Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan | Industrial IoT | Best Researcher Award

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Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan | Industrial IoT | Best Researcher Award

Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan | Istanbul Okan University | Turkey

Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan is a distinguished academic and technology expert with extensive experience in knowledge management, C4I technologies, management information systems, cybersecurity, project management, and maritime systems. His professional and academic work emphasizes the integration of advanced digital solutions into management and defense applications. He has contributed significantly to education and research in areas such as information systems, business process management, and IT project management, helping bridge the gap between theory and practice. His leadership in major software and simulator projects, including the Full Mission Bridge Simulator, GMDSS Simulator, and municipal smart system software initiatives, highlights his ability to combine innovation with strategic execution. He has also played a key role in institutional development and technology-based research, particularly through his contributions to TÜBİTAK-supported projects and his work in reestablishing academic and training programs within defense-oriented institutions. With 5 published works, 4 citations, and an h-index of 1, his research reflects a growing impact on applied technology and information management fields. Assist. Prof. Dr. Mehmet Bilge Kağan Önaçan continues to advance multidisciplinary innovation, blending academic rigor with practical technological solutions.

Profile: Scopus

Featured Publication

Önaçan, M. B. K. (2025). Designing automatic card dispensers based on design thinking approach and selecting the suitable alternative. Journal of Engineering and Applied Science, Open access.

 

Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

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Assist. Prof. Dr. Yan Zeng | AI in Engineering | Best Researcher Award

Assist. Prof. Dr. Yan Zeng | Hangzhou Dianzi University | China

Assist. Prof. Dr. Yan Zeng, an accomplished associate professor at the School of Computer Science, Hangzhou Dianzi University, has made significant contributions in the fields of distributed and parallel computing, distributed machine learning, and big data analytics. After earning her PhD from the Institute of Software, Chinese Academy of Sciences in 2016, her research has focused on advancing large-scale computation and data-intensive systems.  The Key Research and Development Program of Zhejiang Province, the Yangtze River Delta Project, and the Natural Science Foundation of Zhejiang Province. Her academic influence is reflected in 173 citations by 161 documents, 42 published papers, and an h-index of 9, demonstrating strong research impact and visibility. With 10 peer-reviewed publications in SCI and Scopus-indexed journals, Yan Zeng’s scholarly output showcases innovation in computational frameworks and distributed systems. Furthermore, she has been actively involved in practical technological advancements, holding 34 patents that bridge theoretical insights with industrial applications. Through her extensive research, publication record, and innovation-driven approach, Yan Zeng continues to play a pivotal role in shaping advancements in computer science and data engineering.

Profile: Scopus

Featured Publications

Zeng, Y., et al. (2025). FedAMM: Federated learning for brain tumor segmentation with arbitrary missing modalities [Conference paper]. Proceedings of the International Conference on Artificial Intelligence and Machine Learning.

Zeng, Y., et al. (2025). TransAware: An automatic parallel method for deep learning model training with global model structure awareness [Conference paper]. Proceedings of the International Conference on Advanced Computing and Applications.

Zeng, Y., et al. (2025). A correlation analysis-based federated learning framework for defending against collusion-free-riding attacks. Cybersecurity, 2025(1), 1–12.

Zeng, Y., et al. (2025). FedAEF: Optimizing federated learning with mining and enhancing local data features. Cluster Computing, 2025(1), 1–15.

Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

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Mr. Adizue Ugonna | AI in Engineering | Best Researcher Award

Mr. Adizue Ugonna | Budapest University of Technology and Economics | Hungary

Mr. Adizue Ugonna Loveday is a Doctoral Researcher and Laboratory Instructor at the Budapest University of Technology and Economics, specializing in Mechanical Engineering with expertise in industrial and production systems. His research focuses on intelligent modelling and process optimization for ultra-precision machining of hard materials, integrating artificial intelligence, tribological analysis, and thermal modeling to enhance manufacturing precision and efficiency. Professionally, he has contributed to several major research initiatives including the Horizon 2020 Centre of Excellence in Production Informatics and Control (EPIC CoE), the iNext project on industrial digitalization, and multiple Hungarian Scientific Research Fund (OTKA) projects emphasizing AI-based predictive models for advanced machining and intelligent forming processes. His scholarly record demonstrates strong research performance, with 45 citations by 42 documents, 6 documents, and an h-index of 4 in Scopus; and 66 citations, an h-index of 5, and an i10-index of 2 in Google Scholar. In addition, his ORCID profile lists 6 professional activities and 8 published works, reflecting active engagement in international research collaboration, scientific reviewing, and production editing. Through these contributions, Mr. Loveday continues to advance smart and sustainable manufacturing technologies, bridging artificial intelligence and mechanical systems design in alignment with Industry 4.0 innovation goals.

