Mayank Choubey | Mechanical Engineering | Research Excellence Award

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Dr. Mayank Choubey | Mechanical Engineering | Research Excellence Award

SGT University Gurugram Haryana | India

Dr. Mayank Choubey is an accomplished academic and researcher in the field of manufacturing engineering, currently serving as an Assistant Professor with strong expertise in advanced and digital manufacturing technologies. He holds a solid educational background in mechanical and manufacturing engineering and has gained substantial teaching and research experience at the undergraduate and postgraduate levels. His research interests span hybrid machining, additive and digital manufacturing, micro-machining, simulation-driven process optimization, and finite element analysis, with a focus on improving manufacturing efficiency and precision. He is the author of three ISBN-registered books and has five patents published or under process, demonstrating a strong orientation toward innovation and applied research. In addition, he serves on the editorial board of an academic journal and actively contributes as a peer reviewer. His academic contributions are supported by verified research documents and citation records available through recognized scholarly platforms. Overall, Dr. Choubey’s consistent research output, academic service, and innovation-driven mindset make him a deserving candidate for research excellence recognition.

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Featured Publications

A Review on Vibration-Assisted EDM, Micro-EDM and WEDM
K.P. Maity, M. Choubey, Surface Review and Letters, 26(05), 1830008, 2019. (Citations: 81)

Algae: A Potential Feedstock for Third Generation Biofuel
R.S. Powar, A.S. Yadav, C.S. Ramakrishna, S. Patel, M. Mohan, et al., Materials Today: Proceedings, 63, A27–A33, 2022. (Citations: 60)

Finite Element Modeling of Material Removal Rate in Micro-EDM Process with and without Ultrasonic Vibration
M. Choubey, K.P. Maity, A. Sharma, Grey Systems: Theory and Application, 10(3), 311–319, 2020. (Citations: 33)

Modeling and Process Simulation of Vibration Assisted Workpiece in Micro-EDM Using FEM
K.P. Maity, M. Choubey, World Journal of Engineering, 13(3), 242–250, 2016. (Citations: 16)

A Review on Various Methods to Improve Process Capabilities of Electrical Discharge Machining Process
M. Choubey, M. Rawat, Materials Today: Proceedings, 47, 2756–2764, 2021. (Citations: 10)

Elly Ogutu Isaya | Mechanical Engineering | Research Excellence Award

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Mr. Elly Ogutu Isaya | Mechanical Engineering | Research Excellence Award

Budapest University of Technology and Economics | Hungary

Ogutu Isaya Elly is a mechanical engineering researcher and PhD candidate at the Géza Pattantyús-Ábrahám Doctoral School of Mechanical Sciences, Budapest University of Technology and Economics (BME), specializing in advanced manufacturing and micromachining. He earned a Master’s degree in Mechanical Engineering from Huazhong University of Science and Technology (HUST), China and a Bachelor’s degree in Mechanical Engineering from Jomo Kenyatta University of Agriculture and Technology (J.K.U.A.T), Kenya. His academic experience includes active participation in nationally funded Hungarian research projects focused on AI-based predictive modeling for machining quality and on transient deformation, thermal, and tribological phenomena in fine machining of high-hardness metals. His research interests center on micromachining processes, burr formation mechanisms, and the integration of machine learning within Industry 4.0 manufacturing frameworks. His recent work on burr-size prediction and optimization provides industry-ready solutions to reduce deburring costs and improve surface integrity. He is currently a nominee for the Best Research Article Award, reflecting the applied impact and relevance of his research to modern manufacturing systems.

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Featured Publications

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN Tool
U.L. Adizue, A.D. Tura, E.O. Isaya, B.Z. Farkas, M. Takács,
The International Journal of Advanced Manufacturing Technology, 129(3), 1375–1389, 2023. (Citations: 34)


Analysis, Modelling, and Optimization of Force in Ultra-Precision Hard Turning of Cold Work Hardened Steel Using the CBN Tool
O.I. Elly, U.L. Adizue, A.D. Tura, B.Z. Farkas, M. Takács,
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46, 2024. (Citations: 8)


Optimization of Ultra-Precision CBN Turning of AISI D2 Using Hybrid GA-RSM and Taguchi-GRA Statistical Tools
A.D. Tura, E.O. Isaya, U.L. Adizue, B.Z. Farkas, M. Takács,
Heliyon, 10(11), 2024. (Citations: 5)


Feed Optimization Based on Force Modelling and TLBO Algorithm in Milling Al 7075
O.I. Elly, Y. Yin,
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024. (Citations: 2)


Burr Size Minimization Using a Surrogate Artificial Neural Network (ANN) Assisted Multi-Objective Genetic Algorithm (MOGA) in Micromilling Hardened AISI H13
O.I. Elly, M. Takács, B.Z. Balázs,
The International Journal of Advanced Manufacturing Technology, 2026. (In Press)

