Yadaiah Nirsanametla

Dr. Yadaiah Nirsanametla is a distinguished researcher and academic with a profound background in mechanical engineering, focusing on cutting-edge areas such as laser-based additive manufacturing, laser material processing, and computational welding mechanics. With over 50 published research papers in high-impact international journals and conferences and close to 750 citations, Dr. Nirsanametla has established himself as a leading voice in advanced manufacturing technologies.
As a highly organized and detail-oriented professional, he has successfully overseen the completion of multiple Ph.D. and M.Tech. projects, contributing significantly to the academic and industrial discourse. He has also been involved in the organization of prestigious international conferences and workshops.

EDUCATION

Ph.D. in Mechanical Engineering Department, Indian Institute of Technology (IIT) Guwahati, Guwahati, India, in 2015.

RESEARCH, TEACHING, or OTHER INTERESTS

Mechanical Engineering, Industrial and Manufacturing Engineering, Mechanics of Materials, Multidisciplinary

Scopus Publications

Impact of Tool Plunge Depth, Tool Tilt Angle, and Tool Offset on Microstructure, Mechanical Properties, and Fracture Morphology of Friction Stir Welded Pure Copper Butt Joints
Puspendu Chandra Chandra, Arpan Kumar Mondal, Yadaiah Nirsanametla, Sohini Chowdhury, and Barun Halder
Springer Science and Business Media LLC



Investigating the Synergistic Effects of Hybrid Nanofillers in Polymer Matrix Nanocomposites for Superior Mechanical and Electrical Performance
Mahesh Bhong, Yadaiah Nirsanametla, Jitendra Gudainiyan, Rahul Kumar, Pravin P. Patil, Vijay Kumar Yadav, and Akhil Sankhyan
EDP Sciences
This research examines the synergistic impacts of hybrid nanofillers, particularly silica nanoparticles (SiO2) and multi-walled carbon nanotubes (MWCNTs), in polyethene (PE) network nanocomposites. The nanocomposites are methodically arranged and characterized for predominant mechanical and electrical execution. Tensile tests uncover a significant upgrade in mechanical properties, with test C showing a tensile quality of 83.2 MPa, flexible modulus of 3.6 GPa, and stretching at a break of 11.8%. Electrical conductivity estimations demonstrate an outstanding change, with test C coming to 1.1×10 −4 S/m Comparative investigation with related works exhibits the competitive points of interest of the crossover nanocomposites, adjusting with later improvements within the field. Morphological examination through checking and transmission electron microscopy affirms the successful scattering and interconnectivity of cross-breed nanofillers inside the polymer network. Affectability examinations emphasize the significance of preparing parameters in fitting nanocomposite properties, whereas recreation studies give hypothetical bits of knowledge into microstructural angles impacting by and large execution. This study contributes to the advancing scene of hybrid nanocomposite materials, advertising a promising road for the improvement of progressed materials with improved multifunctionality.


Towards a Carbon Neutral Future: Integrating Renewable Sources and Energy Storage in Sustainable Energy Solutions
Rahul Singh, Ravindra Pratap Singh, Yadaiah Nirsanametla, Brijesh Prasad, Anurag Shrivastava, Arun Pratap Srivastava, and Amit Srivastava
EDP Sciences
This research examines the way to a carbon-neutral future by looking at the integration of renewable vitality sources and vitality capacity advances in feasible energy arrangements. Through a multidisciplinary approach, enveloping information collection, numerical modelling, and scenario investigations, the study investigates the flow of transitioning vitality frameworks. The optimization of energy capacity capacities is educated by scientific models, uncovering that as renewable infiltration increments (20%, 40%, and 60%), optimal capacity capacities rise correspondingly (300 MWh, 700 MWh, and 1200 MWh). Situation investigations illustrate that higher renewable entrance and appropriately measured energy capacity capacities lead to significant diminishments in CO2 outflows (25%, 45%, and 65%) while keeping up positive financial reasonability. Sensitivity investigations confirm the vigour of the models, showing the versatility to varieties in key parameters such as renewable asset accessibility, energy capacity productivity, and capital costs. A comparative investigation against related work underscores the competitiveness and uniqueness of the proposed approach, emphasizing the noteworthiness of numerical modelling in optimizing energy frameworks. This research contributes profitable insights for policymakers, industry partners, and analysts committed to exploring the complexities of accomplishing a carbon-neutral future. The discoveries displayed here, together with those from different studies crossing worldwide vitality exchange, green hydrogen production, and urban arranging, collectively contribute to the broader discourse on economic energy move.


