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Machine Learning and Artificial Intelligence in Tribology

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Description

Machine learning and artificial intelligence present an exciting opportunity for tribology to tackle long-standing challenges in the field. This webinar will begin with example tribological problems that can be solved with the help of machine learning. We will then delve into some of the different categories of machine-learning algorithms and the types of problems that they were designed to address. Finally, a fundamental requirement for developing any machine-learning model is the access to high-quality data. Thus, we will explore some database concepts that are particularly well suited for a diverse field like tribology. Finally, we will revisit the example tribological problems from the beginning, and show how specific cases of machine learning methods can be used to solve them.

Contributors

  • Nick Garabedian, PhD

    Nick Garabedian, PhD, is currently leading a research group at the Karlsruhe Institute of Technology (KIT), Germany, called "Linked Tribological Data". His research interests lie at the intersection of linked data engineering, materials science, machine learning, experimental nanotribology, research data management, FAIR data, and Open Science & Reproducibility. His diverse team actively integrates knowledge from multiple scientific avenues in order to develop the most applicable, reliable and sustainable solutions for FAIR data collection within tribology and materials science. Nick’s developments in data science have been noticed beyond tribology and he is regularly an invited speaker at conferences and workshops.

  • Max Marian

    Max Marian is an Assistant Professor for Multiscale Engineering Mechanics at the Department of Mechanical and Metallurgical Engineering of Pontificia Universidad Católica de Chile. His research focuses on energy efficiency and sustainability through tribology, with an emphasis on the modification of surfaces through micro-texturing and coatings (diamond-like carbon and 2D materials). Besides machine elements and engine components, he expanded his fields towards biotribology and artificial joints as well as triboelectric nanogenerators. His research is particularly related to the development of numerical multiscale tribo-simulation and machine learning approaches. He has published more than 45 peer-reviewed publications in reputed journals, gave numerous conferences and invited talks and has been awarded with various individual distinctions as well as best paper and presentation awards. Furthermore, he was listed among the Emerging Leaders 2023 of Surface Topography: Metrology and Properties. Moreover, he is in the Editorial Boards from Frontiers in Chemistry Nanoscience, Industrial Lubrication and Tribology, Lubricants as well as Tribology - Materials, Surfaces & Interfaces and is a member of the Society of Tribologists and Lubrication Engineers (STLE) and the German Society for Tribology (GfT).

  • Prathima Nalam, PhD

    Prathima Nalam, PhD, is an assistant professor in the Department of Materials Design and Innovation at the University at Buffalo, State University of New York. Nalam’s research focuses on advancing alternative materials that will leave a low-carbon footprint on the environment. Her research interests lie in developing next-generation materials in the fields of tribology, filtration, and bioengineering. To achieve this, she employs advanced materials characterization methods, novel functionalization techniques and couples with machine learning methods to establish structure-property relationships. She is a member of the Society of Tribologists and Lubrication Engineers (STLE) and the Board of Early Career Researchers Tribology Letter, Springer. Dr. Nalam is the recipient of the Swiss National Science Foundation Fellowship and the STLE Early Career Award. In 2023, she received a National Science Foundation CAREER Award in support of her work to understand the behavior of liquids in two-dimensional confined spaces.

January 17, 2024
Wed 10:00 AM CST

Duration 1H 0M

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