Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
Intelligent transportation system; Machine learning; Parameter optimization; Traffic flow prediction; Urban traffic flow
Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Neelakandan S.; Prakash M.; Bhargava S.; Mohan K.; Robert N.R.; Upadhye S.
Studies in Systems, Decision and Control, Vol-412, pp. 111-127.
Springer Science and Business Media Deutschland GmbH
Neelakandan S., Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India; Prakash M., Data Science and Analytics Center, Karpagam College of Engineering, Coimbatore, India; Bhargava S., Department of Computer Science and Engineering, Poornima College of Engineering, Rajasthan, Jaipur, India; Mohan K., Lecturer Information Technology, University of Technology and Applied Sciences, Shinas, Oman; Robert N.R., Department of Computer Science, Christ University, Bangalore, 560029, India; Upadhye S., Department of Computer Application, Shri Ramdeobaba College of Engineering and Management, Maharashtra, Nagpur, India
Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization
Active distribution network; Artificial rabbits optimization; Photovoltaic generation; Plug-in-electric vehicle fleets; Reliability indices; Soft open points
Global warming, rising fuel prices, and limited conventional fuel supplies are driving the use of renewable energy, battery energy storage, and electric vehicles, transforming traditional electrical distribution networks into active distribution networks. Stochastic technologies can present operational and control challenges, especially for radially configured active distribution networks. In this scenario, strengthening the existing active distribution networks is necessary. This study optimally integrates soft open points for dynamic network reconfiguration to handle uncertainty in active distribution networks. The location, size, and reconfiguration of the soft open points were obtained for the hourly load profile, which included electric vehicle fleet load penetration and PV distributed generation. The proposed multi-objective function uses active power loss, voltage profile, and reliability indices. The proposed multivariable optimization problem was solved using artificial rabbits optimization. The simulations were performed on a modified IEEE 33-bus radial distribution system. The computational efficiency of artificial rabbits optimization is competitive with other prominent algorithms. The proposed approach of optimal soft open points and dynamic network reconfiguration is utilized to cope with uncertainty and run the present active distribution networks with better technical and reliability characteristics. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Janamala V.; Radha Rani K.; Sobha Rani P.; Venkateswarlu A.N.; Inkollu S.R.
Process Integration and Optimization for Sustainability, Vol-7, No. 45689, pp. 419-437.
https://doi.org/10.1007/s41660-022-00304-9 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144969240&doi=10.1007%2fs41660-022-00304-9&partnerID=40&md5=fbd06c6c25cdce8b48d5c0708ab8c3cd
Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Radha Rani K., Department of Electrical and Electronics Engineering, R.V.R & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, Guntur, India; Sobha Rani P., Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Jawaharlal Nehru Technological University, Kakinada (JNTUK), East Godavari, Andhra Pradesh, Kakinada, 533003, India; Venkateswarlu A.N., Department of Electrical and Electronics Engineering, Vignans Lara Institute of Technology and Science, Andhra Pradesh, Guntur, 522213, India; Inkollu S.R., Department of Electrical and Electronics Engineering, Dhanekula Institute of Engineering & Technology, Andhra Pradesh, Vijayawada, 521139, India
Optimising lead qualification through machine learning: A customer data-driven approach
cold leads; decision tree; hot leads; logistic regression; machine learning; supervised ML algorithms
Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. Converting bad client leads can cause businesses to burn through cash reserves. As a result of this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (converted to a customer or not). In this study, an attempt is made to evaluate historical data for leads produced by other businesses in order to train and validate a machine learning (ML)/deep learning (DL) model and test it against real-world characteristics to categorise them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low codeno code libraries, such as PyCaret in Python, and can be used to make predictions regarding probable lead creation, propensity to convert generated leads and optimal actions on the leads by communications teams. Supervised ML algorithms such as logistic regression, decision trees, random forests and other models using a Python library were built to score leads for identifying potential conversions. With good and broad lead-scoring models in place, businesses can optimise their CTI actions on the basis of lead prioritisation and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of this study reveals that 52 per cent of the sample of 74,779 leads are cold leads and 48 per cent are hot leads that are sales qualified. The leads are qualified using the lead score matrix. This method can aid digital businesses to remove unqualified leads and manage leads better, and therefore improve the quality of the leads sent to clients. This, in turn, will improve conversion rates for individual customers. These increased conversion rates will enhance the business strategy of digital marketing firms. Henry Stewart Publications.