Renewable Energy Integration for Urban Sustainability A Nanomaterial Perspective
Mahesh Bhong, Rahul Singh, Pradeep Kumar Singh, Yadaiah Nirsanametla, Rajesh Prasad Verma, Manish Saraswat, and Amit Srivastava
EDP Sciences
This research explores the transformative part of nanomaterials in progressing urban maintainability through the integration of renewable vitality frameworks. Synthesized quantum dabs, carbon nanotubes, and graphene were characterized and connected over assorted applications, counting solar vitality saddling, wind vitality improvement, vitality capacity, and urban foundation improvement. In solar cells, the integration of quantum specks resulted in an eminent increment in control transformation proficiency (PCE), with an 85% change in short-circuit current thickness (J sc) and a 20% increment in open-circuit voltage (Voc). Wind turbine edges upgraded with carbon nanotubes displayed a momentous 21% rise in control yield and a 40% advancement in soundness, emphasizing the potential of nanomaterials in optimizing wind vitality frameworks. Graphene-based supercapacitors illustrated a multiplied particular capacitance and a 10% increment in cyclic solidness, underscoring the adequacy of nanomaterials in vitality capacity applications. The consolidation of nanocomposite building materials showcased a 44% diminishment in warm conductivity, contributing to made strides cover for maintainable urban foundations. Nanosensors, coordinated into smart frameworks, showed a prevalent 80% increment in affectability and a 50% lessening in reaction time compared to customary sensors.


Influence of surface-active elements on GTA welds with respect to metallographic analysis and temperature distribution
Anil Kumar Deepati, Sohini Chowdhury, Nabam Teyi, Yadaiah Nirsanametla, Chander Prakash, Kuldeep Kumar Saxena, and Sandeep Kumar
Springer Science and Business Media LLC



A Multifaceted Approach: Investigating Engineered Nanoparticle Inhalation in Infants Based on Nano Science
, J. Gudainiyan, R. Thakur, , Y. Nirsanametla, , A. Raturi, , A. P. Srivastava, ,et al.
Sumy State University



The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects
Irsa Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Mohammad Ali, Azka Mir, Mehedi Masud, and Yadaiah Nirsanametla
IEEE
Differentiated thyroid cancer originates in the thyroid gland, which is positioned in the front of the neck. The thyroid gland generates hormones that control metabolism, heart rate, and other bodily functions. Differentiated thyroid carcinoma (DTC) recurrence is a major challenge in clinical therapy. Early detection and treatment play an important role in reducing the impact of thyroid cancer recurrence. The development of precise prediction algorithms is demanding. The prognosis, diagnosis, and treatment of differentiated thyroid carcinoma have been the subject of extensive investigation. Scholars have investigated diverse methodologies to forecast the likelihood of getting thyroid cancer, refine early identification techniques, and augment therapeutic results. Machine learning (ML) frameworks have emerged as useful tools in this setting, with the potential to improve prediction accuracy and patient outcomes. This paper provides a detailed evaluation of the current state and future directions of DTC recurrence prediction using machine learning. We examine current advances in machine learning techniques, data sources, and feature selection approaches used in DTC recurrence prediction models. Several machine-learning algorithms have been applied. We have suggested the model containing the classifier with the highest accuracy after comparing the accuracy percentage of the various classifiers that were produced. Our suggested model has a 98.17% accuracy rate with Bagging. Finally, we propose solutions to these difficulties and emphasize ML's potential to revolutionize the landscape of DTC recurrence prediction. We hope that this analysis will provide insights into the developing role of machine learning in DTC management and motivate more research in this crucial area.


The Sophisticated Prognostication of Migraine Aura Using Machine Learning
Samiullah, Abdul Rehman, Attique Ur Rehman, Sabeen Javaid, Tahir Mohammad Ali, Azka Mir, and Yadaiah Nirsanametla
IEEE
Migraine is one of the most disabling diseases in the world and impacting more than one billion individuals. The symptoms such as intensity, Nausea, Vomit, Phonophobia, Photophobia, Visual, Dysphasia, Dysarthria, Vertigo, Sensory, intense to sound, occurs before the migraine. Migraine drains the quality of persons' life. Furthermore, the study of this research was to train machine learning model on migraine aura dataset using different modern approaches that could assist medical patients before occurring migraine and could also give the indications related to migraine. Likewise, by applying random forest algorithm, we got 99.5 percent accuracy using google colab which will be enough to deploy on application for future Project. Moreover, we leveraged traditional framework to complete the research incorporating, Data collection (Online free Kaggle), Preprocessing, Classification, Model training and Comparison of models. As a result, our peak algorithm precision was including, Naive Bayes 94.52%, decision tree 98.3%, K-nearest neighbours 98.6%, random forest 99.5%, Similarly, Rapid miner and Google Colab software are used for the comparison of algorithms and best one is chosen.


