Bhiksha Gugulothu

Scopus Publications

Experimental and statistical analysis of tribological properties in Stir-Cast Al2219 hybrid composites using Taguchi- GRA method
Bhiksha Gugulothu, S. Karthikeyan, Kunche Krishna Chaitanya, Chinnam Durga Jaya Teja, Vijayakumar Sivasundar
Interactions, 2026
Development of stir-cast al 6063 hybrid composites with 5 Wt.% fly ash and varying SiC reinforcements for improved mechanical and impact properties
Bhiksha Gugulothu, R. Sanjeev Kumar, P. S. Satheesh Kumar, S. Karthikeyan, Deepak Gupta
Interactions, 2026
Investigation of mix design variables on the performance of Kenaf fibre-reinforced concrete through grey relational analysis/ANN-LM model
Butti Venkatesh, Bhiksha Gugulothu, Rabiu Aliyu Abdulkadir, Movva Swapna NagaSri, N. Dhasarathan, Srikanth Karumuri
Interactions, 2026
Catalytic CO₂ conversion and membrane separation: a review of innovations for industrial CCUS technologies and global decarbonization
Bhiksha Gugulothu, Amaleswari Rajulapati, Anusha Peyyala, Movva Naga Swapna Sri, Nagarajan Dhasarathan Raja, S. Berbeth Mary
Interactions, 2026
Investigation of surface morphology and surface roughness of AISI 304 stainless steel in WEDM: Experimental optimization and prediction using machine learning
Bhiksha Gugulothu, Rajkumar Devapiriam Ramachandran, K. Kumar, P. Pramod Kumar, Vijayakumar Sivasundar, Akanksha Mishra, Veeresha G, Najihah Mohd Tamyis
Journal of Materials Research and Technology, 2026
WEDM is a non-conventional method for making complex shapes of hard metals whose surface finish is significantly dependent on process parameters. The influence of WEDM parameters on surface roughness (R a ) at AISI 304 steel was studied in this work by using an innovative approach for predicting the quality of machined surfaces with four inherent prediction models, including ANN-LOOCV (Artificial Neural Networks and Leave-One-Out Cross Validation), PCR (Principal Component Regression), RSM (Response Surface Methodology) and MLR (Multiple Linear Regression). For machining the samples, three input factors are identified: peak current, pulse-on time and the pulse-off time of the wire. Nine experiments are performed based on an L9 orthogonal array. The SEM test contrasts materials with low and high surface roughness in order to analyse surface morphology.From SEM analysis, Sample 9 reveals a recast microstructure with a thin, uniform layer and evenly distributed microcracks, which resulted from thermal loading within a controlled range. ANOVA shows that the most significant factor affecting surface roughness during EDM is peak current with a percentage contribution of 40.5%, followed by the pulse-on time (17.6%) and the pulse-off time (38.2%). The prediction accuracy for the four models is highest (78%) with the ANN model at a RMSE of 0.325 μm and an MAE of 0.253 μm. These results highlight the superior predictive capability of ANN models for surface roughness, even with limited data. The findings provide valuable insights for optimising WEDM parameters, contributing to improved machining outcomes.
Data-Driven Electromagnetic Wave Propagation Modeling for Urban and Indoor Wireless Channels
Movva Naga Swapna Sri, Bhiksha Gugulothu, A.P. Pushpalatha, Amaleswari Rajulapati, Anusha Peyyala, Vijayakumar Sivasundar
2026 1st International Electronics Packaging Design and Manufacturing Conference Bridging Skills and Innovation for India S Industry Epdmc 2026, 2026
This paper examines how EM wave propagates through urban area and also examines the communication happens indoor. Existing methodologies have been based on the physics of the subject area, however, the application of those same methodologies to actualized versions of real world environments have proven to be time-consuming in a computational sense. Therefore, synthetic data from simulated environments was used to create a model of a neural network model utilizing an attention mechanism to find behavior in wireless communications by identifying propagation characteristics. The neural network model generated predictions concerning the amount of signal attenuation experienced by the signals and the variability in the signal strengths experienced within an environment, and was able to do so using only modeled data and without the need for explicit ray tracing methodologies. Further, the neural network model’s outputs showed spatial continuity, consistency in their patterns, and similar levels of error throughout the environment. The model also was found to demonstrate good generalization capabilities for a number of different environments for which it had no prior knowledge. In comparison to reference baselines, this model displayed superior accuracy and improved flexibility to accommodate a variety of environments. Ablation test results suggest that the performance of models can be significantly influenced by both how they are designed (i.e., their use of attention mechanisms) and how the inputs are processed (i.e., feature selection). The output from this model appears to exhibit many of the properties that one would expect of an output generated as part of simulating a realistic environment even though the input data was entirely artificially created. In general, this research provides evidence for the ability of employing data driven methods to guide propagation of modeling toward informing current wireless system planning and future network design.
The role of robotics in smart agriculture for sustainability of food systems
Bhiksha Gugulothu, Murugesan Ganesan, Mohd Naved, D. Vetrithangam, Kantilal Pitambar Rane, Manisha Mali
Robotics and Intelligent Machines in Smart Agriculture Emerging Systems and Applications, 2026
The increasingly pressing need for food supply in the face of labor shortage, climate change, and sustainability has stimulated the adoption of new technology and machinery for agriculture. As a fundamental factor for smart agriculture, robotics enables the transformation of automation of farming activities and can be used to improve the productivity and sustainability of crop production. From self-driving tractors and robot harvesters to drones and weeding robots, robotics has been changing agriculture by cutting resource use, boosting yield, and reducing reliance on human labor. This chapter further investigates the adoption of robotics in precision agriculture, studying pivotal applications, namely, crop monitoring, sowing, watering, spraying, harvesting, and the supply chain. It also discusses the enabling technologies, such as AI, IoT, and computer vision, that collaborate with robotics to improve agricultural decision-making. Some challenges, such as expensive implementation, interoperable problems, and ethical issues, are also discussed. Lastly, this chapter outlines the future and opportunities for robots to contribute to developing resilient, climate-smart, and fully automated farming ecosystems.
Analysis of Atmospheric Attenuation Effects on mmWave Signal Propagation in Future 6G Wireless Communication Networks
Amaleswari Rajulapati, Vijayakumar Sivasundar, Anusha Peyyala, Movva Naga Swapna Sri, Bhiksha Gugulothu, A.P. Pushpalatha
2026 1st International Electronics Packaging Design and Manufacturing Conference Bridging Skills and Innovation for India S Industry Epdmc 2026, 2026
The objective of this study was to assess the effects of the environment on mm wave communications in reference to a 6 G wireless network. As there was no available measured data, the researchers used simulations created using synthetic data to determine how different environmental conditions affect the mm wave communications. The results showed that frequency bands above 20 GHz are significantly attenuated, especially at 22, 60 and 120 GHz due to oxygen and water vapor absorption. Also, rain, fog, and humidity were shown to weaken the signal as well as increase the effect of distance on signal strength loss. The random variations produced by changing weather conditions were simulated using Monte Carlo simulations. A hybrid RABA-AMCS simulation model was developed and tested and found to improve error rates relative to the Free Space and ITU-R models. The results also indicated that the atmosphere is likely to be a significant factor in signal loss for a reliable 6 G communication system and thus it should be considered during design development.
MULTI-OBJECTIVE OPTIMIZATION TECHNIQUE TO OPTIMIZE THE PROCESS PARAMETERS OF EDM FOR AL ALLOYS USING TAGUCHI WITH GRA AND TOPSIS METHOD
B. Gugulothu, K. Srividya, D. B. Prakash, N. Dhasarathan, K. Bharadwaja, S. Karumuri
Annals of Dunarea De Jos University of Galati Fascicle Xii Welding Equipment and Technology, 2025
Electrical Discharge Machining (EDM) is a non-traditional machining process which employed to make complex products from hard materials. The machining efficiency and surface integrity are influenced by the selection of parameters. In this research, datasets were collected from earlier experimental research works on EDM process of AL alloy to evaluate machining performance. The input parameters: pulse-on time, pulse-off time, discharge current, gap voltage, flushing pressure, and tool rotational speed and the output responses: material removal rate (MRR), tool wear rate (TWR), surface roughness (SRS), recast layer (RLR), and microhardness (MHS) were considered. MINITAB software was utilized to identify the most significant factors affecting responses through signal-to-noise analysis. For multi-objective optimization, the Taguchi method was integrated with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). From the results, GRA identified optimal parameters of discharge current of 11 A, pulse-on time of 75 µs, pulse-off time of 25 µs, gap voltage of 50 V, flushing pressure of 0.4 MPa, and tool speed of 600 rpm, yielding the highest productivity with MRR of 4.76 g/min, moderate TWR of 0.478 g/min. In contrast, TOPSIS suggested a discharge current of 7 A, pulse-on time of 175 µs, pulse-off time of 90 µs, gap voltage of 40 V, a flushing pressure of 0.5 MPa, and a tool speed of 900 rpm, which produced superior surface quality with a lower SRS of 7.34 µm, reduced RLR of 19.6 µm, and higher MHS of 120.9 HV and a reduced MRR of 0.78 g/min. Both optimal results were validated using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, confirming accurate prediction of EDM responses. This study demonstrates that GRA is more suitable for productivity-focused applications, whereas TOPSIS is advantageous when surface integrity and hardness are critical, offering a robust decision-making framework for EDM optimization.
Big Data Analytics-Driven Temperature Prediction in Wireless Sensor Networks using ANFIS and Sensor Operational Parameters
Bhiksha Gugulothu, S. Jancy Sickory Daisy, P. S. Satheesh Kumar, T.R. Arunprasand, S. Vijayakumar, N.Dhasarathan
Proceedings of 5th International Conference on Ubiquitous Computing and Intelligent Information Systems Icuis 2025, 2025
Wireless Sensor Networks are important in the current industrial and environmental surveillance fields that led to give accurate and find the temperature used in operational efficiency and also provided the predictive maintenance. These networks offered sensing capabilities which used to age-related performance degradation. These are limitations that highlighted the growth of better predictive algorithms to ensure long-term feasibility of data. In the era of Big Data, managing large number volumes in sensor information effectively becomes essential to extract meaningful insights. This study applied Thean FIS model for prediction of the temperature-based input parameters and the data were collected from the Kaggle Notebook Wireless Sensor Network Project -Quick Overview, and these collection data were trained under the ANFIS parameter at MATLAB. The produced model was established 78 nodes and 27 fuzzy rules for 108 linear parameters in the consequent layer and 27 nonlinear parameters in the antecedent layer amounting to 135 parameters to be optimized. The planned ANFIS model reached an accuracy of 93.53% and MAPE value of 6.47%, which showed ANFIS was capable of accurately modelling the nonlinear relationship between operational values of sensor variables and temperature readings under Big Data analytics-driven conditions.