Profiles: Scopus | Orcid | Google Scholar | LinkedIn | ResearchGate

Publication Details

  1. Adizue, U. L., Tura, A. D., Isaya, E. O., Farkas, B. Z., & Takács, M. (2023). Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool. The International Journal of Advanced Manufacturing Technology, 128(1), 1–28.

  2. Adizue, U. L., Nwanya, S. C., & Ozor, P. A. (2020). Artificial neural network application to a process time planning problem for palm oil production. Engineering and Applied Science Research, 47(2), 161–169.

  3. Adizue, U. L., & Takács, M. (2025). Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: A comparative study. The International Journal of Advanced Manufacturing Technology, 1–30.

  4. Elly, O. I., Adizue, U. L., Tura, A. D., Farkas, B. Z., & Takács, M. (2024). Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(1), 1–18.

  5. Adizue, U. L., Balázs, B. Z., & Takács, M. (2022). Surface roughness prediction applying artificial neural network at micro machining. IOP Conference Series: Materials Science and Engineering, 1246(1), 012034.

  6. Tura, A. D., Isaya, E. O., Adizue, U. L., Farkas, B. Z., & Takács, M. (2024). Optimization of ultra-precision CBN turning of AISI D2 using hybrid GA-RSM and Taguchi-GRA statistic tools. Heliyon, 10(11), e24357.

  7. Adizue, U. L., Agbadah, S. E., Ibeagha, D. C., & Falade, Y. O. (2017). Design and construction of an automated adjustable-can foil sealing machine. International Journal of Engineering and Applied Sciences, 4(9), 257384.

 

Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

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Dr. Zhu Jingwen | AI in Engineering | Best Researcher Award

Dr. Zhu Jingwen | Jiangsu University | China

Dr. Zhu Jingwen is a researcher in control science and engineering whose work focuses on intelligent detection systems and advanced sensing technologies for agricultural safety. His research emphasizes the development of nondestructive testing methods for grains and edible oils through the integration of microwave, millimeter-wave, and near-infrared technologies with chemometric modeling and machine learning algorithms. Dr. Zhu has designed FPGA-based microwave detection systems capable of accurately identifying contaminants such as heavy metals and aflatoxins, contributing significantly to the field of food safety monitoring. His studies have been widely published in respected international journals, including Microchemical Journal, Sensors and Actuators A: Physical, and Spectrochimica Acta Part A. Beyond research, he has demonstrated leadership in innovation and entrepreneurship, leading projects recognized with national honors such as the China Postgraduate “Rural Revitalization – Sci-Tech Empowering Agriculture+” Competition. His scientific contributions are reflected through 13 published documents, cited 46 times by 41 other documents, demonstrating a growing academic impact and an h-index of 5. His efforts also led to the establishment of Dongfang Xiangyu (Jiangsu) Technology Co., Ltd., translating research outcomes into practical industrial applications. With a strong command of programming and embedded system development, Dr. Zhu continues to explore interdisciplinary approaches that merge intelligent algorithms with hardware systems to advance the precision and reliability of agricultural quality assessment technologies.

Profile: Scopus

Featured Publications

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Quantitative determination of cadmium content in peanut oil using microwave detection method combined with multivariate analysis. Microchemical Journal, 110946.

Zhu, J., Deng, J., Zhao, X., Xu, L., & Jiang, H. (2024). Accurate identification of cadmium pollution in peanut oil using microwave technology combined with SVM-RFE. Sensors and Actuators A: Physical, 368, 115085.

Zhu, J., Chen, Y., Deng, J., & Jiang, H. (2024). Improving the accuracy of FT-NIR determination of zearalenone content in wheat using a characteristic wavelength optimization algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 313, 124169.

