Wire plus arc additive manufacturing (WAAM) is rapidly growing into a popular and cost-effective technology for manufacturing medium-large complex structured components. WAAM is a novel metal additive manufacturing method that deposits material layer by layer using an electric arc as a heat source, allowing the creation of metal components with high mechanical characteristics. The procedure allows for the construction of a near-net-shape structure at a rapid pace of production (50–130 gm/min) and material utilization efficiency (80–90 %). The present study mainly focuses on the fabrication of the WAAM structure and a detailed investigation of the mechanical and corrosion performance of multi-layer fabricated wall components. The most versatile and widely applicable stainless steel SS316L material is employed to build multi-layered wall structures. The average micro-hardness of the WAAM-built structure along the building direction was found to be 197.43±1.46 HV0.3 and that of the commercially available counterpart was found to be 187.26±1.17 HV0.3. Moreover, the maximum hardness was observed close to the bottom region (201.86±1.44 HV0.3) which further reduces with the building height. The ultimate tensile strength, and yield strength of the WAAM printed wall obtained as 573.81±2.3 MPa, and 275.41±1.1 MPa respectively found to have improved mechanical strength to that of wrought counter-part however the ductility in the form of percent elongation gets slightly reduced. Furthermore, comparable corrosion resistance was observed for the WAAM fabricated wall structure and conventionally manufactured SS316L structure. The bottom region experiences better corrosion resistance compared to the upper region in both saline environment and acidic corrosion solution with an average corrosion rate of 0.0276 mm/yr, and 0.0366 mm/yr respectively.

The present study proposes machining Ti-6Al-4V with graphite nanopowder mixed electric discharge machining (NPMEDM) process using deionized water (6 gm/L) as the dielectric. A comparative analysis is performed with conventional electric discharge machining (EDM) and NPMEDM processes with peak current (IP), pulse on time (TON), and gap voltage (GV) as input parameters. Surface integrity, recast layer thickness (RLT), material removal rate (MRR), chemical analysis, and residual stress are investigated by different characterization techniques such as field emission scanning electron microscopy (FESEM) and x-ray diffraction (XRD). FESEM study reveals that the addition of nano graphite powder leads to a glossy surface finish, lesser micro-cracks, and micro-holes. Recast layer thickness (RLT) improved drastically by 69

Powder mixed EDM is getting popularity due to its improved machining efficiency. The properties of powder particles play an important role in influencing the process. Therefore, the present work is focused on the phenomenon of electrical discharge machining (EDM) process for different dielectric conditions. Experiments on EDM have been performed using plain deionized (DI) water, nonconductive Al2O3 powder mixed DI water, and conductive Al powder mixed DI water as the dielectric. The experimental result shows that the conductive powder mixed EDM process is more stable and is able to generate better surface topography compared to other processes. To explicate the observation, sparking behavior, bubble formation, and interelectrode gap (IEG) for the different dielectric conditions have been investigated using high-speed camera. The average interelectrode gap was measured as 0.05 mm, 0.1015 mm, and 0.1392 mm in conventional EDM, nonconductive alumina powder mixed EDM, and conductive aluminum powder mixed EDM process, respectively. Similarly, mean sizes of bubble diameter in conventional EDM, alumina powder mixed EDM, and aluminum powder mixed EDM are observed as 0.1583 mm, 0.17789 mm, and 0.20621 mm, respectively.

In this experimental study, the multi-objective optimization of responses such as machining speed (MS), and surface roughness average (Ra) during the WEDM process of Nitinol-60 is carried out. Box-Behnken Designs in Response Surface Methodology (RSM) has been followed for the experimental design. The results predict that the highest machining speed and minimum Ra are 2.6218mm/min and 1.6563µm respectively for single-objective optimization and 2.1007mm/min and 1.7072µm for multi-objective optimization using the desirability approach. Monte - Carlo simulation, which is based on random data, was performed to analyze the model for 100,000 runs. The results suggest that the developed models are a good fit. FESEM images were studied for surface integrity and it is observed that at the optimal conditions, the surface defects like micro-cracks and voids decreased considerably

