K V J Bhargav

@qiscet.edu.in

Head Projects and Research and Mechanical Engineering/ bhargav.k@qiscet.edu.in
QIS College of Engineering and Technology

K V J Bhargav


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https://researchid.co/kvjbhargav

EDUCATION

National Institute of Technology, Rourkela (NIT Rourkela):
 Ph.D., Micromachining & Nanoparticle synthesis (CGPA: 9.10) Rourkela, Odisha, India
Dissertation: Development of a μ-ECDM system with different (Thesis Submitted)
process modes for machining of micro features and nanoparticles synthesis

Jawaharlal Nehru Technological University (JNTUH)
 Master of Technology, (M.Tech), Manufacturing Systems (80.61%) Hyderabad, Telangana, India
Department of Mechanical Engineering (Dual Degree) 2015
Dissertation: Optimization of drilling process parameters on GFRP composites.

Jawaharlal Nehru Technological University (JNTUH)
 Bachelor of Technology, (B.Tech), (74.54%) Hyderabad, Telangana, India
Department of Mechanical Engineering (Dual Degree) 2015
Dissertation: Kinematic Analysis of a parallel manipulator for biomedical applications.

Intermediate (Board of Intermediate Education)MPC 2010
 Sri Chaitanya Junior college (97.2%) Ongole, AP, India