Ji, Z., Zhu, J., Deng, J., Jiang, H., & Chen, Q. (2024). Quantitative determination of zearalenone in wheat by the CSA-NIR technique combined with chemometric algorithms. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 323, 124858.

Zhu, J., Deng, J., Xu, L., & Jiang, H. (2024). Enhancing the performance of natural pigment sensor arrays for the detection of Procymidone residues in Allium tuberosum using outcome-corrected decision-making method. Journal of Food Composition and Analysis, 128, 107059.

Prof. Dr. Emad Solouma | Computational Mechanics | Best Researcher Award

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Prof. Dr. Emad Solouma | Computational Mechanics | Best Researcher Award

Prof. Dr. Emad Solouma | Imam Mohammad Ibn Saud Islamic University | Saudi Arabia

Prof. Dr. Emad Mohamed Solouma is an accomplished researcher whose work focuses on advanced studies in differential geometry, kinematic surfaces, and applied mathematics. His scholarly contributions explore geometric modeling, scalar curvature, and the properties of curves and surfaces within Euclidean and Minkowski spaces.With 96 citations by 75 documents, 32 scientific publications, and an h-index of 5, his academic influence continues to grow across the global research community . He has published extensively in reputed international journals such as Applied Mathematics, Journal of Geometry, and International Journal of Applied and Computational Mathematics, addressing topics like constant scalar curvature, cyclic and tubular surfaces, and fractional variational problems. His research also delves into the geometric behavior of surfaces generated by equiform and homothetic motions, as well as analytical techniques for solving fractional differential equations. Prof. Solouma’s investigations provide mathematical frameworks for understanding higher-dimensional kinematic surfaces and their curvature characteristics, contributing to the theoretical foundations of geometry and its computational applications. His consistent output of high-quality publications and conference presentations underscores his role in advancing mathematical theory and its interdisciplinary applications in modern geometry and computational modeling.

Profile: Orcid | Scopus

Featured Publications

Solouma, E., Saber, S., & Baskonus, H. M. (2025, July). Exploring harmonic evolute geometries derived from tubular surfaces in Minkowski 3-space using the RM Darboux frame. Mathematics, 13(15), 2329.

Ragheb, N. M., Solouma, E., Alahmari, A. A., & Saber, S. (2025, July). Reliability and availability analysis of a two-unit cold standby system with imperfect switching. Axioms, 14(8), 589.

Saber, S., Solouma, E., Althubyani, M., & Messaoudi, M. (2025, May). Statistical insights into zoonotic disease dynamics: Simulation and control strategy evaluation. Symmetry, 17(5), 733.

Saber, S., & Solouma, E. (2025, April). The generalized Euler method for analyzing zoonotic disease dynamics in baboon–human populations. Symmetry, 17(4), 541.

Solouma, E., Al-Dayel, I., & Abdelkawy, M. A. (2025, March). Ruled surfaces and their geometric invariants via the orthogonal modified frame in Minkowski 3-space. Mathematics, 13(6), 940.

Saber, S., Solouma, E., Alharb, R. A., & Alalyani, A. (2025, February). Chaos in fractional-order glucose–insulin models with variable derivatives: Insights from the Laplace–Adomian decomposition method and generalized Euler techniques. Fractal and Fractional, 9(3), 149.

 

 

Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

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Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

Ms. Asima Sarwar | Ghulam Ishaq Khan Institute of Engineering Sciences and Technology | Pakistan

Ms. Asima Sarwar is a computer engineer and researcher with expertise in Artificial Intelligence, Data Engineering, and Machine Learning. She is pursuing a PhD in Computer Engineering with research focused on AI, data analytics, and distributed computing systems. Her academic background includes a master’s degree in Computer Systems Engineering, specializing in Smart Grids and the Internet of Things, and a bachelor’s degree in Electrical Engineering (Communication). She has professional experience as a computer engineer, research assistant, and lecturer, contributing to projects in secure IoT device development, cyber-secure systems, and AI-based data processing. Ms. Asima has taught various undergraduate and postgraduate courses including Big Data Analytics, Machine Learning, Generative AI, Operating Systems, and Ethical AI. Her work emphasizes technical innovation, algorithmic optimization, and the integration of intelligent systems for real-world applications. With strong analytical and problem-solving skills, she is actively involved in advancing research in AI-driven technologies, data engineering, and computer vision. Her contributions reflect a balance between academic rigor, applied research, and technological development aimed at improving system efficiency and advancing modern computing solutions.