Nanopowder mixed electrical discharge machining (NPMEDM) is a recent development in the non-traditional machining process. In this process, addition of powder into dielectric increases the spark gap between the electrodes resulting in more number of low intense sparks. Therefore, both the material removal rate (MRR) and surface finish improve. The present work investigates the effect of Al2O3 nanopowder mixed EDM oil on various responses like MRR and surface roughness (SR). The Al2O3 nanopowder is mixed with EDM oil at a concentration of 0.5 g/L. Pulse duration (T-on), peak current (IP) and gap voltage (GV) are taken as the process parameters. The experiment is designed using response surface methodology (RSM), where two sets of experiments have been conducted using two different dielectric conditions (i.e. EDM oil and powder mixed in EDM oil). Analysis of variance (ANOVA) shows that all the three selected parameters are significant for MRR and SR. The study shows that there is considerable increase in MRR and reduction in SR after mixing Al2O3 nanopowder in EDM oil. Using field emission scanning electron microscope (FESEM), a detailed study on the surface integrity of the machined surface has been carried out. It has been found that NPMEDM reduced microcracks, microholes, uneven deposit and recast layer thickness of the machined surface to a great extent.

The problem of representation learning on graph can be difficult due to limited knowledge of training data and large presence of missing edges. Real-world social networks do not provide complete information about the network due to hidden information and privacy constraints. In such scenarios, typical representation learning methods are not able to capture network information effectively. In order to make them more useful, any available feature information can be used in addition to the network structure. In this paper, we aim to learn better representations by exploiting both content (or feature) information of nodes and structural information of the network. Our approach leverages generative adversarial networks to learn embedding for generator and discriminator in a minimax game. While the generator estimates the neighborhood of a node, the discriminator distinguishes between the presence or absence of a link for a pair of nodes. We demonstrate the effectiveness of our approach on five real-world publicly available datasets on the problems of link prediction and node classification. On both tasks, we achieve significant gains, outperforming current stateof- the-art methods by considerable margins. Our code is available on Github(1).

In this paper, an attempt is made to explore the possibilities of modifying the dielectric by adding alumina (Al2O3) nanopowder for improving the machining performances. The performance of newly developed nano powder-mixed electrical discharge machining (NPMEDM) process is compared with conventional EDM. Peak current, gap voltage and pulse-on time are taken as considerable process parameters to investigate material removal rate (MRR), surface roughness (SR), recast layer thickness, surface morphology, surface topography and induced residual stress. It is observed that the nanopowder-mixed dielectric medium gives better surface finish and higher metal removal rate as compared to conventional dielectric. The value of MRR increases from 32.75 to 47 mg/min and surface roughness improves from 2.245 to 1.487 μm. Thereafter, atomic force microscopy (AFM) and field emission scanning electron microscopy (FESEM) investigation of the machined surface reveals that presence of micro-crack, micro-hole and uneven deposition decrease substantially during NPMEDM process. Also, induced tensile residual stress on the machined surface significantly reduces in this modified process. Further, the basic mechanism of these processes are investigated by analysing pulse train discharge waveforms and reveals the better sparking stability of NPMEDM process, which results in the higher MRR and better surface quality.

Powder mixed Electrical discharge machining (PMEDM) is a useful advance machining process for processing difficult to cut conductive materials. Inconel 825 is one of such material which is useful in nuclear power plant, petrochemical, chemical, missileindustries and highly corrosive environment. In this study, machinability of Inconel 825 by Graphenenano powder mixed EDM has been investigated. Three responses parameters namely material removal rate (MRR), surface toughness (SR) and tool wear rate (TWR) have been identified to evaluate process performance. Response surface methodology (RSM)has been used to conduct the experiment. It has been observed that MRR, SR and TWR are mostly influenced by peak current (IP), pulse on time (TON) and gap voltage (GV).Further, surface morphology of the generated surface has been investigated by taking FESEM images, which shows a comparatively better surface.

Electrical discharge machining (EDM) process is popular for machining conductive and difficult-to-cut materials, but low material removal rate (MRR) and poor surface quality are major limitations of the process. These limitations can be overcome by adding the suitable powder in the dielectric. The powder particles influence electric field intensity during the EDM process which in turn improve its performance. The size (micro to nano) and properties of the mixed powder also influence the machining efficiency. In this regard, the objective of the present work is to study the performance of EDM process for machining Inconel 825 alloy by mixing Al2O3 nanopowder in deionized water. The experimental investigation revealed that maximum MRR of 47mg/min and minimum SR of 1.487 mu m, which are 44 and 51% higher in comparison to conventional EDM process, respectively, can be achieved by setting optimal combinations of process parameters. To analyze these observed process behavior, pulse-train data of the spark gap were acquired. The discharge waveform identifies the less arcing phenomenon in the modified EDM process compared to conventional EDM. Further, surface-topography of the machined surface was critically examined by capturing field emission scanning electron microscopy and atomic force microscopy images.