Helen J.V.L.; Moorthy V.; Sai C.; Joji B.
Applied Marketing Analytics, Vol-10, No. 3, pp. 255-270.
https://doi.org/10.69554/ETXF7251 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213383861&doi=10.69554%2fETXF7251&partnerID=40&md5=049aa95eff0da5dc89531c56444bf6cb
Helen J.V.L., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India; Moorthy V., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India; Sai C., Enterprise Solution Architect, Airtel Business, India; Joji B., School of Business and Management, Christ University, Kengeri Campus, Mysore Road, Karnataka, Bangalore, 560074, India
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique
Cloud computing; Cloud-sim; Dual fuzzy; Load balancing; Quality of service
Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service. Copyright 2021 Inderscience Enterprises Ltd.
International Journal of Intelligent Enterprise, Vol-8, No. 4, pp. 323-341.
https://doi.org/10.1504/IJIE.2021.117982 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117149700&doi=10.1504%2fIJIE.2021.117982&partnerID=40&md5=a3e29ab8b92a140d6d2d255bc878e347
Thingom C., Centre for Advanced Research and Training, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Ganesh Kumar R., Faculty of Engineering, CHRIST (Deemed to Be University), Karnataka, Bangalore, India
Optimization and Design of a Sustainable Industrial Grid System
Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system. 2022 Samat Iderus et al.
Iderus S.; Peter G.; Praghash K.; Vadde A.R.
Mathematical Problems in Engineering, Vol-2022
Iderus S., CRISD, School of Engineering and Technology, University of Technology Sarawak, Sarawak, Malaysia; Peter G., CRISD, School of Engineering and Technology, University of Technology Sarawak, Sarawak, Malaysia; Praghash K., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Vadde A.R., Department of Electrical and Computer Engineering, Debre Tabor University, Debra Tabor, Ethiopia
Optimization and sensitivity analysis of heat transport of hybrid nanoliquid in an annulus with quadratic Boussinesq approximation and quadratic thermal radiation
The quadratic convective flow of hybrid nanoliquid in an annulus subjected to quadratic thermal radiation is studied for the first time. The impact of suction/injection and the uniform movement of the rings are considered. Nonlinear equations are handled numerically by adopting the shooting technique. An optimization procedure is performed by using response surface methodology. The maximum heat transport is observed for chosen values of effective parameters (thermal radiation parameter (5 ? Rt? 15) , temperature ratio parameter (1.1 ? ?w? 5.1) and nanoparticle volume fraction of copper (1 % ? ?Cu? 3 %)) at three different levels (low(? 1), middle(0) and high(+ 1)). In addition, a slope of the data point is evaluated for the friction coefficient and the Nusselt number. The results showed that the impact of quadratic thermal radiation on velocity and temperature distributions is more significant than linear thermal radiation. Further, an increase in quadratic convection and quadratic thermal radiation leads to an improvement in the friction coefficient of the skin on the inner surface of the outer annulus. Furthermore, the sensitivity of the friction coefficient is positive for the appearance of quadratic thermal radiation. 2020, SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature.
European Physical Journal Plus, Vol-135, No. 6
Thriveni K., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India
Optimization Based Rice Leaf Disease Classification in Federated Learning
Archimedes optimization algorithm; Federated learning; Kalman filter; LeNet; Spotted hyena optimization
Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena Archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Tripathy R.; Mandala J.; Pappu S.R.; Gopisetty G.K.D.
Multimedia Tools and Applications, Vol-83, No. 29, pp. 72491-72517.
All Open Access; Hybrid Gold Open Access
Tripathy R., Chitkara University School of Engineering, Chitkara University, Himachal Pradesh, India; Mandala J., Department of Computer science and engineering, Christ (Deemed to be) University, Bangalore, India; Pappu S.R., Department of Computer Science and Engineering, GITAM (Deemed to be University),Visakhapatnam Campus, Andhra Pradesh, Visakhapatnam, India; Gopisetty G.K.D., Department of Information Technology, KKR & KSR Institute of Technology and Sciences, Affiliated to JNTUK Kakinada, Guntur, India
Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML
Decision Tree Regressor; Ensemble Learning; Gradient Boosting Regressor; Hyperparameter tuning; Outlier Analysis; R-squared Score; Random Forest Regressor
The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. 2024 IEEE.