Beyond Traditional Methods: Exploring Deep Learning Techniques for Accurate Traffic Projection
Avinash Jha, Shresthi Singh, Shamneesh Sharma, Isha Batra, Arun Malik, Saira Muzzaffar, and Yadaiah Nirsanametla
IEEE
Predicting traffic flow is a crucial component of the intelligent transportation system. This is a precise projection of the volume of traffic in a specific area at a specific future period. Researching traffic forecasts is beneficial in reducing traffic and facilitating safer, more affordable travel. Although classic models make use of shallow networks, the number of cars has grown exponentially in recent years, making these traditional machine learning techniques inapplicable to contemporary situations. We discuss some of the most recent developments in deep learning for traffic flow prediction in our article. The Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto are among the many deep learning designs. Encoder (SAE). These deep learning models gradually extract higher level information from raw input by using numerous layers. The most recent deep learning models created to address this specific issue are examined, and because transport networks are complicated, this review also informs the reader about the aspects that affect these models and which models perform best in particular situations.


Enhancing soot emission control and performance in biodiesel-powered diesel engine through Al<inf>2</inf>O<inf>3</inf> nanoparticle
Natesan Kapilan, Ashok Kumar K, Abdulrajak Buradi, Bhaskor Jyoti Bora, Yadaiah Nirsanametla, Ali Majdi, Majed Alsubih, Saiful Islam, Mohammad Amir Khan, Wahaj Ahmad Khan,et al.
Oxford University Press (OUP)
Abstract Interest in biodiesel as a diesel fuel substitute has increased due to the growing need for sustainable energy sources. The blends of biodiesel, such B20, have become more popular because they can lessen the need for fossil fuels and greenhouse gas emissions. The blends of biodiesel, however, may pose problems with emissions, performance, and combustion efficiency. The objective of this study is to investigate the effects of blending ethanol (C2H6OH) and aluminium oxide (Al2O3) into B20 biodiesel blend in order to improve engine performance. The study examines the effects of adding C2H6OH (5% of vol.) and Al2O3 (75 ppm) to the B20 biodiesel mix on its essential features and combustion. To fully assess the performance and emissions characteristics of the single cylinder diesel engine, experimental evaluations include a wide range of engine operating loads. The findings show that adding C2H6OH to the B20 blend increases its volatility and oxygen content, which promotes better ignition and combustion characteristics. Additionally, adding Al2O3 nanoparticles to the blend shows promise for improving combustion efficiency by enhancing fuel atomization and lowering soot emission. The synergy of adding both Al2O3 and C2H6OH to B20 significantly reduces CO, HC, and smoke levels of the diesel engine by 33.04, 28.13, and 12.88%, respectively. The results of this study offer important new information about how C2H6OH and Al2O3 additives might improve the B20 biodiesel blend's emissions performance and combustion efficiency, increasing the fuel's potential as a greener alternative for the transportation industry.


Deep Learning Based Fault Detection and Classification in SRAM Cells with Data Augmentation
C. Venkatesan, Yadaiah Nirsanametla, Kuldeep Sharma, and Anurag Shrivastava
IEEE
The Static Random Access Memory (SRAM) cells are important in various integrated circuit designs, providing volatile memory storage for cache memories, register files and other critical components. This paper proposes a deep learning-based technique for classifying and detecting faults in SRAM cells. The proposed approach, which uses deep neural networks, attempts to automatically identify and extract intricate fault patterns from circuit responses, allowing precise fault diagnosis and classification with no human involvement. In order to capture spatial and temporal correlations within circuit data, a variety of deep learning architectures are explored, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multilayer perceptron (MLPs). The comprehensive tests are performed on various SRAM cells, simulating different operating circumstances and failure situations to assess the efficiency of the proposed SRAM ANN technique. Based on the experimental findings, the ATLANTA tool achieves a maximum fault coverage of 92.2%, surpassing conventional models in performance.