Addition of metallic or non-metallic nanoparticles in a base fluid is called nanofluid (NF). In the present paper, nanofluid samples with superior thermos-physical and tribological properties are prepared by mixing of graphene nanoplatelets (GnP) in a water-based emulsion. Response surface methodology (RSM) is used to design the experiments and orthogonal array L27 is selected for experimentation. Furthermore, the machining performance of nanofluids is examined on turning operation of AISI 304 using minimum quantity lubrication (MQL) technique. The analysis of variance (ANOVA) is used to find the contribution of process parameters on the response such as surface roughness and cutting temperature. Results show that higher concentration of graphene nanoparticles plays a significant role in reducing the surface roughness and cutting temperature at higher cutting speed and feed rate, which enhance the performance of machining process

Powder mixed electrical discharge machining (PMEDM) is an advanced machining process. It is used for cutting hard material and for creating intricate shapes. In present work Inconel 825 is used as workpiece due to some of its unique properties like oxidation and corrosive resistance in high pressure and temperature environment as well as it retains its strength at higher temperature. The difficulty in machining it due to its fast work hardening is one of the reasons to use Electrical Discharge Machining (EDM) process is used in which there no direct contact of tool and workpiece so less chances of residual stress. To obtain the efficient output response parameter aluminium oxide (Al2O3) micro powder is mixed with dielectric was used. The input process parameters are gap voltage, pulse on time, powder concentration and peak current. The output process parameters are surface roughness (SR), material removal rate (MRR) and surface integrity. To analyse the surface morphology FESEM image is used. On the basis of application Inconel is used as studs in space shuttle, thrust chamber of rocket engine and engine combustion chamber etc. It has been observed that MRR and SR are directly influenced by the peak current (Ip),pulse on time(Ton) and gap voltage (GV).By using powder in dielectric surface roughness is improved as compare to conventional EDM process.

Powder mixed electrical Discharge Machining (PMEDM) plays vital role in machining process of some very difficult to machine materials. Inconel 825 are highly demanding materials for today's industries due to their unique properties. Properties like high creep resistance under high stress condition, high resistant to corrosion, good machinability, ductility, weldibility, low value of thermal expansion makes it durable for high speed aeroplanes and supersonic jets. In present work Inconel 825 (nickel alloy) which is widely used in aerospace industries, power plants, nuclear power plants, petro chemical, chemical rockets has been used as work piece and machined with using powder mixed electrical discharge machining process (PMEDM). Peak current (IP), gap voltage (GV), powder concentration (PC) and pulse on time (TON) are opted as input experimental parameters. Whereas, surface roughness (SR) and material removal rate (MRR) are considered as output response parameters. Tool used during experiment is tungsten carbide. Tungsten carbide is hard materials in which wear is minimum. It could be concluded from the experiment that there is significant improvement in MRR as well as SR. Tool wear is negligible. Further, surface machined by PMEDM is compared with conventional EDM and analysed using field emission electron microscopy (FESEM) and RSM (response surface methodology). Images obtained by FESEM also confirm improvement in surface integrity. Moreover, less micro-crack, microholes and shallower craters were observed on the surface generated by PMEDM as compare to conventional EDM.

Assessment of groundwater is an effective tool for proper planned and optimal utilisation of water resources in the context of future national requirement and expected impact of climate changed, its variability is critical for relevant national and regional long term development strategies and sustainable development. Our main purpose for the assessment of groundwater in Patna and Gaya district is to compute a complete evaluation of groundwater resources and produce information that can be incorporated for future requirement.The study was undertaken based on the recommendation of groundwater estimation committee, 1997 (GEC-97). Methodology used the estimation of annual groundwater recharge from rainfall and other sources, including irrigation, water bodies and artificial recharge, determination of present status of groundwater utilization and categorization of assessment units based on the level of groundwater utilization and long-term water level trend. Water level fluctuation techniques and empirical norms were used for recharge estimation. The data collected for investigation were water table fluctuation data, rainfall data cropping pattern, number of groundwater structures, hydrogeology of area, specific yield, groundwater draft, pond area etc. The study computes the following result for Patna District and Gaya District respectively. Total annual groundwater recharge is 91924.33 ha-m and 98648.29 ha-m, net annual replenishable groundwater resource is 82731.93 ha-m and 88783.47 ha-m, groundwater draft for all uses is 47328.6 ha-m and 43140.2 ha-m. The net annual groundwater available for future irrigation development is 35403.325 ha-m and 45643.27 ha-m. The stage of groundwater development is 57.20 % and 48.59 % which falls in safe category for both. The study recommended that there is a good scope for future groundwater development and keeping in view of rapid increase in groundwater draft, roof top rainwater harvesting needs to be taken up to recharge the aquifer in Patna and Gaya district particularly in urban areas.