Upreti K.; Lingareddy N.; Deepika S.; Kumar N.; Parashar J.; Divakaran P.
Proceedings - 2024 1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024, pp. 431-436.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/TIACOMP64125.2024.00078 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212043398&doi=10.1109%2fTIACOMP64125.2024.00078&partnerID=40&md5=80771159abaa3f5e48b09bb5179a808d
Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Delhi NCR, India; Lingareddy N., Vignan Institute of Technology and Science, Department of Cse, Telangana, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India; Kumar N., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Parashar J., Adgitm, Dept. of Computer Science & Engg, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India
Optimization in the Flow of Scientific Newspapers
The evolutions that occurred in the past decades have provoked variations in the market as well as academic and research. Given this scenario, the research explored in this article was aimed to analyze the contribution of the management of PMBOK methods for the optimization of Scientific Editorial Flow. The methodology used presented a quantitative approach, of descriptive character based on a survey, made available on social networks and Facebook groups, through the google forms platform. The sample is given by Snowball, this type of sampling enables the researcher to study specific groups and is difficult to reach. The analysis was by descriptive statistics, using the Likert scale, as well as the weighted average and fashion responses. It was identified that the Critical Success Factors of a Project that can contribute to the optimization of the editorial flow of a Scientific Periodical are efficient communication, empowerment, change management, client involvement, supplier involvement and conflict management. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Mishra P.; Weersma L.; Desai N.; Uppal A.; Sharma S.; Gupta S.K.; Rajsekar D.; Zadnieprovska G.
Lecture Notes in Networks and Systems, Vol-621 LNNS, pp. 33-47.
Mishra P., Sri Balaji University, Pune, India; Weersma L., Centre Christ University, Bengaluru, Brazil; Desai N., Dr D Y Patil Institute of Management Studies, Pune, India; Uppal A., Dr D Y Patil Institute of Management Studies, Pune, India, P P Savani University, Surat, India; Sharma S., DIT University, Dehradun, India; Gupta S.K., AMET University, Chennai, India; Rajsekar D., AMET University, Chennai, India; Zadnieprovska G., V.N. Karazin, Kharkiv National University, Kharkiv, Ukraine
Optimization of Abrasive Wear Parameters of Halloysite Nanotubes Reinforced Silk/Basalt Hybrid Epoxy Composites using Taguchi Approach
Basalt fibre; HNTs; Hybrid nanocomposites; Silk fibre; Taguchi method; Three body abrasive wear
The demand for environmentally friendly and sustainable materials for nonstructural and structural applications grows by the day. Polymeric composites reinforced with fillers and fibres are considered to have increased strength and desirable wear resistance. Abrasive wear of industrial and agricultural based components are currently one of the most serious issue. Therefore, the current research reports on the influence of Halloysite-Nanotubes (HNTs) loading on the three body abrasive behavior of bi-directional silk fibre (SF) and basalt fibre (BF) reinforced epoxy (Ep) composites. Rubber wheel with dry sand abrasion testing in accordance with ASTM G65-16e1 was performed with four control parameters such as filler content, load, abrading distance and silica sand size. The tests were planned as per orthogonal array of Taguchi (L27). Significant impact of control factors were identified using ANOVA (Analysis of variance). The results demonstrated that adding HNTs to SF-BF/Ep nanocomposites significantly improved the wear resistance and the combination of A2, B1, C3 and D1 control factors yields the lower specific wear rate (SWR). Findings exhibit that the load and abrading distance were the most significant parameters affecting the abrasive wear of SF-BF/Ep nanocomposites followed by filler content and silica sand size. Microstructural features were observed via scanning-electron-microscopy (SEM). 2022 Published by Faculty of Engineering.
Tribology in Industry, Vol-44, No. 2, pp. 253-267.