Multi-Objective Optimization of Fusion Welding Parameters Using Non-Dominated Sorting Genetic Algorithm II
Monoj Kanti Chakraborty, Arun Jyoti Kalita, Deepty Pandey, Nirsanametla Yadaiah, Md. S. Mujaheed Khan, and Nabam Teyi
Wiley



Influence of tool traverse speed on mechanical properties and fracture morphology of friction stir welded pure copper butt joints
Puspendu Chandra Chandra, Arpan Kumar Mondal, Yadaiah Nirsanametla, Ajay Kumar, and Sohini Chowdhury
Informa UK Limited



An Integration of Satellite A Based Network with Higher Level Type Network with the use of P-P Connection: A Deep Review
Voruganti Naresh Kumar, Kassem Al-Attabi, Gaurav Thakur, Sharath Ambrose, Yadaiah Nirsanametla, and Murugajothi T
IEEE
The Aerial Access 6g Network (AAN) is seen as a way to access remote and sparsely populated areas not served by traditional terrestrial networks, especially with the advent of 6G technology. This study presents a new approach for efficient data collection and transmission in point to point access networks using low earth orbit (LEO) satellites and high altitude platforms (HAPS). Incorporating LEO satellites as backlinks and HAPs as airborne base stations, the system provides low-bandwidth transmission to ground users. A Time Augmented Graph (TEG) model is proposed to represent the dynamic topology of the air access network according to time slots. With this example, this study can create an entire programming problem with the goal of maximizing data transfer to the country’s data processing centre (DPC) while respecting resource constraints. Benders’ decomposition-based algorithm (BDA) is proposed to solve the NP-hardness of the problem and is shown to perform well in producing near-optimal solutions. The effectiveness and efficiency of the proposed strategy is verified through simulation results performed in a realistic environment, showing high speed and performance comparable to search methods. By informing the design and optimization of future communication systems, this study will provide a better understanding of how HAP and LEO satellites work together in aerial access networks for the collection and delivery of remote terrain data.


The Development of Structured Tele Based Medicine Concept Using Programmable System
Myasar Mundher Adnan, Rajesh Pant, Yadaiah Nirsanametla, Tabeen Fatima, Dr Magesh Kumar, and Neelam Sanjeev Kumar
IEEE
In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for “Video Adaptation framework, across security gateways, based on transcription,” Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform’s versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer.


EXPLORING THE HEAT TREATMENT OF ALUMINIUM MATRIX COMPOSITES: A REVIEW
Varun Singhal, Lavish Kumar Singh, Devender Kumar, and Yadaiah Nirsanametla
University of Buckingham Press
The primary synthesis and secondary treatment of aluminum matrix composites are thoroughly reviewed in this work. further treatments that are intended to improve the properties of the synthesized composites—such as heat treatment, forging, and other thermomechanical processes—are covered. An overview of the benefits and limitations of several main synthesis pathways and secondary treatments for the production of ceramic-reinforced AMCs is provided in a clear and comprehensive manner through a synthesis of previous investigations. A noteworthy vacuum exists in the literature regarding the synergistic application of several synthesis pathways and secondary treatment procedures for the production of AMCs, despite substantial research efforts in this area.


A Distributed Network Monitoring and Positioning System Based on TDOA
Yadaiah Nirsanametla, Kuldeep Sharma, Anurag Shrivastava, Arun Pratap Srivastava, Kanchan Yadav, and Amit Srivastava
IEEE
The increasing interconnectedness of our world has led to a growing demand for precise network monitoring and positioning solutions in various commercial sectors like asset management, indoor navigation, and security. Standard GPS systems face limitations in urban areas and indoors due to signal ambiguity, prompting the need for innovative solutions. The proposed Distributed Network Monitoring and Positioning System, based on Time-Difference of Arrival (TDOA) technology, offers unparalleled flexibility, scalability, and precision in localization, making it suitable for diverse applications. The system demonstrates high accuracy across different environments, including non-line-of-sight scenarios, showcasing its versatility and ability to overcome traditional system limitations.


