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Prediction of Material Removal Rate and Surface Roughness in Hot Air Assisted Hybrid Machining on Soda-Lime-Silica Glass using Regression Analysis and Artificial Neural Network
Hybrid machining is a combination of conventional with the non-conventional process or two non-conventional processes. In the present work, an attempt has been made to combine hot air with a conventional cutting tool to form a novel Hot Air Assisted Hybrid Machining (HAAHM) for the machining of soda-lime-silica glass. The mathematical model for the Material Removal Rate (MRR) and Surface Roughness (Ra) using Regression Analysis (RA) and the Artificial Neural Network (ANN) models has been developed for the grooving process. The deviation of 8.24% and 7.70% were found in the prediction of MRR and Ra by regression analysis and the deviation of 1.89% and 1.70% for MRR and Ra using an artificial neural network model. The deviation between the predicted and the experimental results of both the models are found to be within the permissible limit. Higher predictive capabilities were observed in ANN model than the regression model. However, both models demonstrated good agreement with the MRR of soda-lime-silica glass by this hybrid machining process. 2020, Springer Nature B.V. -
Parametric optimization on hot air assisted hybrid machining of soda-lime glass using Taguchi based grey relational analysis
The present research underlines the development of a hybrid method for the machining of soda-lime glass known as the hot air assisted hybrid machining. It is a combination of conventional machining assisted with the jet of hot air. The influence of process variables such as feed of the cutting tool, flow of hot air, depth of cut, and the air temperature on the material removal rate (MRR) and surface roughness (Ra) applied to the grooving operation have been investigated. The Taguchi orthogonal array L27 was considered to reduce the number of experiments. The ANOVA was used to recognize the major influencing process parameters for the MRR and Ra. The results of ANOVA indicate that the air temperature is the most significant parameter for the objective of maximum MRR and minimum Ra with contributions ratios of 56.91% and 52.68% respectively for the grooving operation on soda-lime glass. The optimal machining parameters for the maximum MRR and minimum Ra were found to be A1B1C3D3 and A1B1C1D3 respectively. The multi-objective optimization was performed using the Taguchi based grey relational analysis (GRA). The optimal level of parameters based on GRA for maximum MRR and minimum Ra was found to be A1B1C3D3. In addition, the material removal process was explained with the help of SEM micrographs. 2021, Springer Nature Switzerland AG. -
Influence of heat treatment and reinforcements on tensile characteristics of aluminium aa 5083/silicon carbide/fly ash composites
The effect of reinforcements and thermal exposure on the tensile properties of aluminium AA 5083silicon carbide (SiC)fly ash composites were studied in the present work. The specimens were fabricated with varying wt.% of fly ash and silicon carbide and subjected to T6 thermal cycle conditions to enhance the properties through precipitation hardening. The analyses of the microstructure and the elemental distribution were carried out using scanning electron microscopic (SEM) images and energy dispersive spectroscopy (EDS). The composite specimens thus subjected to thermal treatment exhibit uniform distribution of the reinforcements, and the energy dispersive spectrum exhibit the presence of Al, Si, Mg, O elements, along with the traces of few other elements. The effects of reinforcements and heat treatment on the tensile properties were investigated through a set of scientifically designed experimental trials. From the investigations, it is observed that the tensile and yield strength increases up to 160?C, beyond which there is a slight reduction in the tensile and yield strength with an increase in temperature (i.e., 200?C). Additionally, the % elongation of the composites decreases substantially with the inclusion of the reinforcements and thermal exposure, leading to an increase in stiffness and elastic modulus of the specimens. The improvement in the strength and elastic modulus of the composites is attributed to a number of factors, i.e., the diffusion mechanism, composition of the reinforcements, heat treatment temperatures, and grain refinement. Further, the optimisation studies and ANN modelling validated the experimental outcomes and provided the training models for the test data with the correlation coefficients for interpolating the results for different sets of parameters, thereby facilitating the fabrication of hybrid composite components for various automotive and aerospace applications. 2021 by the authors. -
Wear and Friction Behaviour of Aluminium Metal Matrix Composite Reinforced with Graphite Nano Particles for Vehicle Structures