Big Data Learning for Forecasting and Reducing Energy Consumption in Mobile Communication Systems
Anusha Peyyala, Boyapati Purna ChandraSekhar, Varsha D. Jadhav, Bhiksha Gugulothu, Srikanth Karumuri, S. S. Sivaraju
Proceedings of the 7th International Conference on Innovative Data Communication Technologies and Application Icidca 2025, 2025
MACHINE LEARNING MODELS FOR PREDICTING MECHANICAL PROPERTIES IN FRICTION STIR WELDING OF AL ALLOYS
B. Gugulothu, K. Srividya, S. Vijayakumar, I. Veeranjaneyulu, S. Revathi, M. Ramya
Annals of Dunarea De Jos University of Galati Fascicle Xii Welding Equipment and Technology, 2025
Optimization of TIG welding process parameters on chrome alloy steel using Box–Behnken method
Bhiksha Gugulothu, Srikanth Karumuri, S. Vijayakumar, B. Muthuvel, Suresh Seetharaman, S. Jeyakrishnan, Kuldeep K. Saxena
International Journal on Interactive Design and Manufacturing, 2024
Modeling and parametric optimization of electrical discharge machining on casted composite using central composite design
Bhiksha Gugulothu, K. Bharadwaja, S. Vijayakumar, T. V. Janardhana Rao, M. Naga Swapna Sri, P. Anusha, Manoj Kumar Agrawal
International Journal on Interactive Design and Manufacturing, 2024
Friction Stir Welded Magnesium AZ31B Alloy Used to Evaluate Mechanical Properties at Various Rotational Speeds
Bhiksha Gugulothu, P. S. Satheesh Kumar, N. Srinivasa Rao, S. Vijayakumar, D. R. Rajkumar, T. V. Janardhana Rao, M. Naga Swapna Sri
Advances in Science Technology and Innovation, 2024
RETRACTED ARTICLE: Investigating the strength of butt-welded joints of AA6082 and AA5052 alloys through friction stir welding; the impact of tool tilt angle and feed rate (Journal of adhesion science and technology, (2024), 38, 22, (i–xxiv))
Bhiksha Gugulothu, Rajasekaran Saminathan, A. Pradeep, Ankit Sharma, S. Vijayakumar, Prabhu Paramasivam, N. Srinivasa Rao
Journal of Adhesion Science and Technology, 2024
Optimization of process parameters by response surface methodology using box-behnken method on electrical Discharge machining of Ti-6Al-4VAlloy
Bhiksha Gugulothu
Aip Conference Proceedings, 2023
Electrical discharge machining parameters and dielectric fluid: A review
Bhiksha Gugulothu, N. Aravindan, Gunawan Widjaja, S. A. Lakshmanan, M. Suresh
Handbook of Research on Advanced Functional Materials for Orthopedic Applications, 2023
Electric discharge machining: A promising choice for surface modification of metallic implants
Bhiksha Gugulothu, Pankaj Sharma, N. Pragadish, V. Vidyapriya, H. Venkatesan
Handbook of Research on Advanced Functional Materials for Orthopedic Applications, 2023
A Retrospective Investigation on Hybrid Metal Matrix Composites: Materials, Processing Methods, and Properties of Composites
Anand K., R. Jeyapaul, Bhiksha Gugulothu, S. Selvaraj, M. Varatharajulu
Iranian Journal of Chemistry and Chemical Engineering, 2023
Manihot esculenta tuber microcrystalline cellulose and woven bamboo fiber-reinforced unsaturated polyester composites: mechanical, hydrophobic and wear behavior
Bhiksha Gugulothu, N Nagabhooshanam, M Arun, N Vinayaka, Pravin P Patil, K V S R Murthy
Materials Research Express, 2023
Process parameter optimization for tensile strength and Hardness of Al-MMC using RSM technique
Bhiksha Gugulothu, Suresh Seetharaman, S. Vijayakumar, D. Jenila Rani
Materials Today Proceedings, 2022
Analysis of Mechanical Properties for Al-MMC Fabricated through an Optimized Stir Casting Process
Bhiksha Gugulothu, N. Nagarajan, A. Pradeep, G. Saravanan, S. Vijayakumar, Janardhana Rao
Journal of Nanomaterials, 2022
Design and Fabrication of Patient-Specific Implant for Maxillofacial Surgery Using Additive Manufacturing
Rakesh Koppunur, Kiran Kumar Dama, Uzwalkiran Rokkala, Balaji Thirupathi, N. V. S. S. Sagar, Bhiksha Gugulothu
Advances in Materials Science and Engineering, 2022
Effect of SC Parameters on Material Characteristics for Al 5083-Based Hybrid Matrix Composites Using Taguchi RSM Technique
A. U. Rao, K. Ramasamy, A. Pradeep, P. S. Satheesh Kumar, Srikanth Karumuri, Bhiksha Gugulothu
Journal of Nanomaterials, 2022
Optimization of Stir-Squeeze Casting Parameters to Analyze the Mechanical Properties of Al7475/B4C/Al2O3/TiB2 Hybrid Composites by the Taguchi Method
Bhiksha Gugulothu, P. Anusha, M. Naga Swapna Sri, S. Vijayakumar, R. Periyasamy, Suresh Seetharaman
Advances in Materials Science and Engineering, 2022
Analysis of Wear Behaviour of AA5052 Alloy Composites by Addition Alumina with Zirconium Dioxide Using the Taguchi-Grey Relational Method
Bhiksha Gugulothu, S. Lakshmi Sankar, S. Vijayakumar, Adina Srinivasa Vara Prasad, M. Thangaraj, M. Venkatachalapathy, T.V.Janardhana Rao
Advances in Materials Science and Engineering, 2022
Optimization of Tensile and Impact Strength for Injection Moulded Nylon 66/Sic/B4c Composites
G. Boopathy, V. Vanitha, K. Karthiga, Bhiksha Gugulothu, A. Pradeep, Hari Prasadarao Pydi, S. Vijayakumar
Journal of Nanomaterials, 2022
RESEARCH RESULTS OF TI-6AL-4V ON ELECTRICAL DISCHARGE MACHINING
Proceedings of the International Astronautical Congress Iac, 2021
Grey relational analysis for multi-response optimization of process parameters in green electrical discharge machining of Ti-6Al-4V alloy
Bhiksha Gugulothu, G. Krishna Mohana Rao, Minyahil Bezabih
Materials Today Proceedings, 2021
Effect of process parameters on centre lathe of EN8 steel in turning process
Bhiksha Gugulothu, Desta Kalbessa Kumsa, Minyahil Bezabih Kassa
Materials Today Proceedings, 2021
Experimental results on EDM of Ti-6Al-4V in drinking water with Graphite powder concentration
Bhiksha Gugulothu, G. Krishna Mohana Rao, D. Hanuantha Rao, Desta Kalbessa Kumsa, Minyahil Bezabih Kassa
Materials Today Proceedings, 2021
Investigating the Material Removal Rate Parameters in ECM for Al 5086 Alloy-Reinforced Silicon Carbide/Flyash Hybrid Composites by Using Minitab-18
Bhiksha Gugulothu, P. S. Satheesh Kumar, Bathula Srinivas, Alapati Ramakrishna, S. Vijayakumar
Advances in Materials Science and Engineering, 2021
Parameters Optimization of Dissimilar Friction Stir Welding for AA7079 and AA8050 through RSM
M. Kavitha, V. M. Manickavasagam, T. Sathish, Bhiksha Gugulothu, A. Sathish Kumar, Sivakumar Karthikeyan, Ram Subbiah
Advances in Materials Science and Engineering, 2021
Effect of Tool Profile Influence in Dissimilar Friction Stir Welding of Aluminium Alloys (AA5083 and AA7068)
S. Jayaprakash, S. Siva Chandran, T. Sathish, Bhiksha Gugulothu, R. Ramesh, M. Sudhakar, Ram Subbiah
Advances in Materials Science and Engineering, 2021
Optimization of process parameters on EDM of titanium alloy
Bhiksha Gugulothu
Materials Today Proceedings, 2020
Influence of drinking water and graphite powder concentration on electrical discharge machining of Ti-6Al-4V alloy
Bhiksha Gugulothu, G. Krishna Mohana Rao, D. Hanumantha Rao
Materials Today Proceedings, 2020
Some anomalies in the experimental results of EDM
Gugulothu Bhiksha, K. Buschaiah
Applied Mechanics and Materials, 2012

RECENT SCHOLAR PUBLICATIONS

Catalytic CO₂ conversion and membrane separation: a review of innovations for industrial CCUS technologies and global decarbonization
B Gugulothu, A Rajulapati, A Peyyala, MNS Sri, N Dhasarathan Raja, ...