AI and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing
P. William, Yadaiah Nirsanametla, Ahmed Al-Samalek, Ahmed Hussain, Neeraj Varshney, and ALN Rao
IEEE
Artificial neural networks (ANNs), fuzzy systems, expert systems, pattern recognition methods, and modern hybrid approaches to artificial intelligence (AI) may all be considered as phases of a progression that began more than 20 years ago. This trend began with the creation of artificial neural networks (ANNs). This research article includes a number of original discoveries and focuses on hybrid "artificial intelligence’ (AI) and multi-strategy machine learning approaches. This new knowledge is presented as well as a discussion of the essential stages that comprise this process. One of the possible uses for agent-based holonic systems has been identified as being the management of complexity, changes, and interruptions in production systems. It is envisaged that more approaches would be incorporated together. In the event that one so chooses, the subject of defect detection might be rethought as one of binary categorization. Both the classification task Machine learning technique and the choice of the features that make up the data and are most significant to the process's quality were decided on the basis of the l1-regularized logistic regression. The establishment of a brand-new manufacturing industry that is being referred to as Smart Manufacturing. This was done in order to guarantee the highest possible level of quality throughout the whole of the process. This allowed for optimal efficiency in both areas. Because of this, it is feasible to combine the most relevant facts about the procedure. The suggested strategy is supported by a cutting-edge hybrid feature removal technique and the best classification threshold search algorithm currently available. The outcomes of the tests reveal that flaws can always be precisely detected without fail.


Particle Filter-Based Localization Algorithm for Autonomous Robots in Smart Factories
Yadaiah Nirsanametla, Kuldeep Sharma, Kanchan Yadav, A L N Rao, Amit Srivastava, and Sanjeev Kumar Shah
IEEE
This research aims to study and compare the performance of four particle filter-based localization algorithms, Monte Carlo Localization (MCL), Auxiliary Particle Filter (APF), Rao-Blackwellized Particle Filter(RBPF), and FastSLAM implemented in smart factories using autonomous robots. By conducting a variety of simulations as well as real-world experiments, the paper assesses algorithmic performance in terms of accuracy computational efficiency, and responsiveness to change. FastSLAM shows itself as the best model demonstrating 0.09 of MSE and 15 ms of processing time among others. RBPF also demonstrates notable accuracy (MSE: It also focuses on its factorized state representation, accuracy, and computational efficiency. MCL and APF, which reach excellent precision and adaptive capacities, however, display slightly more computational expenses. The study offers useful information for practitioners and researchers looking for perfect solutions to robust autonomous robot localization in dynamic industrial conditions, thereby contributing towards the development of smart factory technologies.


Three-dimensional transient heat transfer analysis of micro-plasma arc welding process using volumetric heat source models
Benjamin Das, Sohini Chowdhury, Yadaiah Nirsanametla, Chander Prakash, Lovi Raj Gupta, and Vladimir Smirnov
Walter de Gruyter GmbH
Abstract The micro plasma arc welding process is associated with different physical phenomena simultaneously. This results in complexities to comprehend the actual mechanism involved during the process. Therefore, a robust numerical model that can compute the weld pool shape, temperature distribution, and thermal history needs to be addressed. Unlike, other arc welding processes, the micro plasma arc welding process utilizes thin sheets of thickness between 0.5 and 2 mm. However, joining thin sheets using a high-density arc welding process quickens the welding defects such as burn-through, thermal stresses, and welding-induced distortions. The incorporation of a surface heat source model for computational modeling of the high energy density welding process impedes heat transfer analysis. In that respect, researchers have developed numerous volumetric heat source models to examine the welding process holistically. Although, selecting volumetric heat source models for miniature welding is a significant task. The present work emphasis developing a rigorous yet efficient model to evaluate weld pool shape, temperature distribution, and thermal history of plasma arc welded Ti6Al4V sheets. The computational modeling is performed using a commercially available COMSOL Multiphysics 5.4 package with a finite element approach. Two different prominent thermal models, namely, Parabolic Gaussian and Conical power energy distribution models are used. A comparative analysis is carried out to determine the most suitable heat source model for evaluating temperature distribution, peak temperature, and thermal history. The analysis is done by juxtaposing the simulated half-cross-section weld macrographs with the published experimental results from independent literature. The numerical results showed that the proximity of top bead width magnitude was obtained using the Parabolic Gaussian heat source model for low heat input magnitude of 47.52 and high heat input magnitude of 65.47 J·mm−1, respectively. In terms of percentage error, the maximum top bead width percentage error for the Parabolic heat source model is 13.26%. However, the maximum top bead width percentage error for the Conical heat source model is 18.36%. Likewise, the maximum bottom bead width percentage error for the Parabolic heat source model and the Conical heat source model is 12.3 and 25.8%, respectively. Overall, it was observed that the Parabolic heat source model produces the least deviating outcomes when compared with the Conical distribution. It was assessed that the Parabolic Gaussian heat source model can be a viable heat source model for numerically evaluating micro-plasma arc welded Ti6Al4V alloy of thin sheets.