RESEARCH, TEACHING, or OTHER INTERESTS

Mechanical Engineering
17

Scopus Publications

185

Scholar Citations

7

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Examination of Double Balanced Gilbert Cell Mixer Performance RF Design Trade-Offs Perspective
    Subramanyam Avvaru, Murali Anumothu, K. V. J. Bhargav, Netrananda Behera, Ananda Babu Devarapalli, Srinivasulu Rami Reddy
    Lecture Notes in Networks and Systems, 2026
  • Optimization of EDM Process Parameters on Material Removal Rate, Tool Wear Rate, Wear Ratio, and Geometrical Tolerance of Aluminum 7075 Material
    Navaneethakrishnan Gopal, K. V. J. Bhargav, Netrananda Behera, V. S. J. C. Prasad, Siddi Someshwar, Durga Bhavani Guduru
    Lecture Notes in Networks and Systems, 2026
  • Realtime Predictions and Channel Pruning in Bilateral Feature Pyramid Network using Deep Learning Approach in Agricultural Environment
    Kolamala Sumathi, K V J Bhargav, Abdullah Khatib, V Radhamani, M Ramya, V Manasa
    IEEE International Conference on Electronic Systems and Intelligent Computing Icesic 2026 Proceedings, 2026
    Feature Pyramid Network (FPN) is a deep learning architecture that extracts feature maps at different scales, combining high-level semantic and low-level spatial features to enhance object detection accuracy across various sizes through top-down and lateral connections. The research exhibits a Robust Pepper Classification and Prediction using a Bidirectional Fusion Model (RPCPBF) that combines advanced feature extraction, adaptive fusion, and efficient network optimization for the intelligence of the agriculture sector. To address green pepper detection issues, the model incorporates a Bidirectional Enhanced Feature Pyramid Network (BiE-FPN) with enhancement modules for purifying multi-scale features. A CNN – GPR unit leverages spatial-temporal correlations from remote sensing data to predict the crop yield. Plant trait classification faces the challenge of becoming accurate the eccentricity of the plant is among the morphological features that have been extracted. The performance analysis includes loss of 0.04 to 0.025, mAP curve of 0.95, RPCPBF accuracy of 1.8 to 0.017, and RPCPBF loss calculations of 1.3 to 0.01.
  • Empowering Health: The Fusion of AI and Machine Learning in Wearable Technologies
    Yerumbu Nandakishora, S. Prasad Jones Christydass, K. V. J. Bhargav, S. Suresh Kumar
    Smart Textiles and Wearables for Health and Fitness, 2025
    This study investigates the incorporation of machine learning (ML) and artificial intelligence (AI) methods into wearable health technology. Wearable gadgets have become potent instruments for ongoing health monitoring, facilitating the collection and analysis of data in real time. By incorporating AI and ML algorithms, these devices can provide personalized insights, early disease detection, and proactive healthcare management. This chapter reviews recent advancements in AI and ML algorithms applied to wearable health technologies, including activity recognition, vital sign monitoring, and disease prediction. Furthermore, it examines the challenges and opportunities in deploying AI-powered wearable devices, such as data privacy concerns, algorithm optimization, and user acceptance. This study examines the current research and advances to emphasize how AI and ML may revolutionize wearable health technology, making them more effective and accessible. This transformation will ultimately result in improved healthcare outcomes and a higher quality of life.
  • Cucumber Leaf Pests and Diseases Detection Using Deep Learning Architecture with YOLOv5s Target Tracking and Image Enhancement
    K. Malathi, K V J Bhargav, R Sravani, Hebatullah Awwad, T Vijetha, Vijilius Helena Raj
    Proceedings 2025 International Conference on Recent Innovation in Science Engineering and Technology Icriset 2025, 2025
