Guest Editors
Bharat Kumar Chigilipalli
Vignan’s Institute of Information Technology
Dr. Bharat Kumar Chigilipalli is an associate professor at Vignan’s Institute of Information Technology, Visakhapatnam, with over seven years of academic and research experience in manufacturing technology, additive manufacturing, welding engineering, and machine learning applications. He holds a PhD in manufacturing engineering from NIT Tiruchirappalli, where his research focused on wire arc additive manufacturing (WAAM) of Incoloy 825 and the use of machine learning models to predict material behavior. He also holds dual M.Tech degrees, one in welding engineering from NIT Trichy and another in artificial intelligence and machine learning from Andhra University.Dr. Bharat has published numerous Scopus- and SCI-indexed papers, contributed book chapters with CRC Press, and filed patents in advanced manufacturing technologies. His research integrates AI and smart materials to promote sustainable manufacturing. Recipient of the SASTRA Award for Excellence in Research (2023–24), he actively contributes to academic leadership, innovation councils, and interdisciplinary collaborations in intelligent manufacturing.
Krishna Kishore Mugada
Sardar Vallabhbhai National Institute of Technology
Dr. Krishna Kishore Mugada is an assistant professor at Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat. He earned his B.Tech from VR Siddhartha Engineering College (2010), M.Tech from NIT Jamshedpur (2013), and PhD in solid-state welding and bonding from NIT Warangal (2018). He previously worked as a postdoctoral researcher at Dong-Eui University, South Korea, focusing on machine learning in resistance spot welding, and at IIT Delhi on wire arc additive manufacturing. In 2024, he served as a visiting scientist at the University of Grenoble, France.Dr. Mugada has authored over 30 papers in leading international journals and conferences. His research interests include dissimilar joining, lightweight alloys for energy systems, AI and ML in manufacturing, and advanced characterization. He received the Production Division Subject Award from the Institution of Engineers (India) in 2021 and leads national and international research projects in intelligent and sustainable manufacturing.
Murali Mohan Cheepu
Pukyong National University
Dr. Murali Mohan Cheepu holds a PhD in welding engineering from Kyungsung University, South Korea, and completed postdoctoral research at Pukyong National University. He earned his bachelor’s degree in mechanical and production engineering from Andhra University and a master’s degree from the National Institute of Technology Tiruchirappalli, India.Currently, he serves as a research professor at Pukyong National University. He brings extensive expertise in additive manufacturing, welding automation, and AI-driven manufacturing technologies, complemented by industry experience at JSW Steel Ltd., where he focused on advanced welding methods and innovative production processes.Dr. Cheepu has received several international awards from the American Welding Society, the Korean Welding and Joining Society, IAAM Sweden, and KSPE. He has published over 100 national and international research papers and holds six patents, with more than 3,500 citations to his work.
Kottala Ravi Kumar
King Fahd University of Petroleum and Minerals; SR University
Dr. Kottala Ravi Kumar is a postdoctoral fellow at the Interdisciplinary Research Center for Construction and Building Materials, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, and a postdoctoral fellow in artificial intelligence at SR University, Warangal, India. He earned his PhD in mechanical engineering from the National Institute of Technology Tiruchirappalli and completed a postgraduate program in artificial intelligence at the Indian Institute of Technology Kharagpur.His research expertise includes artificial intelligence, machine learning, renewable energy systems, thermal energy storage, and sustainable materials. Dr. Ravi Kumar has published extensively in reputed international journals and holds several patents in solar and hybrid energy systems. His current work focuses on integrating AI with green energy technologies to develop intelligent, energy-efficient solutions for industrial and environmental applications.
Collection Overview
Artificial intelligence (AI) is increasingly becoming a core enabler for next-generation thermal and manufacturing systems by facilitating data-driven modeling, intelligent control, and autonomous process optimization. This Methods Collection proposes to assemble high-impact experimental research that demonstrates the integration of AI methodologies with thermal engineering and advanced manufacturing, emphasizing reproducibility and protocol-based visual documentation.
The collection will focus on AI-assisted heat transfer analysis, inverse modeling of thermophysical properties, computationally efficient surrogate models for thermal system design, and machine learning-driven parameter optimization for energy-intensive processes. In manufacturing, priority will be given to AI-enhanced machining, additive manufacturing process monitoring, digital twin-supported predictive maintenance, real-time quality inspection using deep learning, and reinforcement learning-based control of manufacturing operations. Experimental works employing computer vision for defect detection, physics-informed neural networks for thermal–fluid problems, and hybrid AI–CFD/FEA frameworks are especially encouraged.
The collection aims to create a high-quality visual repository of standardized, stepwise experimental protocols, sensor data acquisition workflows, AI model training and validation procedures, and deployment strategies for industrial environments. Submissions may include technical demonstrations, experimental case studies, benchmarking studies, and reproducible protocols integrating AI with laboratory or pilot-scale systems. By showcasing technically rigorous, visually documented methodologies, this collection will accelerate AI adoption in thermal and manufacturing research, promote methodological transparency, and support the development of intelligent, sustainable, and high-performance engineering systems.