Study of thermal behavior during the directed energy deposition (DED) of Ti6Al4V alloy using finite element modeling (FEM)
Sapam Ningthemba Singh, Ashish B. Deoghare, and Yadaiah Nirsanametla
Elsevier BV



Influence of welding speed and material location on microstructure and mechanical properties of friction stir welding joints of AA6061-AA7050
Gollo Rinu, Sandeep Singh, N. Yadaiah, Chander Prakash, and Dharam Buddhi
Elsevier BV



Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties &amp; defects, and numerical modelling
Sohini Chowdhury, N. Yadaiah, Chander Prakash, Seeram Ramakrishna, Saurav Dixit, Lovi Raj Gupta, and Dharam Buddhi
Elsevier BV



Experimental investigation of defect formation, microstructure and mechanical properties in friction stir welding of AA5086
Gollo Rinu, Sandeep Singh, Yadaiah Nirsanametla, Anil Kumar Dipati, Chander Prakash, and Ketan Kotecha
EDP Sciences
The joint conditions of a weldment entirely rely on the set of process parameters applied during the welding operation. In friction stir welding (FSW), proper mechanical mixing of the material signifies the appropriate welding variables. The present work aspires to investigate the influence of tool rotational speed (RS), welding speed (WS) and plunge depth (PD) on external as well as internal defect formation, identify the different types of defect encountered during the FSW process, and evaluate the influence of different parameters on tensile properties and microstructure of the joints. In order to achieve the goal of the present work, a detailed experimental investigation was carried out using AA5086-O as base metal. On account of high strength-to-weight ratio, good weldability and high corrosion resistance, friction stir welded joints of aluminum alloys are widely applications in aerospace, automotive, railway and shipbuilding industry. It has been observed that theultimate tensile strength (UTS) of joint tends to decrease when there is increased in the welding speed at constant PD of 0.2 mm and RS of 1400 rpm. Similarly, with consistent PD of 0.3 mm and rotational speed of 2000 rpm, UTS of joints initially enhanced with an increment in welding speed and then reduced eventually. A joint fabricated with maximum rotation speed, intermediate welding speed and the higher plunge depth produced highest tensile strength of 215 MPa which is 16% higher than the conditions required for aerospace applications of FSW for aluminum alloys, as per the standard of American Welding Society (AWS). Microscopic analysis was conducted to scientifically ascertain the grain size, crystal structure and surface morphology of the FSW joints.


An investigation on heat transfer analysis of Micro-Plasma arc welds using finite element method
Benjamin Das, Yadaiah Nirsanametla, and Arpan Kumar Mondal
Elsevier BV

RECENT SCHOLAR PUBLICATIONS

Numerical investigation of Dean vortex evolution in turbulent flow through 90 pipe bends
P Dutta, NK Rajendran, R Cep, R Kumar, H Kumar, Y Nirsanametla
Frontiers in Mechanical Engineering 11, 1405148 2025



Impact of Tool Plunge Depth, Tool Tilt Angle, and Tool Offset on Microstructure, Mechanical Properties, and Fracture Morphology of Friction Stir Welded Pure Copper Butt Joints
PC Chandra, AK Mondal, Y Nirsanametla, S Chowdhury, B Halder
Journal of Materials Engineering and Performance, 1-18 2025



Surface micro-cracks and microstructures of Ti6Al4V alloy fabricated by high-layer thickness multi-laser directed energy deposition additive manufacturing process
SN Singh, A Mahmun, AB Deoghare, Y Nirsanametla, S Chowdhury
Scientia Iranica 2024



Influence of tool traverse speed on mechanical properties and fracture morphology of friction stir welded pure copper butt joints
P Chandra Chandra, AK Mondal, Y Nirsanametla, A Kumar, S Chowdhury
Journal of Adhesion Science and Technology, 1-14 2024



Multi‐Objective Optimization of Fusion Welding Parameters Using Non‐Dominated Sorting Genetic Algorithm II
MK Chakraborty, AJ Kalita, D Pandey, N Yadaiah, MS Mujaheed Khan, ...
New Materials, Processing and Manufacturability: Fabrication and Processing 2024