    Detect cucumber leaf diseases and pests early to increase agricultural output and reduce losses. This study presents Cucumber Leaf Pests and Diseases Detection Using Deep Learning Architecture with YOLOv5s Target Tracking and Image Enhancement (CLPDDA) model, a lightweight deep learning framework for real-time identification of common cucumber leaf problems. It uses the YOLOv5s architecture, which is tailored for mobile and edge devices, combining advanced data preprocessing, augmentation techniques, and a solid detection backbone. The focus is on two main threats to cucumber leaves: leaf miner flies and target spot disease. Data augmentation techniques are used to enhance visual diversity. The YOLOv5s architecture boosts feature extraction. Transfer learning was applied with four models, VGG16, ResNet50, ResNet101, and DenseNet201, to evaluate feature extraction capabilities. The CLPDDA model has with over 89% mean average precision and high classification accuracy. The model's small size and high accuracy make it ideal for real-time use in agriculture, identifying cucumber pests and diseases.
  • AURA: An Adaptive Ultra-Reliable Framework for Secure and Energy-Efficient IoT Communications in 5G Smart Cities
    M. Haribabu, K.V.J Bhargav, Jafar Ali Ibrahim. S, N. S. Kalyan Chakravarthy, D. Chaitanya, Reynaldo G. Alvez
    2025 IEEE 3rd Global Conference on Wireless Computing and Networking Gcwcn 2025, 2025
    One of the promising use cases for 5G-based smart cities and metropolises is where large-scale IoT infrastructure has been rapidly developed; however, maintaining security, ultra-reliability, and energy efficiency in such heterogeneous networks is quite challenging. In this paper, we propose AURA (Adaptive Ultra-Reliable Architecture), a lightweight framework to improve secure IoT communications in 5G smart cities. AURA leverages adaptive reliability schemes, post-quantum secure communication protocols and energy-aware resource allocation to overcome scalability and resilience drawbacks in current IoT paradigms. The experimental results show remarkable gains in latency reduction, throughput stability and power management as compared to the baseline architectures. The proposed framework is a future enabler of trusted, green, and ultra-reliable IoT environments in next-generation urban networks.
  • Multi-objective design optimization of hydride hydrogen storage reactor structured with finned helical tubes based on energetic and economic analyses
    A.K. Aadhithiyan, K.V.J. Bhargav, R. Sreeraj, S. Anbarasu
    Journal of Energy Storage, 2023
  • Generation of microchannels on PMMA using an in-house fabricated μ-ECDM system
    Bhargav K. V. J., Balaji P. S., Ranjeet Kumar Sahu
    International Journal of Materials Research, 2023
    Electrochemical corona discharge micromachining (µ-ECDM) is a newly advented, advanced hybrid machining process capable of machining non-conducting and conducting materials. In this article, Polymethyl methacrylate (PMMA), a non-conducting material, often used in microfluidic applications, is machined to generate microchannels. The process parameters chosen for machining are voltage, duty factor, and concentration. The process parameters are chosen at three levels, and their effect on machining characteristics such as material removal rate and surface roughness are detailed in this paper. Optimization is carried out for individual response using the signal to noise ratio optimization technique for maximizing material removal rate and minimizing surface roughness.
  • Micromachining of borosilicate glass using an electrolyte-sonicated-µ-ECDM system
    K. V. J. Bhargav, P. S. Balaji, Ranjeet Kumar Sahu
    Materials and Manufacturing Processes, 2023