Interactions 247 (1), 234 , 2026
2026
Experimental and statistical analysis of tribological properties in Stir-Cast Al2219 hybrid composites using Taguchi-GRA method
B Gugulothu, S Karthikeyan, KK Chaitanya, CDJ Teja, V Sivasundar
Interactions 247 (1), 8 , 2026
2026
Citations: 1
Investigation of mix design variables on the performance of Kenaf fibre-reinforced concrete through grey relational analysis/ANN-LM model
B Venkatesh, B Gugulothu, RA Abdulkadir, MS NagaSri, N Dhasarathan, ...
Interactions 247 (1), 138 , 2026
2026
Analysis of Atmospheric Attenuation Effects on mmWave Signal Propagation in Future 6G Wireless Communication Networks
A Rajulapati, V Sivasundar, A Peyyala, MNS Sri, B Gugulothu, ...
2026 1st International Electronics & Packaging Technologies Conference … , 2026
2026
Data-Driven Electromagnetic Wave Propagation Modeling for Urban and Indoor Wireless Channels
MNS Sri, B Gugulothu, AP Pushpalatha, A Rajulapati, A Peyyala, ...
2026 1st International Electronics & Packaging Technologies Conference … , 2026
2026
Investigation of Surface Morphology and Surface Roughness of AISI 304 Stainless Steel in WEDM: Experimental Optimization and Prediction Using Machine Learning
Bhiksha Gugulothu , Rajkumar Devapiriam Ramachandran , K. Kumar , P. Pramod ...
Journal of Materials Research and Technology 1 (6), 35 , 2026
2026
Citations: 1
Development of stir-cast al 6063 hybrid composites with 5 Wt.% fly ash and varying SiC reinforcements for improved mechanical and impact properties
B Gugulothu, RS Kumar, PS Satheesh Kumar, S Karthikeyan, D Gupta
Interactions 247 (1), 27 , 2026
2026
Multi-Objective Optimization Technique to Optimize the Process Parameters of EDM for Al Alloys Using Taguchi with GRA and TOPSIS Method
B Gugulothu, K Srividya, DB Prakash, N Dhasarathan, K Bharadwaja, ...
Annals of “Dunarea de Jos” University of Galati. Fascicle XII, Welding … , 2025
2025
Machine Learning Models for Predicting Mechanical Properties in Friction Stir Welding of Al Alloys
B Gugulothu, K Srividya, S Vijayakumar, I Veeranjaneyulu, S Revathi, ...
Annals of “Dunarea de Jos” University of Galati. Fascicle XII, Welding … , 2025
2025
Citations: 9
Big Data Analytics-Driven Temperature Prediction in Wireless Sensor Networks using ANFIS and Sensor Operational Parameters
B Gugulothu, SJS Daisy, PSS Kumar, TR Arunprasand, S Vijayakumar, ...
2025 5th International Conference on Ubiquitous Computing and Intelligent … , 2025
2025
A Sustainable Adsorption Process Using H₃PO₄-Activated Rice Husk and Pomegranate Peel Adsorbent for Efficient Phenol Removal and Adsorption Capacity
S S. Jeyakrishnan , Bhiksha Gugulothu , A.R. Saravanan
Journal of Industrial and Engineering Chemistry 2 (23), 10 , 2025
2025
Big Data Learning for Forecasting and Reducing Energy Consumption in Mobile Communication Systems
Anusha Peyyala, Boyapati Purna ChandraSekhar, Varsha D Jadhav, Bhiksha ...
2025 7th International Conference on Innovative Data Communication … , 2025
2025
Optimization of TIG welding process parameters on chrome alloy steel using Box–Behnken method
B Gugulothu, S Karumuri, S Vijayakumar, B Muthuvel, S Seetharaman, ...