Faculty of Engineering, University of Kragujevac
https://doi.org/10.24874/ti.1131.06.21.08 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132433996&doi=10.24874%2fti.1131.06.21.08&partnerID=40&md5=2b11ec60aa8e1c6af95391ede31fa79a
Darshan S.M., Centre for Composite Material Research, Department of Mechanical Engineering, The National Institute of Engineering, Karnataka, Mysuru, 570008, India, Department of Mechanical Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, 570008, India; Suresha B., Centre for Composite Material Research, Department of Mechanical Engineering, The National Institute of Engineering, Karnataka, Mysuru, 570008, India; Jamadar I.M., Centre for Composite Material Research, Department of Mechanical Engineering, The National Institute of Engineering, Karnataka, Mysuru, 570008, India
Optimization of anti-corrosion performance of novel magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles on Al in simulated acidizing environment using RSM
Ag@PANI-CS-Fe3O4; Corrosion inhibitor; Response surface optimization
The suitability of newly synthesized magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles (Ag@PANI-CS-Fe3O4) as a robust corrosion inhibitor for Aluminum (Al) in a 5 M HCl environment has been investigated via Weight Loss (WL), Alternating Current (AC)-Impedance Spectroscopy (IS), Potentiontiodynamic polarization (Tafel plots), and Scanning Electron Microscopy (SEM) techniques. The protection efficiency (PE) was mathematically modeled using the Response Surface Methodology (RSM) to fit an empirical relation in terms of temperature, nanocomposite concentration, and time using the face-centered central composite design. The model was accurate with a coefficient of determination (R2 = 99.27%). The negative Gibb's free energy of adsorption (?Gads) values confirmed the spontaneity of Freundlich adsorption isotherm process on Al in 5 M HCl solution. The optimization simulation yielded maximum protection efficiency (of 97.88%) at 5 mg/L nanocomposite concentration, 1 h time, and an intermediate temperature of 304.8 K. Furthermore, the sensitivity of PE was evaluated to find that the low temperature 303 K is favorable for PE, whereas higher temperature will act adversely on PE. The results obtained by the RSM model are in agreement with the experimental observations. 2021 Elsevier B.V.
Raghavendra N.; Mahesh R.T.; Mahanthesh B.; Mackolil J.
International Journal of Biological Macromolecules, Vol-195, pp. 329-345.
ISSN: 1418130; PubMed ID: 34902445; CODEN: IJBMD
https://doi.org/10.1016/j.ijbiomac.2021.11.207 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121218537&doi=10.1016%2fj.ijbiomac.2021.11.207&partnerID=40&md5=9139bb39913b8c04b60aceb881ca3c11
Raghavendra N., Department of Chemistry, K.L.E. Society's P. C. Jabin Science College (Autonomous) Vidyanagar, Hubballi, 580031, Karnataka, India; Mahesh R.T., Department of Chemistry, JSS Banashankari Arts, Commerce, and S.K. Gubbli Science College, Vidyagiri, Dharwad, 580003, Karnataka, India; Mahanthesh B., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, Karnataka, India; Mackolil J., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, Karnataka, India
Optimization of Biodiesel Production from Waste Cooking Oil by Box Behnken Design Using Response Surface Methodology
Box-Behnken method; Response Surface Methodology.; Transesterification; Waste cooking oil (WCO)
Interest in Biodiesel production has grown over the years due to concerns related to the environment, and the solutions include deriving energy from waste as the replacement for diesel, a petroleum-derived fuel. Biodiesel has been accepted as a "green fuel" as it is a renewable, non-toxic, safe and biodegradable energy material. The utilisation of waste cooking oil (WCO) by converting it into biodiesel is one of the promising alternatives to diesel. An attempt to optimise the biodiesel production from WCO (a waste material) has been made via this study. The process adopted was Trans-esterification of pretreated WCO, and the optimization of biodiesel production was carried out by Box-Behnken method using a response surface methodology. The variations between the analytical and experimental results were within acceptable limits. The response surface methodology resulted in an optimum yield of 96.88% (analytical), which was validated through an experiment within an acceptable error of 0.58%. 2021,International Journal Of Renewable Energy Research.All rights reserved.
International Journal of Renewable Energy Research, Vol-11, No. 1, pp. 344-354.
