ni-16Cr-16Mo alloys result in partial recrystallization after recovery and presence of twins in recrystallized grains, respectively. Both the alloys display two-slopes in the true plastic stress-strain curves and follow Ludwigson relation. However, the deformation mechanisms of both the alloys are different. Sample orientation dependent ductility of these alloys has been explained based on dislocation storage capacity and dynamic recovery coefficient using Kock-Mecking-Estrin analysis.