Exploring the Heat Treatment of Aluminium Matrix Composites: A Review
V Singhal, LK Singh, D Kumar, Y Nirsanametla
International Journal of Maritime Engineering 1 (1), 409-418 2024



The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects
I Imtiaz, AU Rehman, S Javaid, TM Ali, A Mir, M Masud, Y Nirsanametla
2024 International Conference on Emerging Trends in Networks and Computer 2024



Beyond Traditional Methods: Exploring Deep Learning Techniques for Accurate Traffic Projection
A Jha, S Singh, S Sharma, I Batra, A Malik, S Muzzaffar, Y Nirsanametla
2024 International Conference on Emerging Trends in Networks and Computer 2024



The Sophisticated Prognostication of Migraine Aura Using Machine Learning
A Rehman, AU Rehman, S Javaid, TM Ali, A Mir, Y Nirsanametla
2024 International Conference on Emerging Trends in Networks and Computer 2024



The Development of Structured Tele Based Medicine Concept Using Programmable System
MM Adnan, R Pant, Y Nirsanametla, T Fatima, M Kumar, NS Kumar
2024 4th International Conference on Advance Computing and Innovative 2024



An Integration of Satellite A Based Network With Higher Level Type Network with the use of PP Connection: A Deep Review
VN Kumar, K Al-Attabi, G Thakur, S Ambrose, Y Nirsanametla
2024 4th International Conference on Advance Computing and Innovative 2024



Influence of surface-active elements on GTA welds with respect to metallographic analysis and temperature distribution
AK Deepati, S Chowdhury, N Teyi, Y Nirsanametla, C Prakash, ...
International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (3 2024



Deep Learning Based Fault Detection and Classification in SRAM Cells with Data Augmentation
C Venkatesan, Y Nirsanametla, K Sharma, A Shrivastava
2024 10th International Conference on Advanced Computing and Communication 2024



Particle Filter-Based Localization Algorithm for Autonomous Robots in Smart Factories
Y Nirsanametla, K Sharma, K Yadav, ALN Rao, A Srivastava, SK Shah
2024 4th International Conference on Innovative Practices in Technology and 2024



A Distributed Network Monitoring and Positioning System Based on TDOA
Y Nirsanametla, K Sharma, A Shrivastava, AP Srivastava, K Yadav, ...
2024 4th International Conference on Innovative Practices in Technology and 2024



AI and Machine Learning Techniques for Managing Complexity, Changes and Uncertainties in Manufacturing
P William, Y Nirsanametla, A Al-Samalek, A Hussain, N Varshney, ...
2024 4th International Conference on Innovative Practices in Technology and 2024



A Multifaceted Approach: Investigating Engineered Nanoparticle Inhalation in Infants Based on Nano Science
J Gudainiyan, R Thakur, Y Nirsanametla, A Raturi, AP Srivastava, ...
Sumy State University 2024



Influence of tool traverse speed on mechanical properties and fracture morphology of friction stir welded pure copper butt joints
PC Chandra, AK Mondal, Y Nirsanametla, A Kumar, S Chowdhury
2024



Enhancing soot emission control and performance in biodiesel-powered diesel engine through Al₂O₃ nanoparticle
N Kapilan, AK K, A Buradi, BJ Bora, Y Nirsanametla, A Majdi, M Alsubih, ...
International Journal of Low-Carbon Technologies 19, 2638-2645 2024



Towards a Carbon Neutral Future: Integrating Renewable Sources and Energy Storage in Sustainable Energy Solutions
R Singh, RP Singh, Y Nirsanametla, B Prasad, A Shrivastava, ...
E3S Web of Conferences 511, 01007 2024

MOST CITED SCHOLAR PUBLICATIONS

Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties & defects, and numerical modelling
S Chowdhury, N Yadaiah, C Prakash, S Ramakrishna, S Dixit, LR Gupta, ...
Journal of Materials Research and Technology 20, 2109-2172 2022
Citations: 414



Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component
RC Raman Kumar, Rohit Dubey, Sehijpal Singh, Sunpreet Singh, Chander Prakash ...
Materials 14 (8), 2084 2021
Citations: 94



Development of egg-configuration heat source model in numerical simulation of autogenous fusion welding process
N Yadaiah, B Swarup
International Journal of Thermal Sciences 86, 125-138 2014
Citations: 73