Kiran K., Alternative Fuels Research Lab, Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, Karnataka, India; Hebbar G.S., Alternative Fuels Research Lab, Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, Karnataka, India
Optimization of cutting parameters and prediction of surface roughness during hard turning of H13 steel with minimal vegetable oil based cutting fluid application using response surface methodology
The manufacturing industries in modern era are competing to reduce cost of production by employing innovative techniques, one being hard turning. In hard turning process, the work piece is heat treated to the required hardness in the initial stage itself and near net shape is arrived directly by hard turning process. Hard turning reduces manufacturing lead time by excluding the normal cost incurring processes such as, turning, heat treatment, finish grinding etc. In this experimental investigation hard turning process is assisted with minimal cutting fluid application technique, which reduces cutting fluid usage to a minimum of 6-8 ml/min. Soya bean oil based emulsion was used to make the hard turning environment friendly. The oil was prepared by adding additives, which will enhance the desirable properties of the oil for hard turning. Response surface methodology was used for optimization of cutting parameters and for the prediction of surface roughness. A central composite design was implemented to estimate the second-degree polynomial model. The cutting parameters considered for experimentation were cutting speed, feed rate and depth of cut. The surface roughness was considered parameter for prediction. Surface roughness predicted by the response Surface Methodology matched well with the experimental results. Published under licence by IOP Publishing Ltd.
Raj A.; Leo Dev Wins K.; Varadarajan A.S.
IOP Conference Series: Materials Science and Engineering, Vol-577, No. 1
Raj A., Department of Mechanical Engineering, Christ University, Faculty of Engineering, Karnataka, 560074, India; Leo Dev Wins K., Department of Mechanical Engineer Karunya University, Coimbatore, 641114, India; Varadarajan A.S., Nehru College of Engineering and Research, Thrissur, 697573, India
Optimization of Flexible Manufacturing Production Line System Based on Digital Twin
Digital twin; Effectiveness; Manufacturing systems; Optimization; Production capacity
This research presents a revolutionary Digital Twin (DT)driven method aimed at quick customization of computerized industrial processes. The DT includes dual components, the semi-physical replication that transfers system information and gives data input to the subsequent clause, which is enhanced. The outcomes of the optimum section are returned directly to the semi-physical replication used for validation. The term Open-Architecture Machine Tool (OAMT) led to a fundamental class of machine tools that consists of a basic unified platform and many individually designed modules that may be quickly added or replaced away. Designers can dynamically modify the production system for responding to process planning by inserting personalized components into its OAMTs. Major enabling approaches, along with how to identical virtual and substantial systems and how to instantly bi-level program the invention size and efficiency of developed structures to accommodate sudden variations of goods, are explained. A real execution is done to demonstrate the efficacy of the method to achieve increased enactment of the system by minimizing the overhead cost of the recompose method by systematizing and quickly enhancing it. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Ramkumar G.; Misra S.; Babu G.R.; Gottimukkala A.R.; Siddi S.; Kumar J.S.
Ramkumar G., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Karnataka, Bangalore, India; Misra S., Birla Global University, Odisha, Bhubaneswar, India; Babu G.R., Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Nizampet (S.O), Telangana, Hyderabad, 500090, India; Gottimukkala A.R., Department of Computer Science and Engineering, Koneru Lakshmaiah Educationa Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, Guntur, 522302, India; Siddi S., Department of Mathematics, St. Martins Engineering College, Kompally, Telangana, Secunderabad, India; Kumar J.S., Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, India
Optimization of Friction Stir Welding of AlCu Butt Joint Using Taguchi Method
Friction stir welding; Hardened OHNS steel tool; Impact load
In this work, the 5mm thickness of base metals AA6101 and C11000 was welded using a hardened OHNS steel tool by FSW mechanism. The Taguchi method involves the optimization of welding mechanism variables tool rotation speed (rpm), feed rate (mm/min), and tool offset (mm) to gain extremely rigid joints. The ANOVA reveals the percentage contribution of the three welding mechanism variables can be examined. From the Taguchi design of optimization technique, at 1000rpm, 40mm/min, andtool offset towards softer metal will possess maximum impact load. The tools rotating speed produced the greatest contribution to the impact load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Mechanical Engineering, pp. 53-61.