In the current research work, AA7050 a marine aluminium alloy was reinforced with the nano graphite particles processed through stir casting technique. The scanning electron microstructure reveals that the nano particles were uniformly distributed over the matrix material and the hardness of the composites increase with raise in weight percentage of Gr particles owing to the Hall-Petch effect. The wear experiments were conducted by varying reinforcement, load, velocity, distance and temperature. The experimental runs were designed using the L25 orthogonal array in which wear, coefficient of friction and worn surface hardness were recorded as response. The wear resistance of the composites increases with raise in the graphite content attributed to the formation of mechanical mixed layer, the wear rate transfer from mild to severe when there swift in temperature from 100C to 150C. The worn surface hardness of the composites was higher than the as cast composites owing to the presence of Fe on the surface confirmed through the EDAX mapping. The composites were optimized using the modified PROMETHEE optimization technique and results revealed that AA7050 reinforced with 8% Gr particles showed best result and recommended for the marine sector. 2024. Carbon Magics Ltd. -
WEAR AND FRICTION BEHAVIOUR OF ALUMINIUM METAL MATRIX COMPOSITE REINFORCED WITH GRAPHITE NANOPARTICLES
In the current research work, AA7050 a marine aluminium alloy was reinforced with the nano-graphite particles, processed through the stir casting technique. The scanning electron microstructure reveals, that the nanoparticles were uniformly distributed over the matrix material and the hardness of the composites increased with a rise in the weight percentage of Gr particles owing to the Hall patch effect. The wear experiments were conducted by varying reinforcement, load, velocity, distance, and temperature, and the experimental runs were designed using the L25 orthogonal array, in which wear, coefficient of friction and worn surface hardness were recorded as a response. The wear resistance of the composites increases with a rise in the graphite content attributed to the formation of a mechanically mixed layer, the wear rate transfers from mild to severe, when there is shift in temperature from 100C to 150C. The worn surface hardness of the composites was higher than those of as-cast composites owing to the presence of Fe on the surface confirmed through the EDAX mapping. The composites were optimized using the modified PROMETHEE optimization technique and the results revealed that AA7050 reinforced with 8% Gr particles showed the best result and was recommended for the marine sector. 2024, Scibulcom Ltd.. All rights reserved. -
Fabrication and Characterization of AA7050 Nano Composites by Enhancing Directional Properties for High Impact Load Applications
The demand for materials with superior strength and impact resistance has driven the exploration of innovative composite materials. In this research, Al 7050 is chosen as the matrix material due to its excellent mechanical properties, whereas SiC and graphene nanoparticles are incorporated to tailor its directional strength characteristics. The fabrication process involves the synthesis of Al7050 nanocomposites through a meticulous blending of nanoparticles with the matrix material. The characterization phase encompasses a comprehensive analysis of various techniques, including scanning electron microscopy, X-ray diffraction, and mechanical testing. The results shows that the directional strength improvements achieved through SiC and graphene nanoparticle reinforcement with Al7050. The tensile strength of the aluminum in the AA7050-7.5g composite rose from 185.3 to 256.1MPa upon the addition of SiC and graphene. The findings of this study contribute to the evolving field of nanocomposite materials, offering insights into the design and development of advanced materials tailored for specific directional strength requirements. The Institution of Engineers (India) 2024. -
A Novel Edge-Based Trust Management System for the Smart City Environment Using Eigenvector Analysis
The proposed Edge-based Trust Management System (E-TMS) uses an Eigenvector-based approach for eliminating the security threats present in the Internet of Things (IoT) enabled smart city environment. In most existing trust management systems, the trust aggregation process completely depends on the direct trust ratings obtained from both legitimate and malicious neighboring IoT devices. E-TMS possesses an edge-assisted two-level trust computation approach for ensuring the malicious free trust evaluation of IoT devices. The E-TMS aims at removing the false contribution on aggregated trust data. It utilizes the properties of the Eigenvector for identifying compromised IoT devices. The Eigenvector Analysis also helps to avoid false detection. The analysis involves a comparison of all the contributed trust data about every single connected device. A spectral matrix will be generated corresponding to the contributions and the received trust will be scaled based on the obtained spectral values. The absolute sum of obtained values will contain only true contributions. The accurate identification of false data will remove the effect of malicious contributions from the final trust value of a connected IoT device. Since the final trust value calculated by the edge node contains only the trustworthy data, the prediction about the malicious nodes will be accurate. Eventually, the performance of E-TMS has been validated. Throughput and network resilience are higher than the existing system. 2022 G. Nagarajan et al. -
Mental health counsellors perceptions on use of technology in counselling
