Dr. Edward Reutzel | Additive Manufacturing Process Planning | Best Researcher Award

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Dr. Edward Reutzel | Additive Manufacturing Process Planning | Best Researcher AwardΒ 

Research Professor, Penn State University, Applied Research Laboratory, United States

Edward W. (Ted) Reutzel is a renowned expert in additive manufacturing and materials processing. As the Director of the Center for Innovative Material Processing thru Direct Digital Deposition at Pennsylvania State University, Reutzel leads cutting-edge research in additive manufacturing. With a strong background in mechanical engineering, Reutzel has made significant contributions to the development of innovative materials processing techniques. Their research has far-reaching implications for industries such as aerospace, healthcare, and energy.

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πŸŽ“ Education

Reutzel holds a Ph.D. in Mechanical Engineering from Pennsylvania State University (2007), an M.S. in Mechanical Engineering from the Georgia Institute of Technology (1993), and a B.S. in Mechanical Engineering from Pennsylvania State University (1991). Their educational background has provided a solid foundation in mechanical engineering principles and prepared them for a career in research and development.

πŸ‘¨β€πŸ”¬ Experience

Reutzel has held various positions, including Director of the Center for Innovative Material Processing thru Direct Digital Deposition, Associate Research Professor at ARL Penn State, and Graduate Faculty in the Mechanical Engineering Department and Additive Manufacturing and Design Program at Penn State. With over two decades of experience in research and development, Reutzel has demonstrated expertise in additive manufacturing, materials processing, and laser systems engineering.

πŸ” Research Interest

Reutzel’s research focuses on additive manufacturing, materials processing, and laser systems engineering. Their work explores innovative techniques for direct digital deposition, process monitoring, and defect detection in additive manufacturing. With applications in industries such as aerospace and healthcare, Reutzel’s research has the potential to transform manufacturing processes and improve product quality.

Awards and Honors πŸ†

Although specific awards and honors are not detailed in the provided information, Reutzel’s research achievements and leadership roles suggest a high level of recognition within the field of additive manufacturing. Their certification and involvement in various research projects demonstrate a commitment to excellence and a strong reputation among peers.

πŸ“š Publications

 

1. Automated defect recognition for additive manufactured parts using machine perception and visual saliency πŸ€–
2. IN SITU LASER ULTRASOUND-BASED RAYLEIGH WAVE PROCESS MONITORING OF DED-AM METALS πŸ’‘
3. Multi-spectral method for detection of anomalies during powder bed fusion additive manufacturing πŸ”
4. Effect of interlayer temperature on meltpool morphology in laser powder bed fusion πŸ”₯
5. Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturing πŸ“Š
6. Electro-strengthening of the additively manufactured Ti–6Al–4V alloy πŸ’ͺ
7. Effect of processing conditions on the microstructure, porosity, and mechanical properties of Ti-6Al-4V repair fabricated by directed energy deposition πŸ”©
8. Formation processes for large ejecta and interactions with melt pool formation in powder bed fusion additive manufacturing 🌐
9. Multi-sensor investigations of optical emissions and their relations to directed energy deposition processes and quality πŸ”Ž
10. Design and evaluation of an additively manufactured aircraft heat exchanger ❄️

Conclusion

Edward W. (Ted) Reutzel is an outstanding researcher with a strong background in additive manufacturing and mechanical engineering. Their extensive research experience, leadership roles, and prolific publication record make them an excellent candidate for the Best Researcher Award. While there are areas for improvement, Reutzel’s research achievements and potential for future impact make them a compelling candidate for this award.

Tadeu Castro da Silva | Additive manufacturing technologies | Best Researcher Award

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Assist. Prof. Dr Tadeu Castro da Silva | Additive manufacturing technologies | Best Researcher Award

Prof. Dr-Ing, National Institute of Technology, Portugal

T.C. da Silva is a researcher and engineer with a strong background in mechanical engineering. He holds a PhD from the University of BrasΓ­lia and has completed postdoctoral research at various institutions. Silva’s research focuses on smart materials, additive manufacturing, and thermal characterization.

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Education πŸŽ“

PhD in Mechanical Engineering, University of BrasΓ­lia (2019) Β Master’s in Mechanical Engineering, University of BrasΓ­lia (2014) Β Specialization in Software Engineering, Catholic University of BrasΓ­lia (2009-2010) Β Bachelor’s in Mechanical Engineering, University for the Development of the State and Region of Pantanal (2003-2008)

Experience πŸ§ͺ

Researcher, University of BrasΓ­lia (2012-present) Β Postdoctoral researcher, University of BrasΓ­lia (2020-2021) Β Engineer, Brazilian Air Force (2011-2012) Β Professor, Federal Institute of Education, Science, and Technology (2005-2007)

Awards & HonorsπŸ†

Unfortunately, the provided text does not mention any specific awards or honors received by T.C. da Silva.

Research Focus πŸ”

Smart materials and structures Β Additive manufacturing (3D/4D printing) Thermal characterization of materials Β Shape memory alloys

PublicationsπŸ“š

1. The effect of a chemical additive on the fermentation and aerobic stability of high-moisture corn 🌽🧬 (2015)
2. Filho TC da Silva, E Sallica-Leva, E RayΓ³n, CT Santos transformation πŸ”©πŸ”§ (2018)
3. Emissivity measurements on shape memory alloys πŸ”πŸ’‘ (2016)
4. Development of a gas metal arc based prototype for direct energy deposition with micrometric wire πŸ’»πŸ”© (2024)
5. Influence of Deep Cryogenic Treatment on the Pseudoelastic Behavior of the Ni57Ti43 Alloy β„οΈπŸ’‘ (2022)
6. Stainless and low-alloy steels additively manufactured by micro gas metal arc-based directed energy deposition: microstructure and mechanical behavior πŸ”©πŸ”§ (2024)
7. Study of the influence of high-energy milling time on the Cu–13Al–4Ni alloy manufactured by powder metallurgy process βš—οΈπŸ’‘ (2021)
8. Cryogenic treatment effect on NiTi wire under thermomechanical cycling β„οΈπŸ’‘ (2018)
9. Effect of Cryogenic Treatment on the Phase Transformation Temperatures and Latent Heat of Ni54Ti46 Shape Memory Alloy β„οΈπŸ’‘ (2022)
10. Cryogenic Treatment Effect on Cyclic Behavior of Ni54Ti46 Shape Memory Alloy β„οΈπŸ’‘ (2021)
11. Influence of thermal cycling on the phase transformation temperatures and latent heat of a NiTi shape memory alloy πŸ”©πŸ”§ (2017)
12. Effect of the Cooling Time in Annealing at 350Β°C on the Phase Transformation Temperatures of a Ni55Ti45 wt. Alloy πŸ”©πŸ”§ (2015)
13. Experimental evaluation of the emissivity of a NiTi alloy πŸ”πŸ’‘ (2015)
14. Microstructure, Thermal, and Mechanical Behavior of NiTi Shape Memory Alloy Obtained by Micro Wire and Arc Direct Energy Deposition πŸ”©πŸ”§ (2025)
15. Low-Annealing Temperature Influence in the Microstructure Evolution of Ni53Ti47 Shape Memory Alloy πŸ”©πŸ”§ (2024)
16. Use of Infrared Temperature Sensor to Estimate the Evolution of Transformation Temperature of SMA Actuator Wires πŸ”πŸ’‘ (2023)
17. Use of infrared temperature sensor to estimate the evolution of transformation temperature of SMA actuator wires πŸ”πŸ’‘ (2021)
18. Effet du traitement cryogΓ©nique sur le comportement cyclique de l’alliage Ni54Ti46 Γ  mΓ©moire de forme β„οΈπŸ’‘ (2020)
19. Efeito de tratamento criogΓͺnico no comportamento cΓ­clico da liga Ni54Ti46 com memΓ³ria de forma β„οΈπŸ’‘ (2020)
20. Functional and Structural Fatigue of NiTi Shape Memory Wires Subject to Thermomechanical Cycling πŸ”©πŸ”§ (2019)

Conclusion

T.C. da Silva is an accomplished researcher with a strong track record in additive manufacturing, materials science, and mechanical engineering. His extensive research experience, interdisciplinary approach, and commitment to knowledge sharing make him an ideal candidate for the Best Researcher Award. By addressing areas for improvement, he can continue to grow as a researcher and make even more significant contributions to his field.