    Glass has become an integral part of today’s world. This is because of its wide range of applications owing to its various potential properties. Though it has enormous applications, processing or machining glass is a challenging task. The present study focuses on the generation of microholes on borosilicate glass (thickness: 1000 µm) using an in-house developed in-situ electrolyte-sonicated (ES)-micro electrochemical discharge machining (µ-ECDM), i.e. ES-µ-ECDM system. The experiments revealed that the sonication of electrolytes had increased the electrolyte flushing, which enables the basic µ-ECDM process to push its limits and machine the materials beyond 300 µm (hydrodynamic regime). The process parameters selected for the experimentation are voltage, concentration, and duty factor with sonication of electrolyte at 36 kHz frequency throughout the experiments. Material removal rate (MRR) and overcut (OC) are identified as the machining characteristics in this study. To acquire enhanced machining characteristics, the process parameters are further optimized using the MOJAYA algorithm in conjunction with the R-method which is a multi-attribute decision-making method (MADM). The detailed experimentation revealed that using electrolyte sonication through-holes was achieved at a higher level of parameter settings.
  • Exemplary approach using tool rotation-assisted µ-ECDM for CFRP composites machining
    K. V. J. Bhargav, P. S. Balaji, Ranjeet Kumar Sahu, Jitendra Kumar Katiyar
    Materials and Manufacturing Processes, 2023
    Carbon fiber-reinforced polymer (CFRP) composites are an advanced composite material class due to their remarkable properties such as high load-carrying capacity and low density. CFRP composites have enormous applications in aerospace, biomedical, automobile, etc. Machining the CFRP composite is need of the day, but issues like delamination, fiber pullouts, workpiece damage, etc. have made it difficult. These limitations can be surpassed by the micro-electrochemical corona discharge machining (µ-ECDM) process. Although the process has showcased high process capability and great versatility in machining conducting and non-conducting materials, the process has limitations in machining holes deeper than 300 µm because of insufficient electrolyte supply at the machining zone. Aiding assistance to the process can overcome the limitation by enhancing electrolyte availability. Therefore, an experimental analysis is carried out by generating through holes on the CFRP composite using a tailor-made rotating tool-assisted micro-electrochemical corona discharge machining (RT-µ-ECDM) system. The process parameters, voltage, concentration, duty factor, and tool rotation rate are taken at three levels. The materials removal rate and overcut as machining characteristics were analyzed. The multi-response optimization using JAYA algorithm and R-method is used to obtain the optimal process parameters. The experimental investigation suggests RT-µ-ECDM system can machine through holes on CFRP composite.
  • Micromachining of Al7075 alloy using an in-situ ultrasonicated µ-ECDM system
    K V J Bhargav, Kaushik Raj Pyla, P S Balaji, Ranjeet Kumar Sahu
    Materials and Manufacturing Processes, 2023
  • MOJAYA Coupled with R-method for Optimization of Machining Parameters Used in the Generation of Micro Holes on GFRP Composite Using an In-House Developed µ-ECDM System
    K. V. J. Bhargav, P. Shanthan, P. S. Balaji, Ranjeet Kumar Sahu
    Lecture Notes in Mechanical Engineering, 2023
  • Experimental investigation on machining characteristics of titanium processed using electrolyte sonicated µ-ECDM system
    K. V. J. Bhargav, P. S. Balaji, Ranjeet Kumar Sahu, Moussa Leblouba
    Scientific Reports, 2022
  • Multi-response optimization and effect of tool rotation on micromachining of PMMA using an in-house developed µ-ECDM system
    K.V.J. Bhargav, P.S. Balaji, Ranjeet Kumar Sahu, Jitendra Kumar Katiyar
    CIRP Journal of Manufacturing Science and Technology, 2022
  • Generation of microholes on GFRP composite using ES-µ-ECDM system
    K.V.J. Bhargav, P. Shanthan, P.S. Balaji, Ranjeet Kumar Sahu, Susanta Kumar Sahoo
    CIRP Journal of Manufacturing Science and Technology, 2022
  • Multiphysics Simulation of ECM for the Machining of Al-SiC Composites
    S. Venu, K. V. J. Bhargav, P. S. Balaji
    Lecture Notes on Multidisciplinary Industrial Engineering, 2020
  • Performance of strain gauge in strain measurement and brittle coating technique
    Balaji P. S., Karthik Selva Kumar Karuppasamy, Bhargav K. V. J., Srajan Dalela
    Applications and Techniques for Experimental Stress Analysis, 2019