The objectives of the study were: (a) to explore the self-reported knowledge of counsellors about technology in counselling. (b) to understand the flexibility, usage, and openness to integrating the technology services in their practice, and (c) to identify the problems associated with using technology as a process in counselling. Semi-structured interviews of eleven practising counsellors in Bangalore and Chennai, India, recruited through snowball sampling, were used for data collection. The deductive content analysis of the interview transcripts generated seven concepts, each comprising of several categories. The seven concepts were 'attitude', 'strengths', 'weakness', 'suitability', 'skills and training', 'therapeutic alliance', and 'theoretical approaches'. The analysis revealed that the counsellors preferred face-to-face counselling and were not using technology for their mainstream practice, but all were quite aware of the process, the benefits and costs of using different forms of technology. The study revealed that the counsellors were also aware about the target population and mental health issues for online counselling. This study has strong implications for building additional skills and enhancing training for counsellors to use technology in their counselling practice, along with the formulation of legal and ethical policies, certification and licensing, in order to protect both the clients and counsellors. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Energy-Efficient Long-Range Sectored Antenna for Directional Sensor Network Applications
The popularity of Directional Sensor Network (DSN) is increasing due to their improved transmission range, spectral reusability, interference mitigation, and energy efficiency. In this paper, the radio module of the DSN is implemented using an eight-sector antenna array. Two types of sectored antennas, namely the Rectangular Patch Sectored Antenna (RPSA) and the Triangular Patch Sectored Antenna (TPSA), are proposed to operate at frequency of 2.4 GHz ISM band. The RPSA has a half-power beamwidth (HPBW) of 45 and a peak gain of 5.2 dBi, while the TPSA has an HPBW of 48 and a peak gain of 4.16 dBi. The design and performance evaluation of RPSA and TPSA in terms of gain, reflection characteristics (|S11|), and HPBW are conducted using Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA). To demonstrate the concept, the fabricated sectored antennas are connected to MicaZ Wireless Sensor Network (WSN) nodes using an indigenously designed Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard. The performance of the DSNs based on RPSA and TPSA is evaluated using the Cooja simulator and a testbed consisting of MicaZ nodes. The results show that RPSA outperforms TPSA and omnidirectional-based WSNs in terms of power consumption, received signal strength, and packet delivery ratio. 2024 IETE. -
Hybrid area explorationbased mobility-assisted localization with sectored antenna in wireless sensor networks
In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location-based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max-gain and cost-utilitybased frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area explorationbased localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area explorationbased localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna. 2019 John Wiley & Sons, Ltd. -
Impact of Brexit on bond yields and volatility spillover across France, Germany, UK, USA, and India's debt markets
Britain's decision to exit the EU lead to disruptions in global markets. This study investigates the change in the return and volatility spillover pattern due to the repercussions of the Brexit vote between the US, France, the UK, Germany, and India's 10-year government bond yields by applying the VAR and GARCH-BEKK models. The findings demonstrate a substantial rise in the return spillover to India and USA 10-year government bond yields following the Brexit vote compared to the pre-Brexit vote era. In addition, the results showed evidence of unidirectional volatility spillover from India to France, bidirectional volatility spillover between the USA and India, and unidirectional volatility spillover from the UK to India 10-year government bond market post-Brexit vote. However, there was no interconnection between these markets before the Brexit vote. Therefore, the Brexit vote did affect and significantly increased the linkage between the US, France, the UK, and India's 10-year government bond market. The increase in correlation in India-US, India-UK, and India-France's 10-year government bond markets will help predict and have an important implication for hedgers, decision-makers, and portfolio managers if similar political events occur in the future. Sangeetha G. Nagarakatte, Natchimuthu Natchimuthu, 2022. -
Return and volatility spillover between India, UK, USA and European stock markets: The Brexit impact
The 2016 Brexit referendum created potential turmoil in financial markets. The purpose of this study is to examine the impact of the Brexit referendum on the return and volatility spillover between the EU, the UK, and the USA stock markets and the Indian stock market during the pre- and post-Brexit referendum period. The VAR and bivariate GARCH BEKK models were employed. The study results suggest that before the Brexit referendum, Indian stock market returns made no significant return spillover on the other markets. On the contrary, following the referendum, Indian stock returns significantly spilled over to France, Germany, the UK, and the USA stock market returns. The study results also identified a substantial increase in the bidirectional volatility spillover between India-France, India-UK, and India-USA during the post-Brexit referendum period. Therefore, the investors opportunity to invest simultaneously in India, UK, EU, and US stock markets for portfolio diversification is limited. India was affected mainly by its own past shocks before the Brexit referendum. However, after the Brexit referendum, Indian markets are getting more and more integrated with other markets. In order to reap the diversification benefits, a prudent investment strategy will need to be developed in the future, especially during times of economic and political uncertainty and market crisis. Sangeetha G Nagarakatte, Natchimuthu Natchimuthu, 2022 -
Analyzing blockchain-based supply chain resilience strategies: resource-based perspective
Purpose: This research tries to find the blockchain-based resilience strategies that can help the supply chains of micro, small, and medium-sized enterprises (MSMEs) to recover from the disruptions and work effectively in a resource-based view perspective. Design/methodology/approach: Eight broad strategies and 32 sub-strategies are identified from the literature review. Delphi study was carried out, and detailed discussion with 16 experts helped in finalizing these strategies. Further, the best-worst method (BWM) prioritized these strategies. Findings: The findings suggests that building social capital, improving coordination capabilities, sensitivity towards market, flexibility in process and production, reduction in process and lead time,and having a resource efficiency and redundancy are the top strategies on which the top management should focus to overcome the situations of disruptions and enhance performance of MSMEs. Practical implications: The blockchain-based strategies will enable the companies in tracing the products from the company to customers. Further, the customers will be able to identify their manufacturers, the raw materials used in manufacturing, and the life and quality of raw used materials. Altogether the textile industry will become more sensitive toward environmental practices. Originality/value: The previous research has not identified and evaluated the blockchain-based resilience strategies, and therefore this study tries to fill this gap. This study used a smaller sample from the experts, so the results may vary if the larger data set is used and hypothesis testing can be done. 2023, Emerald Publishing Limited. -
The Challenges of Blockchain Technology Adoption in the Agro-based Industries
Blockchain is one of the latest innovations in information technology, bringing a digital revolution to many industries by increasing transparency. But this technology needs to be explored a lot as of now. Agriculture supply chain management distributes agro-based products like vegetables, fruits, pulses, and cereals. This research is conducted to identify the agro-based industries' adoption of blockchain in their supply chain for achieving sustainability. The next step towards sustainable agriculture is primarily seen as blockchain-enabled agriculture. By making supply chains transparent, technology can follow products from the point of manufacture and prevent waste and inefficiency. A structured literature review helped determine the barriers to blockchain adoption in agro-based industries. This research is unique as no survey-based research on blockchain in the agriculture supply chain using structural equation modeling has been found. The seven proposed hypotheses support the blockchain challenges for adoption in agro-based industries. The findings of this study suggest that the blockchain can bring transparency and traceability and will remove the agro-industry inefficiencies. 2022 International Journal of Mathematical, Engineering and Management Sciences. All rights reserved. -
Relationship Between Sustainable Tourism Indicators and the Operational Challenges of the Tourism Business: Empirical Evidence from the Wildlife Resorts of Karnataka, India
Wildlife resorts are one of the most prominent and attractive segments of the accommodation sector. They face many socio-economic and environmental challenges to implement sustainable tourism practices in their daily operations. This study aims at investigating whether there is any relationship between socio-economic and environmental issues, and challenges faced by wildlife resorts in implementing sustainable tourism practices, and the indicators used by the resorts to measure their sustainable tourism practices. The study employs triangulation design to conduct the research. Survey method was employed for identifying the sustainable tourism indicators and personal interviews with resort managers were conducted to identify the operational issues and challenges of wildlife resorts for the implementation of sustainable practices. Based on purposive sampling, sixteen wildlife resorts from the Indian state of Karnataka are selected for the study. Dedoose software is used to conduct mixed method analysis, to compare and analyse the data obtained from both qualitative and quantitative methods. Copyright 2022, IGI Global. -
Local community involvement in wildlife resorts: Issues and Challenges
The Global Code of Ethics for Tourism Article 5 states that tourism should be a beneficial activity for host countries and communities (UNWTO). The code also emphasises on equitable distribution (between host countries and communities) of the economic and sociocultural benefits generated by tourism activities. The tourism resorts and accommodation sector have to involve local communities in socio-economic activities and priority should be given to local manpower. A wildlife resort has vast opportunities to involve local communities in their day to day operation by purchasing local products, promoting local festivals, providing employment opportunities to locals, and involving local communities in decision-making. Wildlife resorts can also promote local culture, create environment awareness among local people, provide educational support to the local children, and support development of infrastructure and medical facilities for the locals. Though local communities can be involved in various activities of wildlife resorts, it is essential to address the issues and challenges that hinder wildlife resorts from doing so. This paper attempts to determine the issues and challenges faced by wildlife resorts in involving local communities in their day to day operations and suggests ways and means to overcome those challenges. The scope of the study covered selected wildlife resorts in Karnataka. The targeted respondents of the research survey were resort managers and data were collected using open-ended questions to understand real-time issues and challenges involving local communities in resort activities. The data were then analysed using thematic text analysis. The findings from the study will help explore means of providing a better framework which will help wildlife resorts overcome issues and challenges involving local communities. The Author(s) 2017. -
Machine Learning Technology-Based Heart Disease Detection Models
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al. -
Bio-Inspired Energy Storage Electrode: Utilizing Co3O4 Hollow Spheres Derived from Sugarcane Bagasse Extract Synthesis Via Hydrothermal Route
Recent research has explored the utilization of sugarcane bagasse, a bio-industrial waste, to fabricate energy storage devices due to ecofriendly nature, low cost with industrial scale production. In this investigation, cobalt oxide hollow spheres (Co3O4 HSs) were synthesized from waste sugarcane bagasse extract with the carbon spheres (CSs) act as template. The main component of sucrose (C12H22O11) linked with cellulose fibers and other oxygenic functional groups were used to prepare CSs. Previously, a metal precursor (Co(NO3)2.6H2O) was mixed with sugarcane bagasse extract and subjected to a hydrothermal process, resulting in uniform-sized metal CSs. The uniform sized Co3O4 HSs were formed by calcined metal CSs. The calcination temperature plays a crucial role to eliminating implanted carbon material on inter surface area of the metal oxide, shaping the Co3O4 HSs. Structural, vibrational, morphology and elemental analyses were confirmed by X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), respectively. Electrochemical tests show improved ion transport and low resistance, leading to high capacitance in asymmetric supercapacitor (ASC) devices. Subsequently, for asymmetric supercapacitor (ASC) devices, using with Co3O4 HSs has function of cathode and activated carbon (AC) as anode, the devices demonstrated impressive results of 33.1 Fg? 1 at 1 Ag? 1, 86.8% retention after 4,000 cycles, as well as the energy density and power density of 5.9W h kg? 1 at 1500W kg? 1. The Co3O4 HSs||AC device exhibits promising energy storage properties for future applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Comparative electrochemical investigation for scheelite structured metals tungstate (MWO4 (M = Ni, Cu and Co)) nanocubes for high dense supercapacitors application
Scheelite structured metal tungstate MWO4 (M = Ni, Cu and Co) nanocubes were synthesized through the chemical reflux for supercapacitors application and ceyltrimethylammonium bromide (C-TAB) as surfactant. In X-ray diffraction (XRD) result are fit with relevant JCPDS cards, synthesized materials are closely matched with monoclinic and triclinic crystal phase corresponding to NiWO4, CoWO4 and CuWO4 with Scheelite type structure. To resist the growth of the particles and succeeding nanocubes morphology were achieving by using PEG-400 and C-TAB act as a surfactant. The prepared modified electrodes were examined electrochemical analysis after successive coating of working material in empty Ni foil. From the galvanostatic charge-discharge (GCD) comparative analysis, fast ions movements are interacts through the aqueous electrolyte medium with nanocubes NiWO4 electrode are achieving specific capacitance of 1185 Fg?1 at 0.5 Ag?1 and cyclic stability 93.084 % (retentivity) formerly compare to CuWO4 and CoWO4 electrodes. 2023 -
Production of gymnemic acid from cell suspension cultures of gymnema sylvestre /
Protocols For In Vitro Cultures And Secondary Metabolite Analysis Of Aromatic And Medicinal Plants, Part of the Methods in Molecular Biology book series (MIMB,volume 1391), pp.229–239; 2nd ed.