International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (9 … , 2024
2024
Citations: 14
Investigating the strength of butt-welded joints of AA6082 and AA5052 alloys through friction stir welding; the impact of tool tilt angle and feed rate
B Gugulothu, R Saminathan, A Pradeep, A Sharma, S Vijayakumar, ...
Journal of Adhesion Science and Technology, 1-24 , 2024
2024
Citations: 8
Modeling and parametric optimization of electrical discharge machining on casted composite using central composite design
B Gugulothu, K Bharadwaja, S Vijayakumar, TVJ Rao, MNS Sri, P Anusha, ...
International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (5 … , 2024
2024
Citations: 43
Analysis of Parametric Optimization in EDM
Khempal, Bhiksha Gugulothu
International Journal of Advanced Research in Science, Communication and … , 2024
2024
Evaluation of Process Parameters Variations in Wire Electric Discharge Machining
Khempal , Bhiksha Gugulothu
International Journal of Advanced Research in Science, Communication and … , 2024
2024
Design and analysis of customized medical models using hybrid approach
Rakesh Koppunur , Kiran Kumar Dama , Bhiksha Gugulothu , Chitra Chakravarthy ...
Tuijin Jishu/Journal of Propulsion Technology 44 (6), 13 , 2023
2023
Optimization of process parameters by Response Surface Methodology using Box-Behnken method on Electrical Discharge Machining of Ti-6Al-4V Alloy
B Gugulothu
American Institute of Physics (AIP) 2764 (1), 19 , 2023
2023
Citations: 2
Friction stir welded magnesium AZ31B alloy used to evaluate mechanical properties at various rotational speeds
B Gugulothu, PSS Kumar, NS Rao, S Vijayakumar, DR Rajkumar, ...
International Conference on Smart Sustainable Materials and Technologies … , 2023
2023
Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

Effect of Tool Profile Influence in Dissimilar Friction Stir Welding of Aluminium Alloys (AA5083 and AA7068)
S Jayaprakash, S Siva Chandran, T Sathish, B Gugulothu, R Ramesh, ...
Advances in Materials Science and Engineering 2021 (1), 7387296 , 2021
2021
Citations: 139
Parameters Optimization of Dissimilar Friction Stir Welding for AA7079 and AA8050 through RSM
M Kavitha, VM Manickavasagam, BG T. Sathish, AS Kumar, S Karthikeyan, ...
Advances in Materials Science and Engineering 2021 (https://doi.org/10.1155 … , 2021
2021
Citations: 124
Analysis of Mechanical Properties for Al‐MMC Fabricated through an Optimized Stir Casting Process
B Gugulothu, N Nagarajan, A Pradeep, G Saravanan, S Vijayakumar, ...
Journal of Nanomaterials 2022 (1), 7 , 2022
2022
Citations: 74
Analysis of Wear Behaviour of AA5052 Alloy Composites by Addition Alumina with Zirconium Dioxide Using the Taguchi‐Grey Relational Method
B Gugulothu, SL Sankar, S Vijayakumar, ASV Prasad, M Thangaraj, ...
Advances in Materials Science and Engineering 2022 (1), 4545531 , 2022
2022
Citations: 54
Modeling and parametric optimization of electrical discharge machining on casted composite using central composite design
B Gugulothu, K Bharadwaja, S Vijayakumar, TVJ Rao, MNS Sri, P Anusha, ...
International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (5 … , 2024
2024
Citations: 43
Investigating the material removal rate parameters in ECM for Al 5086 alloy-reinforced silicon carbide/flyash hybrid composites by using Minitab-18
B Gugulothu, PS Satheesh Kumar, B Srinivas, A Ramakrishna, ...