Articles
A Novel Asymmetric Fifteen-Level Inverter for Medium Voltage Applications
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2026
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Lakshmi Prasanna1, Ch. Nayak Bhukya2, D. Anil Kumar3, Allam Venkatesh2, Katragadda Swarnasri4, Rakesh Bhadani5, Kanaka Raju Kalla6, Abera Eshetu Tadesse7
1Proudhadevaraya Institute of Technology, 2Vignan's Institute of Information Technology, 3Vignan's Foundation for Science, Technology & Research, 4R.V.R. & J.C College of Engineering, Guntur, 5Veerayatan Polytechnic, Mandvi, 6Aditya Institute of Technology and Management, Tekkali, 7Assosa University
Explainable Artificial Intelligence for Thermal Condition Assessment of High-Voltage Power Equipment Using Infrared Imaging
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2026
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Kanaka Raju Kalla1, R. Ramya Swetha2, Mahesh K.3, Ijjada Ramesh3, CH V V Ramana4, Sara Abdelwahab Ghorashi5, Faten Khalid Karim6, Samih M. Mostafa7
1Department of EEE, Aditya Institute of Technology and Management, Tekkali, 2Department of Civil Engineering, Institute of Aeronautical Engineering, 3Department of EEE, Sir M. Visvesvaraya Institute of Technology, 4Vignan's Institute of Information Technology (A), 5Department of Computer Sciences, Princess Nourah bint Abdulrahman University, 6Department of Computer Sciences, Princess Nourah bint Abdulrahman University, 7Computer Science Department, Qena University
Hybrid Deep Learning with Attention-Based Interpretability for PM2.5 Forecasting in Delhi Urban Environments
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2026
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Mahasiva Saravagna Sai Lakshmi1, Gollapudi SVSSVN Rithvik1, CH V V Ramana2, N Thirupathi Rao3, Faten Khalid Karim4, Ayman Aljarbouh5, Samih M. Mostafa6
1Department of AI&DS, Vignan's Institute of Information Technology, 2Vignan's Institute of Information Technology, 3Department of Computer Science and Engineering, Vignan's Institute of Information Technology, 4Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, 5Department of Software Engineering, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, 6Computer Science Department, Faculty of Computers and Information, Qena University
Abstracts
Friction Stir Additive Manufacturing: A Next-Generation Manufacturing Process, Technological Convergence, and Machine Learning for Process Excellence
Venkata Ramana Guntreddi*1
1Department of Electrical, Telecommunication and Computer Engineering, School of Engineering and Applied Sciences, Kampala International University - Western Campus, P.O.Box - 71, Ishaka, Bushenyi, Uganda.
Privacy-Preserving for Electronic Records Using Double Encryption Role- Based Access Control Mechanism
Anilkumar Chunduru*1
1Research Scholar, School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371
Hybrid Neural Network and Kinetic Approaches to Model Thermal Degradation in Composite Thermochemical Energy Storage Materials
Devaraj Naik B1,
Vedharaj Sivasankaralingam2,
Savitri vemireddi3,
B Srinivasarao4,
Kanaka Raju Kalla4,
Sajan Jerome5,
Ravi Kumar Kottala*6,
VenkataRamana Guntreddi*7
1Department of Mechanical Engineering, Annamacharya University Rajampet, 516126, India ,
2Clean Combustion Laboratory, Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli, India,
3Sr.Assistant Professor, Vignan’s Institute of Information Technology (A), Visakhapatnam, Andhra Pradesh-530049, Andhra Pradesh, India,
4Assistant professor, Department of EEE, Aditya Institute of Technology and Management, Tekkali,
5Department of Mechanical Engineering, LBS Institute of Technology for Women, Poojappura, Thiruvananthapuram,
6Department of Data Engineering, M V G R College of Engineering, Vizianagaram, India 535005,
7Department Electrical, Telecommunication and Computer Engineering, School of Engineering and Applied Science, Kampala International University - Western Campus, P.o.Box 71, Bushenyi, Uganda
A Machine-Learning-Aided Attack-Resilient Adaptive LQR for a 3-DOF Articulated Manipulator
VenkataRamana Guntreddi*1
1Department of Electrical, Telecommunication and Computer Engineering, School of Engineering and Applied Sciences, Kampala International University - Western Campus, P.O.Box - 71, Ishaka, Bushenyi, Uganda.
SET-Net: A Hybrid Deep Learning Framework for EEG-Based Attention–Relaxation Classification in Brain–Computer Interface Applications
Ravichander Janapati*1
1SR University
EEG based Emotion Recognition Using Autoencoder-Driven Feature Selection and NSGA-II Optimized ANN
Niharika Gudikandula*1
1SR University, Anathasagar, INDIA
Intelligent Threat Interpretation: An XAI-Backed ML framework for network Intrusion Detection
Salini Yalamanchili *1
1Vignan’s Institute of Information Technology
An Innovative and Explainable AI-based Epileptic Seizure Detection and Severity Classification System using Feature Integration with EEG Signal Source
K Tarakeswara Rao*1
1GITAM (Deemed to be University)
A Review on Open Circuit Failure Analysis of Power Electronic Converters Using AI Techniques
CH NAYAK BHUKYA1
1Viganan's Institute of Information Technology
Fuzzy Logic-Based Active Damping of Dual Active Bridge Converters for Electric Vehicle
Nwosu Ogochukwu Fidelia*1
1Lecturer, Department of Computer and Robotics Education, University of Nigeria, Nigeria.
Real-Time Four-Category Student Engagement Classification from a Single Webcam Using a Hybrid ResNet and Geometric Feature Network
CH V V Ramana*1
1Vignan's Institute of Information Technology