Profile: Scopus

Featured Publications

  • Sarwar, A., Usman, M., Hussain, M., Jadoon, K. K., Manzoor, T., & Ali, S. (2025). AI-powered deep ultraviolet laser diode design for resource-efficient optimization. Journal of Computational Electronics, 24(4), 1–19.

  • Mahmood, M. A., Maab, I., Sibtain, M., Sarwar, A., Arsalan, M., & Hussain, M. (2025, March). Advancements in sentiment analysis: A methodological examination of news using multiple LLMs. In Proceedings of the 31st Annual Meeting of the Association for Natural Language Processing.

  • Sarwar, A., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Enhanced anomaly detection system for IoT based on improved dynamic SBPSO. Sensors MDPI, 22(4926).

  • Sarwar, A., Hassan, S., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Design of an advance intrusion detection system for IoT networks. In Proceedings of the 2nd International Conference on Artificial Intelligence (ICAI) (pp. 46–51).

  • Ijaz, A. Z., Ali, R. H., Sarwar, A., Khan, T. A., & Baig, M. M. (2022). Importance of synteny in homology inference. In Proceedings of the IEEE International Conference on Emerging Technologies (ICET).

  • Azam, T., Tahir, F. A., Sarwar, A., & Qayyum, M. A. (2023). A high gain wide band compact size dual band patch antenna for 5G application. In Proceedings of the IEEE International Conference on Emerging and Sustainable Technologies (ICEST) (pp. 1–3).

 

 

Dr. Christen Tharwat | Smart Materials | Best Researcher Award

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Dr. Christen Tharwat | Smart Materials | Best Researcher Award

Dr. Christen Tharwat | National Research Centre | Egypt

Dr. Christen Tharwat is a Postdoctoral Researcher specializing in nanophotonics, advanced materials, and applied nanotechnology with extensive experience in developing optical sensors for environmental applications and nanomaterial synthesis for biomedical and industrial use. Her research integrates plasmonic and graphene-based gas sensors, magnetic and semiconductor nanoparticles, and nanotechnology for wastewater treatment and biomedical coatings. 39 Citations by 39 documents 11 Documents 4 h-index View h-index button is disabled in preview mode At the National Research Centre in Egypt, she has contributed significantly to the Metal Physics Lab, focusing on innovative metal alloy treatments, ceramic coatings for implants, and solid-state material enhancements. Her academic background includes advanced studies in laser-enhanced sciences, emphasizing the synthesis and modification of nanostructured materials for functional applications. In addition to her research, she has been actively involved in academic and scientific writing, preparing manuscripts, research projects, and journal articles that align with institutional and publication standards. Her scientific contributions reflect a commitment to advancing the applications of nanotechnology in health, environment, and materials science.

Profiles: Scopus | Google Scholar

Featured Publications

Girgis, E., Adel, D., Tharwat, C., Attallah, O., & Rao, K. V. (2015). Cobalt ferrite nanotubes and porous nanorods for dye removal. Advances in Nano Research, 3(2), 111.

Mahmoud, A. H. K., Korany, F. M. H., Tharwat, C., Hussein, M., Swillam, M. A., et al. (2020). Surface roughness effect on characteristics of Si nanowire solar cell. Journal of Photonics for Energy, 10(4), 045502–045502.

Girgis, E., Tharwat, C., & Adel, D. (2016). Ferrites nanoflowers for dye removal applications. Journal of Advanced Nanomaterials, 1, 49–56.

Gouda, A. M., Elsayed, M. Y., Tharwat, C., & Swillam, M. A. (2016). Silicon-based nanostructures as surface enhanced Raman scattering substrates. Proceedings of Photonics North (PN), 1.

Aziz, M. A. S. C., Othman, M. A., Amer, A., & Ghanim, A. R. M. (2024). Fabrication of crystalline silicon nanowires coated with graphene from graphene oxide on amorphous silicon substrate using excimer laser. Heliyon.

Tharwat, C., Badr, Y., Ahmed, S. M., Bishay, I. K., & Swillam, M. A. (2024). CW laser beam-based reduction of graphene oxide films for gas sensing applications. Optical and Quantum Electronics, 57(1), 69.