Zicheng Xin | intelligentialization | Best Researcher Award

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Dr. Zicheng Xin | intelligentialization | Best Researcher Award

postdoctor, University of Science and Technology Beijing, China

Zicheng Xin is a renowned researcher and visiting professor at the Korea Invention Academy. He is affiliated with the University of Science and Technology Beijing (USTB) and has made significant contributions to the field of metallurgical engineering. His research focuses on metallurgical process engineering, intelligence, and simulation.

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scopus

Education πŸŽ“

Ph.D. in Metallurgical Engineering, State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing (USTB) (2018-2022)

Experience πŸ§ͺ

Visiting Professor, Korea Invention Academy (current) Β Researcher, State Key Laboratory of Advanced Metallurgy, USTB (current)

Awards & HonorsπŸ†

β€œMultiscale modeling and collaborative manufacturing for steelmaking plants”, the 10th World Scientist Grand Award β€” Golden Scientist Grand Award (Second Place, International Federation of Inventors’ Associations, 2023) β€œMultiscale modeling and collaborative manufacturing for steelmaking plants”, the 10th World Scientist Grand Awardβ€” Science & Technology Grand

Research Focus πŸ”

Metallurgical process engineering and intelligence Β Simulation and optimization of metallurgical process

PublicationsπŸ“š

1. Analysis of multi-zone reaction mechanisms in BOF steelmaking and comprehensive simulation [J]. Materials, 2025, 18(5): 1038. – Zicheng Xin, Qing Liu, Jiangshan Zhang, et al.
2. Modeling of LF refining process: a review [J]. Journal of Iron and Steel Research International, 2024, 31(2): 289-317. – Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
3. Explainable machine learning model for predicting molten steel temperature in LF refining process [J]. International Journal of Minerals, Metallurgy and Materials, 2024, 31(12): 2657-2669. – Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
4. Predicting temperature of molten steel in LF refining process using IF-ZCA-DNN model [J]. Metallurgical and Materials Transactions B, 2023, 54(3): 1181-1194. – Zicheng Xin, Jiangshan Zhang, Junguo Zhang, et al.
5. Predicting the alloying element yield in a ladle furnace using principal component analysis [J]. … – Zicheng Xin, Jiangshan Zhang, Yu Jin, et al.

Conclusion

Zicheng Xin’s academic excellence, research focus, and international recognition make him a strong candidate for the Best Researcher Award. While there are areas for improvement, his strengths and achievements demonstrate his potential to make significant contributions to the field of metallurgy.

Nahid Entezarian | Machine Interaction | Best Researcher Award

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Ms. Nahid Entezarian | Machine Interaction | Best Researcher Award

Author, University of Mashhad, Mashhad, Iran

Nahid Entezarian is a Ph.D. candidate in Information Technology Management at Ferdowsi University of Mashhad. Her research interests include text mining, data mining, NeuroIS, artificial intelligence, machine learning, and research methodology in information systems.

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Education πŸŽ“

Nahid Entezarian is currently pursuing her Ph.D. in Information Technology Management at Ferdowsi University of Mashhad, specializing in Smart Business. Her academic background has provided a solid foundation for her research and professional endeavors.

Experience πŸ§ͺ

Unfortunately, the provided text does not mention specific work experience or professional roles held by Nahid Entezarian.

Awards & Honors οΏ½

Unfortunately, the provided text does not mention specific awards or honors received by Nahid Entezarian.

Research Focus πŸ”

1. Text Mining: Investigating the application of text mining techniques in various domains.
2. Data Mining: Exploring the use of data mining methods for knowledge discovery.
3. NeuroIS: Examining the intersection of neuroscience and information systems.
4. Artificial Intelligence: Investigating the application of AI in various domains.
5. Machine Learning: Developing and applying machine learning algorithms for data analysis.

PublicationsπŸ“š

1. An investigation extent and factors influencing the users’ perception of database interface based on Nielsen model πŸ“Š
2. GUIDELINES FOR USER INTERFACE DESIGN BASED ON USERS’BEHAVIORS, EXPECTATIONS AND PERCEPTIONS πŸ“ˆ
3. Topic Modeling on System Thinking Themes Using Latent Dirichlet Allocation, Non-Negative Matrix Factorization and BER Topic πŸ€–
4. NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis 🧠
5. The Application of Artificial Intelligence in Smart Cities: A Systematic Review with Methodi Ordinatio πŸŒ†
6. Systems Thinking in the Circular Economy: An Integrative Literature Review ♻️
7. The impact of knowledge management and Industry 4.0 technologies in organizations: a meta-synthesis approach πŸ“ˆ
8. Topic Modeling Emerging Trends for Business Intelligence in Marketing: With Text Mining and Latent Dirichlet Allocation πŸ“Š
9. Topic Modeling Emerging Trends for Business Intelligence in Marketing: With Text Mining and Latent Dirichlet Allocation πŸ“Š
10. Introducing and Evaluation of Rogers’s Diffusion Innovation Theory πŸ“ˆ

Conclusion πŸ†

Nahid Entezarian’s impressive academic and research experience, research output, interdisciplinary research approach, and collaborations make her an outstanding candidate for the Best Researcher Award. While there are areas for improvement, her strengths and achievements demonstrate her potential to make a significant impact in her field.

Sabum Jung | Smart factory | Best Researcher Award

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Mr. Sabum Jung | Smart factory | Best Researcher Award

Research engineer, Lg energy solution,South Korea

Sabum Jung is a seasoned Data Scientist and Machine Learning Engineer with over 23 years of expertise in predictive modeling, deep learning, and AI-driven optimization. His career spans LG Energy Solution, SK Holdings, and LG Production Engineering Research Institute, where he pioneered AI applications in high-tech manufacturing, including semiconductor, battery, and display industries. A former Military Intelligence Analyst for the U.S. Army, he has authored research papers and books on AI, machine learning, and Industry 4.0. Fluent in English, Korean, and Japanese, he continues to drive AI innovations in industrial applications.

Profile

πŸŽ“ Education

Sabum Jung holds a B.A. (3.9/4.5) and an M.S. (4.2/4.5) in Industrial Engineering from Sung Kyun Kwan University, South Korea. His academic journey focused on advanced analytics, AI-driven optimization, and industrial process improvements. His research contributions in artificial intelligence, reliability engineering, and digital transformation have shaped his expertise in machine learning, deep learning, and predictive modeling, positioning him as a leader in AI applications for manufacturing and industrial systems.

πŸ’Ό Experience

Currently a Data Scientist at LG Energy Solution, Sabum Jung leads AI-driven innovations in virtual metrology, predictive maintenance, and defect analysis. Previously at SK Holdings, he optimized renewable energy predictions, semiconductor material discovery, and AI-powered industrial operations. His 20-year tenure at LG Production Engineering Research Institute saw groundbreaking work in machine learning for smart appliances, battery systems, and industrial automation. His early career as a Military Intelligence Analyst in the U.S. Army honed his analytical prowess, setting the foundation for his AI-driven problem-solving approach.

πŸ† Awards & Honors

Sabum Jung has been recognized for his contributions to AI, machine learning, and industrial automation. His accolades include leadership in AI-driven manufacturing optimization, predictive maintenance, and reinforcement learning applications. He has received industry recognition for his research and innovation in deep learning, active learning, and process optimization in high-tech sectors, further cementing his influence in AI-driven industrial advancements.

πŸ”¬ Research Focus:

Sabum Jung specializes in AI applications for high-tech manufacturing, focusing on predictive maintenance, virtual metrology, and defect detection. His research spans deep learning, reinforcement learning, and AI-driven industrial process optimization. Notable contributions include renewable energy prediction, semiconductor material discovery, and advanced statistical modeling. His expertise in machine learning has been instrumental in developing AI solutions for smart manufacturing, Industry 4.0, and digital transformation.

Publications

Recent progress of LG PDP: High efficiency & productivity technologies Citations1

Conclusion

Sabum Jung is a strong candidate for the Best Researcher Award, given his vast industry experience, research excellence, and technological contributions to AI and machine learning in manufacturing. Enhancing academic collaborations and increasing research dissemination could further elevate his impact and recognition.