Chao Zhang | Machine Learning | Best Researcher Award

Prof. Chao Zhang | Machine Learning | Best Researcher Award

Professor at Shanghai University, China

Professor Chao Zhang is a distinguished academic and researcher specializing in mechanical engineering, particularly in tribology and engine component wear. With an extensive career spanning multiple prestigious institutions, including Shanghai University and Northwestern University, he has significantly contributed to the field through research, publications, and technical committee roles. His expertise integrates classical tribology with modern computational techniques such as machine learning and quantum chemical molecular dynamics.

Publication Profile 

Scopus

Educational Background 🎓

  • Bachelor’s Degree: Mechanical Engineering, Shanghai Railway University (1983)

  • Master’s Degree: Mechanical Engineering, Shanghai Internal Combustion Engine Research Institute (1989)

  • Ph.D.: Mechanical Engineering, Shanghai University (1997)

Professional Experience 💼

  • Senior Research Associate (1997–2002): Northwestern University, USA (Worked with Profs. H.S. Cheng and Qian Wang)

  • Professor:

    • Tongji University, China

    • Shanghai University, China

    • Kunming University of Science and Technology, China

  • Technical Committee Member: Engines and Powertrains, International Federation for the Promotion of Mechanism and Machine Science (IFToMM)

Research Interests 🔬

  • Tribology and lubrication in engine components

  • Scuffing behavior and wear modeling of piston components

  • Multi-phase and multi-scale engine wear modeling using quantum chemical molecular dynamics and machine learning

  • Digital twin modeling for tribocorrosion

  • Application of artificial intelligence and big data in engine tribology

  • Mechano-chemical kinetic models for boundary lubrication

Awards and Honors🏆✨

  • Technical committee member of IFToMM (Engines and Powertrains)

  • Contributor to Springer’s Mechanisms and Machine Science Series

  • Numerous high-impact journal publications in Tribology Transactions, ASME Journal of Tribology, Tribology International, and Wear

Conclusion🌟

Professor Chao Zhang is an accomplished mechanical engineering expert with a focus on tribology, engine wear, and computational modeling. His interdisciplinary research integrates classical tribology with advanced computational methods, positioning him as a leading figure in his field. His contributions to academia, industry collaborations, and publications underscore his commitment to advancing mechanical engineering and tribology.

Publications 📚

1️⃣ Zhang, C. (2025). Multi-phase and multi-scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework. 🔬🛠️ Wear, xxx(xxx)xxx. [🔗 DOI: 10.1016/j.wear.2025.205771]


2️⃣ Zhang, C. (2023). Lubricant-Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning. 🔍📊 STLE 2023 Annual Meeting Digital Proceedings.


3️⃣ Zhang, C. (2022). Scuffing behavior of piston-pin bore bearing in mixed lubrication. ⚙️📖 In T. Parikyan (Ed.), Advances in Engine and Powertrain Research and Technology (pp. 65–95). Springer, Mechanisms and Machine Science 114.


4️⃣ Zhang, C. (2022). Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives. 🧪⚛️ Proceedings of I4SDG Workshop 2021, MMS 108, pp. 1–9.


5️⃣ Zhang, C. (2021). Scuffing factor and scuffing failure mapping. 🚗🔥 Proceedings of the 2nd World Congress on Internal Combustion Engine, April 21-24, Jinan, China.


6️⃣ Zhang, C. (2018). Analysis of piston scuffing failure based on big data base and cloud computing. ☁️💾 Proceedings of the 2018 World Internal Combustion Engine Congress and Exhibition, November 8-11, Wuxi, China.


7️⃣ Zhang, C., et al. (2007). Effect of loading path on sliding contact status for elastic and plastic media. 🔩⚙️ Proceedings of the STLE/ASME International Joint Tribology Conference, IJTC2007-44481.


8️⃣ Ye, Z.K., Zhang, C., Wang, Y.C., Cheng, H.S, Tung, S. M., Wang, Q., He, J. (2004). An experimental investigation of piston skirt scuffing: a piston scuffing apparatus, experiments, and scuffing mechanism analyses. 🔍🔬 WEAR, 257, 8-31.


9️⃣ Zhang, C., Wang, Q., Cheng, H. S. (2004). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part II: Scuffing Mechanism and Failure Criteria. 🛠️⚡ STLE, Tribology Transactions, 47, 149-156.


🔟 Zhang, C., Cheng, H. S., Qiu, L., Knipstein, K. W., & Bolyard, J. (2003). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part I: Experimental Studies. 🧑‍🔬📊 STLE, Tribology Transactions, 46, 193-199.


Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

Mr. Rafael Natalio Fontana Crespo | Artificial Intelligence | Young Scientist Award

PhD Student at Politecnico di Torino, Italy

Rafael Natalio Fontana Crespo is a dedicated and sociable Ph.D. student specializing in Computer and Control Engineering at Politecnico di Torino. With a strong academic background in mechatronics and practical experience in electrical energy analysis, he is passionate about tackling complex challenges through innovative solutions. 🌐💡

Publication Profile : 

Orcid

 

🎓 Educational Background :

Rafael is currently pursuing a Ph.D. in Computer and Control Engineering at Politecnico di Torino, Italy, since May 2022. He previously obtained a Master’s Degree in Mechatronic Engineering from the same institution, graduating with 110/110 cum laude in July 2022. His master’s thesis focused on designing and developing a distributed software platform for additive manufacturing. Rafael studied Electromechanical Engineering at the Universidad Nacional de Córdoba, Argentina, where he also completed a double degree program.

💼 Professional Experience :

Rafael gained practical experience during his internship at EPEC (Empresa Provincial de Energía de Córdoba) in Argentina, where he worked in the Statistics and Technical Department from May 2020 to May 2021. He was involved in analyzing thermal images of electrical components to prevent failures, contributing to the overall safety and efficiency of electrical systems.

📚 Research Interests : 

Rafael’s research interests lie at the intersection of computer engineering, control systems, and mechatronics, particularly focusing on additive manufacturing, machine learning applications in energy systems, and the optimization of neural networks.

📝 Publication Top Notes :

      1. Fontana Crespo, R.N., E. Patti, S. Di Cataldo, D. Cannizzaro. (2022). Design and Development of a Distributed Software Platform for Additive Manufacturing. Master’s Thesis, Politecnico di Torino.
      2. Fontana Crespo, R.N. (2023). Machine Learning in Energy Applications. Course Exam Paper, Politecnico di Torino.
      3. Fontana Crespo, R.N. (2023). IoT Platforms for Spatial Analytics in Smart Energy Systems. Course Exam Paper, Politecnico di Torino.
      4. Fontana Crespo, R.N. (2023). Optimized Execution of Neural Networks at the Edge. Course Exam Paper, Politecnico di Torino.
      5. Fontana Crespo, R.N. (2023). Adversarial Training of Neural Networks. Course Exam Paper, Politecnico di Torino.