Mr. Veluchamy M | Additive Manufacturing | Best Researcher Award

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Mr. Veluchamy M | Additive Manufacturing | Best Researcher Award

Mr. Veluchamy M | National Institute of Technology Tiruchirappalli | India

Mr. Veluchamy M is a Ph.D. Research Scholar at the Department of Production Engineering, National Institute of Technology Tiruchirappalli, specializing in the study of additively manufactured SS316L materials. His research focuses on analyzing and enhancing the microstructural, mechanical, corrosion, and tribological behavior of these components through advanced surface modification techniques such as heat treatment and laser shock peening. He has contributed significantly to the understanding of surface integrity and performance improvement in selective laser melted materials, publishing five SCIE and ESCI-indexed papers in reputed international journals. His investigations explore tribological performance under lubricated conditions, microstructural evolution, and the mechanical response of post-processed stainless steel components. His research employs multidisciplinary approaches, integrating material science, manufacturing processes, and mechanical testing to achieve optimized engineering performance. His work provides valuable insights for industrial applications where surface durability and wear resistance are critical. Through his continued studies and experimental advancements, he contributes to the development of high-performance materials and the broader field of additive manufacturing, supporting the evolution of sustainable engineering solutions and innovative manufacturing technologies for next-generation industrial systems.

Profile: Google Scholar

Featured Publications

Veluchamy, M., Somasundaram, K., & Satheeshkumar, V. (2024). Investigations on tribological behavior under lubricated condition of post heat treated additively manufactured SS316L parts. Industrial Lubrication and Tribology, 76(7/8), 1003–1014.

Veluchamy, M., & Somasundaram, K. (2025). Influence of laser shock peening on the tribological behavior of additively manufactured SS316L. Surface Review and Letters.

Veluchamy, M., & Kumanan, S. (2025). Estimation of tribological performance of selective laser melted SS316L under lubricated conditions using MCDM techniques. Multiscale and Multidisciplinary Modeling, Experiments and Design, 8(8), 369.

Veluchamy, M., & Kumanan, S. (2025). Effect of double-side laser shock peening on tensile behavior of selective laser-melted 316L SS parts. Journal of Failure Analysis and Prevention, 1–15.

Veluchamy, M., & Kumanan, S. (2025). Microstructural and mechanical characterization of heat-treated and laser shock-peened SS316L fabricated by selective laser melting. Journal of Materials Engineering and Performance, 1–21.

Mrs. Tharangika Bambaravanage | Green Technologies | Green Energy Award

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Mrs. Tharangika Bambaravanage | Green Technologies | Green Energy Award

Mrs. Tharangika Bambaravanage | Institute of Technology, University of Moratuwa | Sri Lanka

Mrs.  Tharangika Bambaravanage is a Senior Lecturer at the Institute of Technology, University of Moratuwa, with more than 20 years of experience in electrical engineering. Her research focuses on renewable energy, green technologies, and power system stability, contributing to the advancement of sustainable and efficient power systems. She has completed 4 major research projects and is currently leading 1 ongoing project in collaboration with the Ceylon Electricity Board on minimizing the impact of solar photovoltaic (PV) penetration on the power quality of distribution lines. Dr. Bambaravanage has published 7 academic documents indexed in Scopus, with citations by 3 documents and an h-index of 1. According to Google Scholar, she has received 42 total citations and 27 in recent years, with an h-index of 5 and 3 respectively. Her scholarly work includes 4 journal papers, 2 book chapters, and 1 published book addressing issues in power system operation and sustainable energy management. As a Chartered Engineer and professional member of the Institute of Electrical and Electronics Engineers and the Institution of Engineers Sri Lanka, she plays a key role in bridging academia and industry while driving innovation in renewable and resilient energy systems.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

  • Bambaravanage, T., Kumarawadu, S., & Rodrigo, A. (2016). Comparison of three under-frequency load shedding schemes referring to the power system of Sri Lanka. Engineer: Journal of the Institution of Engineers, Sri Lanka, 49(1), 9.

  • Bambaravanage, T., Rodrigo, A. S., Kumarawadu, S. P., & Lidula, N. (2013). A new scheme of under frequency load shedding and islanding operation. Annual Transactions of the Institution of Engineers, Sri Lanka, 290–296.