Thirteen diverse genotypes were evaluated to assess the genetic diversity in a randomized block design during 2013–14 for yield and yellow vein mosaic virus (YVMV) incidence in Okra. On the basis of D2 values, the 30 genotypes were clustered into six groups. Cluster II constituted the largest group (11 genotypes) followed by cluster in and cluster VI (5 genotypes each). The cluster IV and V contain 4 genotypes each, whereas only 1 genotypes present in cluster I. The character coefficient of infection alone contributes highest percentage (51%) toward divergence, followed by number of branches per plant (24%), percentage disease incidence (12%). The first six principal components have accounted 84.00% of total variation and percent variation expected were 24.00% (PC1), 19.50% (PC2), 14.30% (PC3), 11.48% (PC4), 7.97% (PC5) and 6.80% (PC6), respectively. The PC1 has positive association with days to first picking, followed by days to first flowering and days to 50% flowering. However, PC1 has negative association for fruits per plant and fruit weight. Therefore, the traits viz., days to first picking, first flowering node and days to first flowering should be given top priority in diverse parent selection for attempting high yielding along with YVMV tolerant hybrids in okra.

Organizations have been under growing pressure to reduce emissions across their supply chains while cutting supply costs to remain competitive. This paper proposes a Carbon Market Sensitive (CMS) and a green decision making approach based on Data Envelopment Analysis (DEA) called CMS–GDEA. It builds on an existing Green DEA model and modifies it to include a carbon market model. Results from the model validation in a well known automobile spare parts manufacturer in India indicate that the “Pay Up” factor from carbon trading adds a new dimension to competition among suppliers and increases overall supply chain profitability. The proposed approach encourages suppliers to go green and cut down their carbon footprints or “Pay Up” to comply with the emission norms along with cutting costs, which adds to healthy competition.

Concept of using solar energy to run vehicles has been introduced many times in many countries. But the stored amount of energy can run vehicle for limited time or only during day times. In the traditional technique of ‘solar powered vehicle’ a motor is drive by battery which is recharged by solar energy. Here, we have introduced a solar charged electromagnetic mechanism, where the both attractive & repulsive effect of two similar dissimilar poles will be responsible for piston’s reciprocation & will produce sufficient shaft power.

The basic DEA model experiences the weights flexibility problem which is resolved by the method of weight restrictions. The current research incorporating Decision Makers' (DMs) preferences into weight restrictions is subject to serious limitations such as lacking a framework for dual role factors and not incorporating organizational hierarchy in decision-making.The proposed Genetic Algorithm (GA) based approach for weight restrictions incorporates a dual role factor and organizational hierarchy in decision-making. The approach involves finding a set of weights which are at a minimum distance from all the DMs' preferences. The approach is flexible and is able to generate a common set of weights and Decision Making Unit (DMU) specific weight restrictions simultaneously.Results from model validation in a well-known automobile spare parts manufacturer in India indicate that the majority of suppliers perceived as highly efficient were actually found to be inefficient in the GA based weight restrictions model.A major contribution of this study is a robust approach to deal with multiple DMs and DEA weights flexibility problem. Another key highlight of the research is translating DMs preferences into a distance function. Using that as a fitness measure within the proposed Evolutionary Algorithms has been done for the first time in the presence of multiple DMs. (C) 2014 Elsevier Ltd. All rights reserved.

With the onset of the ‘climate change movement’, organisations are striving to include environmental criteria into the supplier selection process. This article hybridises a Green Data Envelopment Analysis (GDEA)-based approach with a new Genetic/Immune Strategy for Data Envelopment Analysis (GIS-DEA). A GIS-DEA approach provides a different view to solving multi-criteria decision making problems using data envelopment analysis (DEA) by considering DEA as a multi-objective optimisation problem with efficiency as one objective and proximity of solution to decision makers’ preferences as the other objective. The hybrid approach called GIS-GDEA is applied here to a well-known automobile spare parts manufacturer in India and the results presented. User validation developed based on specific set of criteria suggests that the supplier selection process with GIS-GDEA is more practical than other approaches in a current industrial scenario with multiple decision makers.