Effect of heat source parameters in thermal and mechanical analysis of linear GTA welding process
N Yadaiah, B Swarup
ISIJ International 52 (11), 2069-2075 2012
Citations: 58



Thermo mechanical analyses and characterizations of TiNiCu shape memory alloy structures developed by laser additive manufacturing
S Shiva, N Yadaiah, IA Palani, CP Paul, KS Bindra
Journal of Manufacturing Processes 48, 98-109 2019
Citations: 37



Environmental, economical and technological analysis of MQL-assisted machining of Al-Mg-Zr alloy using PCD tool
MR Karim, JB Tariq, SM Morshed, SH Shawon, A Hasan, C Prakash, ...
Sustainability 13 (13), 7321 2021
Citations: 36



A comparative analysis of laser additive manufacturing of high layer thickness pure Ti and Inconel 718 alloy materials using finite element method
SN Singh, S Chowdhury, Y Nirsanametla, AK Deepati, C Prakash, ...
Materials 14 (4), 876 2021
Citations: 30



Development of avocado shape heat source model for finite element based heat transfer analysis of high-velocity arc welding process
AK Mondal, B Kumar, S Bag, Y Nirsanametla, P Biswas
International Journal of Thermal Sciences 166, 107005 2021
Citations: 26



A perspective review on experimental investigation and numerical modeling of electron beam welding process
S Chowdhury, N Yadaiah, SM Khan, R Ozah, B Das, M Muralidhar
Materials Today: Proceedings 5 (2), 4811-4817 2018
Citations: 25



Identification of modes of welding using parametric studies during ytterbium fiber laser welding
S Chowdhury, Y Nirsanametla, M Muralidhar, S Bag, CP Paul, KS Bindra
Journal of Manufacturing Processes 57, 748-761 2020
Citations: 23



A perspective review on estimation of keyhole profile during plasma arc welding process
B Das, N Yadaiah, R Ozah, S Chowdhury, AK Mondal, M Muralidhar
Materials today: proceedings 5 (2), 6345-6350 2018
Citations: 18



Influence of tack operation on metallographic and angular distortion in electron beam welding of Ti-6l-4V alloy
S Chowdhury, N Yadaiah, DA Kumar, M Murlidhar, CP Paul, C Prakash, ...
Measurement 175, 109160 2021
Citations: 15



Synthesis of functionalized TiO2-loaded HAp-coating by ball-burnishing assisted electric discharge cladding process
C Prakash, R Wandra, S Singh, A Pramanik, A Basak, A Aggarwal, ...
Materials Letters 301, 130282 2021
Citations: 12



Influence of self-protective atmosphere in fiber laser welding of austenitic stainless steel
N Yadaiah, S Bag, CP Paul, LM Kukreja
The International Journal of Advanced Manufacturing Technology 86, 853-870 2016
Citations: 12



Role of Oxygen as Surface-Active Element in Linear GTA Welding Process
N Yadaiah, B Swarup
Journal of Materials Engineering and Performance 22 (11), 3199-3209 2013
Citations: 12



Fiber laser welding in a controlled inert gas atmosphere: An experimental and numerical investigation
Y Nirsanametla, S Bag, CP Paul, LM Kukreja
Lasers Based Manufacturing: 5th International and 26th All India 2015
Citations: 10



Numerical simulation of welding-induced residual stress in fusion welding process using adaptive volumetric heat source
S Singh, N Yadaiah, S Bag, S Pal
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of 2014
Citations: 9



Comparison of microstructure and mechanical performance of laser and electron beam welded Ti6Al4V alloy
S Chowdhury, N Yadaiah, M Murlidhar, DA Kumar, CP Paul, SK Patra, ...
Journal of the Brazilian Society of Mechanical Sciences and Engineering 43, 1-12 2021
Citations: 8



Influence of welding speed and material location on microstructure and mechanical properties of friction stir welding joints of AA6061-AA7050
G Rinu, S Singh, N Yadaiah, C Prakash, D Buddhi
Materials Today Communications 33, 104419 2022
Citations: 6



A State-of-the-Art Review of the Technology, Materials, Properties & Defects, and Numerical Modelling., 2022, 20
S Chowdhury, N Yadaiah, C Prakash, S Ramakrishna, S Dixit, LR Gupta, ...
DOI: https://doi. org/10.1016/J. JMRT 121, 2109-2172 2022
Citations: 6