Advances in materials science and engineering 2021, 1-6 , 2021
2021
Citations: 40
Optimization of process parameters on EDM of titanium alloy
B Gugulothu
Materials today 27 (P1), 257-262 , 2020
2020
Citations: 37
Optimization of Stir-Squeeze Casting Parameters to Analyze the Mechanical Properties of Al7475/B4C/Al 2 O 3 /TiB2 Hybrid Composites by the Taguchi Method.
B Gugulothu, P Anusha, MN Swapna Sri, S Vijayakumar, R Periyasamy, ...
Advances in Materials Science & Engineering , 2022
2022
Citations: 36
Optimization of Tensile and Impact Strength for Injection Moulded Nylon 66/Sic/B4c Composites
G Boopathy, V Vanitha, K Karthiga, B Gugulothu, A Pradeep, HP Pydi, ...
Advances in Materials Science and Engineering 2022 (1), 7 , 2022
2022
Citations: 36
Grey relational analysis for multi-response optimization of process parameters in green electrical discharge machining of Ti-6Al-4V alloy
B Gugulothu, GKM Rao, M Bezabih
Materials today 46 (P1), 89-98 , 2021
2021
Citations: 34
Process parameter optimization for tensile strength and Hardness of Al-MMC using RSM technique
Bhiksha Gugulothu , Suresh Seetharaman , S. Vijayakumar , D. Jenila Rani
Materials today: Proceedings 62 (P4), 2115-2118 , 2022
2022
Citations: 28
Experimental results on EDM of Ti-6Al-4V in drinking water with Graphite powder concentration
Bhiksha Gugulothu , Krishna Mohana Rao G , Hanuantha Rao D , Desta Kalbessa ...
Materials Today: Proceedings 46 (P1), 234-242 , 2021
2021
Citations: 22
Influence of drinking water and graphite powder concentration on electrical discharge machining of Ti-6Al-4V alloy
B Gugulothu, GKM Rao, DH Rao
Materials Today: Proceedings 27, 294-300 , 2020
2020
Citations: 15
Optimization of TIG welding process parameters on chrome alloy steel using Box–Behnken method
B Gugulothu, S Karumuri, S Vijayakumar, B Muthuvel, S Seetharaman, ...
International Journal on Interactive Design and Manufacturing (IJIDeM) 18 (9 … , 2024
2024
Citations: 14
Effect of process parameters on centre lathe of EN8 steel in turning process
Bhiksha Gugulothu , Desta Kalbessa Kumsa , Minyahil Bezabih Kassa
Materials Today: Proceedings 46 (P1), 228-233 , 2021
2021
Citations: 14
Design and Fabrication of Patient-Specific Implant for Maxillofacial Surgery Using Additive Manufacturing
R Koppunur, KK Dama, U Rokkala, B Thirupathi, N Sagar, B Gugulothu
2022
Citations: 13
Optimization of EDM process parameters and Graphite powder concentration on electrical Discharge machining of Ti-6Al-4V alloy using Taguchi method
B Gugulothu, DH Rao, GKM Rao
Int. J. Adv. Prod. Mech. Eng 1 (5), 31-44 , 2015
2015
Citations: 13
Machine Learning Models for Predicting Mechanical Properties in Friction Stir Welding of Al Alloys
B Gugulothu, K Srividya, S Vijayakumar, I Veeranjaneyulu, S Revathi, ...
Annals of “Dunarea de Jos” University of Galati. Fascicle XII, Welding … , 2025
2025
Citations: 9
Investigating the strength of butt-welded joints of AA6082 and AA5052 alloys through friction stir welding; the impact of tool tilt angle and feed rate
B Gugulothu, R Saminathan, A Pradeep, A Sharma, S Vijayakumar, ...
Journal of Adhesion Science and Technology, 1-24 , 2024
2024
Citations: 8
Friction stir welded magnesium AZ31B alloy used to evaluate mechanical properties at various rotational speeds
B Gugulothu, PSS Kumar, NS Rao, S Vijayakumar, DR Rajkumar, ...
International Conference on Smart Sustainable Materials and Technologies … , 2023
2023
Citations: 6