RECENT SCHOLAR PUBLICATIONS

  • Utilizing Deep Neural Networks for Image Noise Reduction
    KVJ Bhargav, T Pandi, J Rao, P Narendra, YK Krishna, B Lingarao
    Embracing the Digital Horizon: Pioneering Commerce and Management Strategies … , 2026
    2026
  • Hybrid micromachining of GFRP composites using laser-assisted µ-ECDM
    B KVJ
    Materials and Manufacturing Processes 40 (16), 2176-2188 , 2025
    2025
  • Cucumber Leaf Pests and Diseases Detection Using Deep Learning Architecture with YOLOv5s Target Tracking and Image Enhancement
    K Malathi, KVJ Bhargav, R Sravani, H Awwad, T Vijetha, VH Raj
    2025 International Conference on Recent Innovation in Science Engineering … , 2025
    2025
  • Empowering health: The fusion of AI and machine learning in wearable technologies
    Y Nandakishora, SPJ Christydass, KVJ Bhargav, SS Kumar
    Smart textiles and wearables for health and fitness, 159-182 , 2025
    2025
    Citations: 1
  • Examination of Double Balanced Gilbert Cell Mixer Performance RF Design Trade-Offs Perspective
    S Avvaru, M Anumothu, KVJ Bhargav, N Behera, AB Devarapalli, ...
    Proceedings of Eighth International Conference on Information System Design … , 2025
    2025
  • Optimization of EDM Process Parameters on Material Removal Rate, Tool Wear Rate, Wear Ratio, and Geometrical Tolerance of Aluminum 7075 Material
    N Gopal, KVJ Bhargav, N Behera, V Prasad, S Someshwar, DB Guduru
    Proceedings of Eighth International Conference on Information System Design … , 2025
    2025
  • Micromachining of Al7075 alloy using an in-situ ultrasonicated µ-ECDM system
    KVJ Bhargav, KR Pyla, PS Balaji, RK Sahu
    Materials and Manufacturing Processes 38 (13), 1663-1675 , 2023
    2023
    Citations: 14
  • Multi-objective design optimization of hydride hydrogen storage reactor structured with finned helical tubes based on energetic and economic analyses
    AK Aadhithiyan, KVJ Bhargav, R Sreeraj, S Anbarasu
    Journal of Energy Storage 64, 107194 , 2023
    2023
    Citations: 37
  • Generation of microchannels on PMMA using an in-house fabricated μ-ECDM system
    B KVJ, B PS, RK Sahu
    International Journal of Materials Research 114 (4-5), 351-358 , 2023
    2023
  • MOJAYA Coupled with R -method for Optimization of Machining Parameters Used in the Generation of Micro Holes on GFRP Composite Using an In-House …
    KVJ Bhargav, P Shanthan, PS Balaji, RK Sahu
    Advanced Engineering Optimization Through Intelligent Techniques: Select … , 2023
    2023
  • Using an In-House Developed μ-ECDM System
    KVJ Bhargav, P Shanthan, PS Balaji, RK Sahu
    Advanced Engineering Optimization Through Intelligent Techniques: Select … , 2023
    2023
  • Exemplary approach using tool rotation-assisted µ-ECDM for CFRP composites machining
    KVJ Bhargav, PS Balaji, RK Sahu, JK Katiyar
    Materials and Manufacturing Processes 38 (3), 271-283 , 2023
    2023
    Citations: 45
  • Micromachining of borosilicate glass using an electrolyte-sonicated-µ-ECDM system
    KVJ Bhargav, PS Balaji, RK Sahu
    Materials and Manufacturing Processes 38 (1), 64-77 , 2023
    2023
    Citations: 23
  • Development of μ-ECDM System with Different Process Modes for Machining of Micro Features and Nanoparticles Synthesis
    KVJ Bhargav
    2023
  • Experimental investigation on machining characteristics of titanium processed using electrolyte sonicated µ-ECDM system
    KVJ Bhargav, PS Balaji, RK Sahu, M Leblouba
    Scientific Reports 12 (1), 15540 , 2022
    2022
    Citations: 16
  • Generation of microholes on GFRP composite using ES-µ-ECDM system
    KVJ Bhargav, P Shanthan, PS Balaji, RK Sahu, SK Sahoo
    CIRP Journal of Manufacturing Science and Technology 38, 695-705 , 2022
    2022
    Citations: 21
  • Multi-response optimization and effect of tool rotation on micromachining of PMMA using an in-house developed µ-ECDM system
    KVJ Bhargav, PS Balaji, RK Sahu, JK Katiyar
    CIRP Journal of Manufacturing Science and Technology 38, 473-490 , 2022
    2022
    Citations: 28
  • Multiphysics Simulation of ECM for the Machining of AL-SIC Composites
    S Venu, KVJ Bhargav, PS Balaji
    Manufacturing Engineering: Select Proceedings of CPIE 2019, 589-601 , 2020
    2020
  • Performance of Strain Gauge in Strain Measurement and Brittle Coating Technique
    PS Balaji, KSK Karuppasamy, KVJ Bhargav, S Dalela
    Applications and Techniques for Experimental Stress Analysis, 78-90 , 2020
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • Exemplary approach using tool rotation-assisted µ-ECDM for CFRP composites machining
    KVJ Bhargav, PS Balaji, RK Sahu, JK Katiyar
    Materials and Manufacturing Processes 38 (3), 271-283 , 2023
    2023
    Citations: 45
  • Multi-objective design optimization of hydride hydrogen storage reactor structured with finned helical tubes based on energetic and economic analyses
    AK Aadhithiyan, KVJ Bhargav, R Sreeraj, S Anbarasu
    Journal of Energy Storage 64, 107194 , 2023
    2023
    Citations: 37
  • Multi-response optimization and effect of tool rotation on micromachining of PMMA using an in-house developed µ-ECDM system
    KVJ Bhargav, PS Balaji, RK Sahu, JK Katiyar
    CIRP Journal of Manufacturing Science and Technology 38, 473-490 , 2022
    2022
    Citations: 28
  • Micromachining of borosilicate glass using an electrolyte-sonicated-µ-ECDM system
    KVJ Bhargav, PS Balaji, RK Sahu
    Materials and Manufacturing Processes 38 (1), 64-77 , 2023
    2023
    Citations: 23
  • Generation of microholes on GFRP composite using ES-µ-ECDM system
    KVJ Bhargav, P Shanthan, PS Balaji, RK Sahu, SK Sahoo
    CIRP Journal of Manufacturing Science and Technology 38, 695-705 , 2022
    2022
    Citations: 21
  • Experimental investigation on machining characteristics of titanium processed using electrolyte sonicated µ-ECDM system
    KVJ Bhargav, PS Balaji, RK Sahu, M Leblouba
    Scientific Reports 12 (1), 15540 , 2022
    2022
    Citations: 16
  • Micromachining of Al7075 alloy using an in-situ ultrasonicated µ-ECDM system
    KVJ Bhargav, KR Pyla, PS Balaji, RK Sahu
    Materials and Manufacturing Processes 38 (13), 1663-1675 , 2023
    2023
    Citations: 14
  • Empowering health: The fusion of AI and machine learning in wearable technologies
    Y Nandakishora, SPJ Christydass, KVJ Bhargav, SS Kumar
    Smart textiles and wearables for health and fitness, 159-182 , 2025
    2025
    Citations: 1
  • Utilizing Deep Neural Networks for Image Noise Reduction
    KVJ Bhargav, T Pandi, J Rao, P Narendra, YK Krishna, B Lingarao
    Embracing the Digital Horizon: Pioneering Commerce and Management Strategies … , 2026
    2026
  • Hybrid micromachining of GFRP composites using laser-assisted µ-ECDM
    B KVJ
    Materials and Manufacturing Processes 40 (16), 2176-2188 , 2025
    2025
  • Cucumber Leaf Pests and Diseases Detection Using Deep Learning Architecture with YOLOv5s Target Tracking and Image Enhancement
    K Malathi, KVJ Bhargav, R Sravani, H Awwad, T Vijetha, VH Raj
    2025 International Conference on Recent Innovation in Science Engineering … , 2025
    2025
  • Examination of Double Balanced Gilbert Cell Mixer Performance RF Design Trade-Offs Perspective
    S Avvaru, M Anumothu, KVJ Bhargav, N Behera, AB Devarapalli, ...
    Proceedings of Eighth International Conference on Information System Design … , 2025
    2025
  • Optimization of EDM Process Parameters on Material Removal Rate, Tool Wear Rate, Wear Ratio, and Geometrical Tolerance of Aluminum 7075 Material
    N Gopal, KVJ Bhargav, N Behera, V Prasad, S Someshwar, DB Guduru
    Proceedings of Eighth International Conference on Information System Design … , 2025
    2025
  • Generation of microchannels on PMMA using an in-house fabricated μ-ECDM system
    B KVJ, B PS, RK Sahu
    International Journal of Materials Research 114 (4-5), 351-358 , 2023
    2023
  • MOJAYA Coupled with R -method for Optimization of Machining Parameters Used in the Generation of Micro Holes on GFRP Composite Using an In-House …
    KVJ Bhargav, P Shanthan, PS Balaji, RK Sahu
    Advanced Engineering Optimization Through Intelligent Techniques: Select … , 2023
    2023
  • Using an In-House Developed μ-ECDM System
    KVJ Bhargav, P Shanthan, PS Balaji, RK Sahu
    Advanced Engineering Optimization Through Intelligent Techniques: Select … , 2023
    2023
  • Development of μ-ECDM System with Different Process Modes for Machining of Micro Features and Nanoparticles Synthesis
    KVJ Bhargav
    2023
  • Multiphysics Simulation of ECM for the Machining of AL-SIC Composites
    S Venu, KVJ Bhargav, PS Balaji
    Manufacturing Engineering: Select Proceedings of CPIE 2019, 589-601 , 2020
    2020
  • Performance of Strain Gauge in Strain Measurement and Brittle Coating Technique
    PS Balaji, KSK Karuppasamy, KVJ Bhargav, S Dalela
    Applications and Techniques for Experimental Stress Analysis, 78-90 , 2020
    2020