  • Dilushani, P. D. R., Nawodani, W. R. N., Bambaravanage, T., & Udayakumar, K. A. C. (2020). Soil resistivity analysis and earth electrode resistance determination. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 15(2), 26–35.

  • Bambaravanage, T., Rodrigo, A., & Kumarawadu, S. (2018). Modeling, simulation, and control of a medium-scale power system. Springer.

  • Bambaravanage, T., Kumarawadu, S., Rodrigo, A., & Arachchige, L. N. W. (2013). Under-frequency load shedding for power systems with high variability and uncertainty. In 2013 IEEE International Conference on Signal Processing, Computing and Control (pp. 1–6).

  • Bambaravanage, T., Perera, C., & Rodrigo, A. (2025). An effective stability solution for small power systems with high distributed generation. Discover Energy, 5(1), 17.

  • Bambaravanage, T., Rodrigo, A., & Kumarawadu, S. (2017). Designing the load shedding scheme. In Modeling, Simulation, and Control of a Medium-Scale Power System (pp. 97–120). Springer.

 

Assist. Prof. Dr Andrei Pilipchuk | Computational Mechanics | Best Researcher Award

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Assist. Prof. Dr Andrei Pilipchuk | Computational Mechanics | Best Researcher Award

Assist. Prof. Dr Andrei Pilipchuk | Belarusian National Technical University | Belarus

Assist. Prof. Dr Andrei Pilipchuk research focuses on the interaction of laser radiation with solid materials, emphasizing the mechanisms of stress formation and material response during laser processing. His work involves solving complex problems related to internal stress determination under laser irradiation and developing analytical and computational models to optimize technological processes. Through studies on stresses in laser processing, crack formation after laser treatment of thermal spray coatings, and the behavior of surface layers under pulsed laser irradiation, he has contributed to a deeper understanding of residual stress distribution and its impact on material performance. His research on calculation schemes for laser processing and the analysis of stress fields in components such as blades used in descaling after laser hardening has helped establish methods for predicting and minimizing structural damage. By integrating theoretical modeling with practical laser applications, his work supports advancements in laser manufacturing, materials engineering, and the broader field of industrial surface modification. His research contributions have been recognized through 34 citations from 17 documents and an h-index of 2, reflecting the growing impact of his work in the field of laser-material interaction.

Profile: Google Scholar | Orcid

Featured Publications

Pilipchuk, A. P., Devoyno, O. G., & Zharskiy, V. V. (2019). Modeling of surface hardening using a scanning fiber laser. Izvestiya of the National Academy of Sciences of Belarus, Series of Physical and Technical Sciences, 9 citations.

Devoyno, O. G., Pilipchuk, A. P., & Kocherov, A. L. (2014). Evaluation of the stress state during laser processing of gas-thermal coatings. Belarusian National Technical University (BNTU), 3 citations.

Devoyno, O. G., Volodko, A. S., Pilipchuk, A. P., Devoyno, D. G., & Mishin, A. A. (2020). Formation of multilayer coatings of ultra-high molecular weight polyethylene on para-aramid fabrics by flame spraying. Belarusian National Technical University (BNTU), 2 citations.

Devoyno, O. G., Pilipchuk, A. P., & Lochs, S. (2019). Formation of functionally graded coatings by combined gas-thermal spraying and laser processing. Topical Issues of Mechanical Engineering, 8, 277–282. 2 citations.

Devoyno, O. G., Zharskiy, V. V., Pilipchuk, A. P., & Rudyy, V. V. (2019). Hardening of large-sized parts using scanning fiber laser radiation with programmable power variation. Photonics, 13(6), 524–531. 2 citations.

Pilipchuk, A. P., Devoyno, O. G., Gribkov, Y. A., Lutsko, N. I., & Romanov, D. A. (2016). Use of self-fluxing alloys for creating parts by selective laser sintering. Bulletin of the Military Academy of the Republic of Belarus, 165–173. 2 citations.

Devoyno, O. G., Zharskiy, V. V., Kardapolova, M. A., Lutsko, N. I., & Pilipchuk, A. P. (2016). Study of the surface formation process by selective laser sintering from PG-SR3 alloy. Proceedings of the Conference “Modern Methods and Technologies for Creating and Processing Materials”, 2 citations.