Kumar J.P., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Raj A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India
Optimization of friction stir welding parameters during joining of AA3103 and AA7075 aluminium alloys using Taguchi method
Friction stir welding; H13 tool steel; Taguchi orthogonal array
This paper investigates the optimization of input parameters for the friction stir welding of AA3103 and AA7075 aluminium alloys. The properties of base materials AA3103 are non-heat-treatable alloy, which is having good weldability while AA 7075 is having higher strength. Therefore, the welding of these aluminium alloys will produce superior mechanical properties. Friction stir welding is a rapidly growing welding process which is being widely used in marine, automobile and aerospace industries. Rather than its widespread use, this type of welding has several advantages over normal welding processes like low production of fume, no consumable electrodes are used and can be used in any position. In this paper, optimization of input parameters were conducted based on Taguchi method using the L9 orthogonal array. There were nine experimental runs in total after creating the L9 orthogonal array table in MINITAB software. The input parameters selected for optimization are tool rotation speed, feed rate, tool pin profile the output parameters which are optimized hardness, tensile strength, impact strength. The ANOVA analysis was carried out in the Qualitek 4 software to find out the percentage influence of input parameters on the output parameters. This research work was carried out to find the optimized condition to carry out friction stir welding of above mentioned aluminium alloys. 2021 Elsevier Ltd. All rights reserved.
Raj A.; Pratap Kumar J.; Melwin Rego A.; Sunit Rout I.
Materials Today: Proceedings, Vol-46, pp. 7733-7739.
https://doi.org/10.1016/j.matpr.2021.02.246 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114159811&doi=10.1016%2fj.matpr.2021.02.246&partnerID=40&md5=383d3fcca9ae662e84ce1b17f04a0d1b
Raj A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560074, India; Pratap Kumar J., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560074, India; Melwin Rego A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560074, India; Sunit Rout I., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560074, India
Optimization of Friction Stir Welding Parameters for the Optimum Hardness of AlCu Butt Joints Using the Taguchi Method
Friction stir welding; Hardness; OHNS steel tool
In the present study, the base plates made of alloys AA6101 and C11000 (each 5 mm thick) were welded bythe FSW technique using a hardened OHNS steel weld tool. The percentage contribution of the input process parameters, such as tool rotational speed in rpm, feed rate in mm/min, and tool pin offset in mm, on the output parameter joint hardness, were examined using the experimental design Taguchi L9 and ANOVA numerical tool analysis. From the optimization method, at 1000rpm tool rotational speed, 40mm/min feed rate and weld tool pin toward AA6101 alloy side will have the highest hardness. The tool rotational speed experiences a maximum significant impact on the joint hardness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Kumar J.P.; Raj A.; Rout I.S.; Ravichandran G.; Darshan S.M.; Niranjana S.J.; Vinod S.K.
Lecture Notes in Mechanical Engineering, pp. 63-70.
Kumar J.P., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Raj A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Rout I.S., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Ravichandran G., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Darshan S.M., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Niranjana S.J., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Vinod S.K., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India
Optimization of Friction Stir Welding Parameters Using Taguchi Method for Aerospace Applications
AA3103; AA7075; Aerospace; Components; Friction stir welding; H13 tool steel; Minitab; Taguchi orthogonal array
The current research work investigated the optimization of the input parameters for the friction stir welding of AA3103 and AA7075 aluminum alloys for its applications in aerospace components. Friction stir welding is rapidly growing welding process which is being widely used in aerospace industries due to the added advantage of strong strengths without any residual stresses and minimal weld defects, in addition to its flexibility with respect to the position and direction of welding. Thus, the demand for this type of welding is very high; however, the welding of aluminum alloys is a key aspect for its use in aircraft components, particularly with respect to bracket mounting frames, braces and wing components. Henceforth in the current work, research is focused on optimization of welding of aluminum alloys, viz. AA 3103 and AA 7075; AA 3103 is a non-heat treatable alloy which is having good weldability, while AA 7075 is having higher strength. Therefore, the welding of these aluminum alloys will produce superior mechanical properties. The optimization of input parameters was accomplished in this work based on L9 orthogonal array designed in accordance with Taguchi methodusing which the friction stir welding experiment was conducted. There were nine experimental runs in total after formulating the L9 orthogonal array table in Minitab software. The input parameters which were selected for optimization weretool rotation speed, feed rate, tool pin profile. The output parameters which were optimized were hardness, tensile strength and impact strength. In addition, the microstructure of the fractured surfaces of the friction stir welded joint was analyzed. It was found from the optimization of the process parameters that strong friction stir welded joints for aerospace applications can be produced at an optimized set of parameters of tool rotational speed of 1100rpm, traverse speed of 15mm/min with a FSW tool of triangular pin profile of H13 tool steel material. 2020, Springer Nature Singapore Pte Ltd.
Ramesha K.; Sudersanan P.D.; Santhosh N.; Ravichandran G.; Manjunath N.
Lecture Notes on Multidisciplinary Industrial Engineering, Vol-Part F252, pp. 293-306.
Ramesha K., Department of Mechanical Engineering, Dr. T. Thimmaiah Institute of Technology, Karnataka, Kolar Gold Fields, India; Sudersanan P.D., Department of Mechanical Engineering, Dr. T. Thimmaiah Institute of Technology, Karnataka, Kolar Gold Fields, India; Santhosh N., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Ravichandran G., Department of Mechanical and Automobile Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Manjunath N., Department of Sciences and Humanities, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
Optimization of graded catalyst layer to enhance uniformity of current density and performance of high temperature-polymer electrolyte membrane fuel cell
Catalyst grading; Current distribution; High temperature-polymer electrolyte membrane fuel cell; Mathematical functions; Numerical modeling
The optimal use of catalyst materials is essential to improve the performance, durability and reduce the overall cost of the fuel cell. The present study is related to spatial distributions of current and overpotential for various graded catalyst structures in a high temperature-polymer electrolyte membrane fuel cell (HT-PEMFC). The effect of catalyst gradient across the catalytic layer (CL) thickness and along the channel and their combination on cell performance and catalyst utilization is investigated. The graded catalytic structure comprises two, three, or multiple layers of catalyst distribution. For a total cathode catalyst loading of 0.35 mg/cm2, higher loading near the membrane presents improved cell performance and catalyst utilization due to reduced limitations caused by oxygen and ion diffusions. However, non-uniformity in the current distribution is significantly increased. The increase in the catalyst loading along the reactant flow provides a substantially uniform current density but lower cell performance. The synergy of varying catalytic profiles across the CL thickness and along the cathode flow direction is investigated. The results emphasize the importance of a rational design of cathode structure and mathematical functions as a strategic tool for functional grading of a CL towards improved uniform current distribution and catalyst utilization. 2021 Hydrogen Energy Publications LLC
K.P V.B.; Varghese G.; Joseph T.V.; Chippar P.
International Journal of Hydrogen Energy, Vol-47, No. 6, pp. 4018-4032.
https://doi.org/10.1016/j.ijhydene.2021.11.006 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120079777&doi=10.1016%2fj.ijhydene.2021.11.006&partnerID=40&md5=f931d172b71e471e48311bb659197682
K.P V.B., CHRIST (Deemed to Be University), Bengaluru, 560 029, India; Varghese G., CHRIST (Deemed to Be University), Bengaluru, 560 029, India; Joseph T.V., CHRIST (Deemed to Be University), Bengaluru, 560 029, India; Chippar P., Applied Engineering and Computational Analysis Laboratory, St Joseph Engineering College (Affiliated to Visvesvaraya Technological University, Belagavi), Vamanjoor, Mangaluru, 575 028, India
Optimization of heat transfer in the thermal Marangoni convective flow of a hybrid nanomaterial with sensitivity analysis
exponential heat source; hybrid nanofluid; inclined magnetic field; Marangoni boundary layer flow; O368; response surface methodology (RSM); sensitivity analysis
The heat transfer rate of the thermal Marangoni convective flow of a hybrid nanomaterial is optimized by using the response surface methodology (RSM). The thermal phenomenon is modeled in the presence of a variable inclined magnetic field, thermal radiation, and an exponential heat source. Experimentally estimated values of the thermal conductivity and viscosity of the hybrid nanomaterial are utilized in the calculation. The governing intricate nonlinear problem is treated numerically, and a parametric analysis is carried out by using graphical visualizations. A finite difference-based numerical scheme is utilized in conjunction with the 4-stage Lobatto IIIa formula to solve the nonlinear governing problem. The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The mono and hybrid nanomaterial flow fields are compared. The hybrid nanomaterial possesses enhanced thermal fields for nanoparticle volume fractions less than 2%. The irregular heat source and the thermal radiation enhance the temperature profiles. The high level of the thermal radiation and the low levels of the exponential heat source and the angle of inclination (of the magnetic field) lead to the optimized heat transfer rate (Nux = 7.462 75). 2021, Shanghai University.
Applied Mathematics and Mechanics (English Edition), Vol-42, No. 11, pp. 1663-1674.
Springer Science and Business Media B.V.
Mackolil J., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, Karnataka, 560029, India; Mahanthesh B., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, Karnataka, 560029, India