Florin Popister | Mechanical Engineering | Engineering Research Excellence Award

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Assoc. Prof. Dr. Florin Popister | Mechanical Engineering | Engineering Research Excellence Award

Technical University of Cluj-Napoca | Romania

Assoc. Prof. Dr. Eng. Florin Popișter is an accomplished researcher and academic in industrial engineering, mechanical design, and robotics. He earned his Ph.D. in Industrial Engineering from the Technical University of Cluj-Napoca, where he currently serves as an Associate Professor, contributing to teaching, research, and supervision in areas such as CAD/CAM systems, industrial robots, reverse engineering, and advanced manufacturing. His professional experience spans both academia and industry, including significant contributions as a design engineer at Gühring Romania, where he developed customized PCD/PCBN tools for the automotive sector. His research includes 3D printing technologies, robotic mechanisms, toolpath generation, workspace optimization, and digital manufacturing, with multiple Q1 journal publications in Polymers, Applied Sciences, and Mathematics. He has led and contributed to national and international research contracts, including H2020 Smart2 and projects focused on automation systems for the pharmaceutical industry. His achievements include a Best Paper Award, participation in Prototypes for Humanity, and invited professorships in Europe. His work continues to advance intelligent manufacturing, Industry 4.0 applications, and smart robotic systems.

Profile : Orcid

Featured Publications

Popescu, D., Dragomir, D., Popișter, F., & Dragomir, M. (2025). AI alignment to enhance production processes performance and resilience. In Book chapter. Springer.

Dragomir, M., Apolțan, D., Cioșan, A., & Popișter, F. (2025). Assessing the potential of emerging digital technologies to transform the production sector. Annals of the Academy of Romanian Scientists Series on Economy, Law and Sociology.

Popișter, F., Ciudin, P., Dragomir, M., & Goia, H. Ș. (2025). From assistive to intelligent: The development of a low-cost smart crutch system. In Book chapter. Springer.

Popișter, F. (2025). Experimental study of comprehensive performance analysis regarding the dynamical/mechanical aspects of 3D-printed UAV propellers and sound footprint. Polymers, 17(11), 1466.

Popișter, F., Goia, H. Ș., & Ciudin, P. (2025). Influence of polymers surface roughness on noise emissions in 3D-printed UAV propellers. Polymers, 17(8), 1015.

Marius Šumanas | Computer Vision | Best Researcher Award

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Assist. Prof. Dr Marius Šumanas | Computer Vision | Best Researcher Award

Vilnius Tech | Lithuania

Dr. Marius Šumanas is an Associate Professor in the Department of Mechatronics, Robotics, and Digital Manufacturing at Vilnius Gediminas Technical University (VILNIUS TECH).  His research interests encompass robotics, machine learning, and digital manufacturing. Dr. Šumanas has authored several publications, including a study on improving positioning accuracy of articulated robots using deep Q-learning algorithms. He has also participated in various conferences, presenting topics related to machine learning applications in robotics. In addition to his academic roles, Dr. Šumanas has practical experience in the industry. Since 2018, he has been working as a Process Engineer and ERP Systems Business Analyst at MB Pramones algoritmas. He has also held positions in marketing and sales management in previous years. Dr. Šumanas’ work bridges the gap between theoretical research and practical application, contributing to advancements in robotics and digital manufacturing.

Profile : Orcid

Featured Publications

Andrijauskas, I., Šumanas, M., Dzedzickis, A., Tanaś, W., & Bučinskas, V. (2025). Computer vision-based optical odometry sensors: A comparative study of classical tracking methods for non-contact surface measurement. Sensors.

Šumanas, M., Treciokaite, V., Čerškus, A., Dzedzickis, A., Bučinskas, V., & Morkvenaite-Vilkonciene, I. (2022). Sitting posture monitoring using Velostat based pressure sensors matrix. In Smart Sensor Systems (pp. 1–12). Springer.

Bučinskas, V., Dzedzickis, A., Šumanas, M., Sutinys, E., Petkevicius, S., Butkiene, J., Virzonis, D., & Morkvenaite-Vilkonciene, I. (2022). Improving industrial robot positioning accuracy to the microscale using machine learning method. Machines, 10(10), 940.

Šumanas, M., Urbonis, D., Petronis, A., Stankaitis, S., Januškevičius, T., Iljin, I., & Dzedzickis, A. (2021). Evaluation of motion characteristics using absolute sensors. In Advances in Mechatronics and Intelligent Robotics (pp. 1–12). Springer.

Šumanas, M., Petronis, A., Urbonis, D., Januskevicius, T., Rasimavicius, T., Morkvenaite-Vilkonciene, I., Dzedzickis, A., & Bučinskas, V. (2021, April 22). Determination of excavator tool position using absolute sensors. In 2021 Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE.