Rodrigo Santiago Coelho| Manufacturing processes | Best Researcher Award

Prof. Rodrigo Santiago Coelho| Manufacturing processes | Best Researcher Award

Associate Professor,SENAI CIMATEC University, Brazil

🔬 Short Biography 🌿💊📚

Prof. Rodrigo Santiago Coelho is an Associate Professor at SENAI CIMATEC University, Brazil, specializing in advanced manufacturing processes. With a strong academic and industrial background, he has made significant contributions to the fields of additive manufacturing, materials engineering, and industrial automation. Prof. Coelho’s research focuses on optimizing manufacturing techniques to enhance efficiency, precision, and sustainability in production systems

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🎓 Education:

Dr.-Ing. Rodrigo Santiago Coelho has a strong international academic background in materials engineering and manufacturing. He completed his undergraduate studies at the Pontifical Catholic University of Minas Gerais (PUC Minas) in Brazil from 1999 to 2003, where he developed a solid foundation in mechanical and materials engineering. Motivated by a passion for research and innovation, he pursued his doctorate in Germany at the Ruhr-Universität Bochum (RUB) from 2004 to 2008. His Ph.D. research focused on advanced joining techniques of lightweight materials, particularly friction stir welding and laser beam welding. Further strengthening his scientific training, Dr. Coelho undertook a postdoctoral fellowship at Helmholtz-Zentrum Berlin für Materialien und Energie GmbH from 2008 to 2010, working on energy-related materials and in-situ characterization techniques. His academic journey reflects a deep commitment to cutting-edge research and a global perspective on materials science.

💼 Professional Experience:

Dr. Coelho currently serves as the Chief Researcher at the SENAI Institute of Innovation for Materials Conformation and Joining (ISI – F&J) at SENAI CIMATEC, Salvador, Brazil, a role he has held since 2018. Previously, he was the Director of the same institute from 2014 to 2018, overseeing numerous industrial R&D initiatives. He has also held the role of Materials Technology Area Manager at SENAI CIMATEC. Since May 2014, Dr. Coelho has contributed to higher education as an Associate Professor at SENAI CIMATEC University. Internationally, he worked as an Industrial Liaison Officer at Helmholtz-Zentrum Berlin (2012–2014) and continues to serve as a Guest Professor at Technische Universität Berlin. His diverse experience across academia, research institutes, and industry collaborations highlights his leadership in innovation and advanced manufacturing.

🧪 Research Focus:

Dr. Coelho’s research is at the intersection of advanced manufacturing processes, materials science, and sustainability. His primary focus areas include additive manufacturing (AM) of metals and polymers, welding and joining of dissimilar materials, and metal forming processes. He has extensively investigated laser powder bed fusion (LPBF) and directed energy deposition (DED) using both experimental and numerical techniques. His recent work delves into porosity prediction in AM via machine learning, residual stress modeling, and real-time monitoring of manufacturing processes. Dr. Coelho also applies synchrotron radiation and electron backscatter diffraction (EBSD) for in-situ studies of microstructure, phase transformations, and residual stresses. His research aligns with industrial needs in the aerospace, offshore, and energy sectors, promoting innovation in sustainable manufacturing.

🛠️ Skills and Technical Expertise:

Dr. Coelho is highly skilled in a range of advanced manufacturing techniques, particularly friction stir welding, laser welding, adhesive bonding, and mechanical fastening. His proficiency extends to additive manufacturing technologies like EB-PBF and LPBF, as well as non-destructive testing (NDT) and numerical simulation of thermal and mechanical behaviors. He has expertise in synchrotron X-ray diffraction, XRD-based phase and residual stress analysis, and texture studies using EBSD. Dr. Coelho also employs machine learning and in-situ monitoring systems to enhance the performance and reliability of manufacturing processes. His interdisciplinary knowledge allows him to lead large-scale collaborative projects with direct industrial applications, especially in developing intelligent systems and robotic platforms for production and inspection.

🏆 Awards, Honors, and Achievements:

Dr. Coelho’s career is marked by significant academic and industrial recognition. He has published 63 peer-reviewed journal articles, authored 1 book, and contributed to 6 book chapters. He has successfully supervised 20 master’s theses, 1 doctoral dissertation, and is currently guiding multiple graduate and postdoctoral researchers. His work has garnered international attention, with over 1,700 citations on Google Scholar, and notable citation counts on Scopus (1,300) and Web of Science (879). Dr. Coelho is also a named inventor in three patents, covering innovations in die-cast alloys, robotic underwater inspection, and gradient mechanical property stamping. He has led and participated in multiple funded projects by CNPq, CAPES, ANP Levy, and EMBRAPII, strengthening Brazil’s technological development in manufacturing.

  • 1. Investigation of Thermal Parameters in Numerical Modeling of the Laser Powder Bed Fusion Process for Maraging 300 Steel

    • Authors: B.C. Dos Santos Silva, L. de Figueiredo Soares, L.F. Seixas, R.S. Coelho, G.F. Batalha

    • Journal: Materials Science and Engineering B

    • Year: 2025

    • Citations: 0

    2. Investigation of Distortion, Porosity and Residual Stresses in Internal Channels Fabricated in Maraging 300 Steel by Laser Powder Bed Fusion

    • Authors: B.C. Dos Santos Silva, B. Callegari, L.F. Seixas, R.S. Coelho, G.F. Batalha

    • Journal: Materials

    • Year: 2025

    • Citations: 0

    3. Microstructural and Electrochemical Analysis of the Physically Simulated Heat-Affected Zone of Super-Duplex Stainless Steel UNS S32750

    • Authors: F.M. Dos Santos, L.O.P. da Silva, Y.T.B. Dos Santos, T.N. Lima, R.S. Coelho

    • Journal: Metals

    • Year: 2025

    • Citations: 0

    4. A Review on Sheet Metal Forming Behavior in High-Strength Steels and the Use of Numerical Simulations

    • Authors: L.F. Folle, T.N. Lima, M.P.S. Santos, L.G.S. Zamorano, R.S. Coelho

    • Journal: (Journal not specified)

    • Type: Review

    • Year: 2025

    • Citations: 0

    5. Additive Manufacturing of Tungsten Carbide (WC)-Based Cemented Carbides and Niobium Carbide (NbC)-Based Cermets with High Binder Content via Laser Powder Bed Fusion

    • Authors: F. Miranda, M.O. Dos Santos, R. Condotta, L.G. Martinez, G.F. Batalha

    • Journal: Metals

    • Year: 2024

    • Citations: 0

    6. Influence of Phase Transformation Coefficient on Thermomechanical Modeling of Laser Powder Bed Fusion for Maraging 300 Steel

    • Authors: B.C. Dos Santos Silva, L.D.F. Soares, R.S. Coelho, M. Król, G.F. Batalha

    • Journal: Journal of Materials Research and Technology

    • Year: 2024

    • Citations: 2

🏁conclusion:

Highly Suitable for the Best Researcher Award.
Dr. Coelho not only advances the frontier of materials science but also bridges academic and industrial needs through practical innovations and high-impact research. With his continued trajectory, he is poised to become a global leader in advanced manufacturing technologies.

Zisheng Wang | Industrial Big Data | Best Researcher Award

 Dr. Zisheng Wang | Industrial Big Data | Best Researcher Award

 Dr, Zisheng Wang,Tsinghua University, China

Dr. Zisheng Wang is affiliated with Tsinghua University in China. His research interests include mechanical engineering, robotics, and automation. Dr. Wang has contributed significantly to the field through his research and publications, focusing on topics such as optimization, robotic design, and advanced manufacturing techniques. He is actively involved in academic activities and has a strong background in both theoretical research and practical applications in engineering.

Professional Profiles:

Scopus

Education :

Doctorate (Ph.D. in Engineering)School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (2018-2023),Bachelor’s Degree in Engineering,School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China (2014-2018)

 Experience:

  • Research Assistant,Department of Industrial Engineering, Tsinghua University, Beijing, China (December 2023 – Present)

Skills:

  • Condition monitoring and fault detection
  • Signal processing
  • Deep reinforcement learning
  • Intelligent maintenance of complex equipment
  • Compound fault recognition
  • Time-frequency transform technology
  • Fault diagnosis of robot arm

Awards:

  • Shuimu Tsinghua Scholar Project, Tsinghua University, Beijing, China, Grant: 2023SM233 (2024-2025)
  • Postdoctoral Fellowship Program of CPSF, China Postdoctoral Science Foundation, Grant: GZC20240820 (2024-2025)

Research Focus:

  • Autonomous recognition frameworks for compound faults in mechanical equipment
  • Deep reinforcement learning applications in fault recognition and maintenance
  • Multi-label fault recognition using machine learning algorithms
  • Transfer learning methods for fault recognition in different conditions

Publications :

  • An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment. Mechanical Systems and Signal Processing, 2024.
  • Multi-source information fusion deep self-attention reinforcement learning framework for multi-label compound fault recognition. Mechanism and Machine Theory, 2023.
  • Multi-label fault recognition framework using deep reinforcement learning and curriculum learning mechanism. Advanced Engineering Informatics, 2022.
  • A novel semi-supervised generative adversarial network based on the actor-critic algorithm for compound fault recognition. Neural Computing and Applications, 2022.
  • Alternative multi-label imitation learning framework monitoring tool wear and bearing fault under different working conditions. Advanced Engineering Informatics, 2022.