Machine Learning Applied to Composite Materials (eBook)
VI, 198 Seiten
Springer Nature Singapore (Verlag)
978-981-19-6278-3 (ISBN)
This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of material composite modelling and design.
Dr. Sanjay Mavinkere Rangappa, is currently working as a Senior Research Scientist/Associate Professor and also 'Advisor within the office of the President for University Promotion and Development towards International goals' at King Mongkut's University of Technology North Bangkok, Bangkok, Thailand. He is a Life Member of Indian Society for Technical Education (ISTE) and an Associate Member of Institute of Engineers (India). Also acting as a Board Member of various international journals in the fields of materials science and composites. He is a reviewer for more than 120 international Journals, also a reviewer for book proposals, and international conferences. In addition, he has published more than 200 articles in high-quality international peer-reviewed journals indexed by SCI/Scopus, 11 editorial corners, 60 book chapters, one book, 25 books as an Editor (Published by lead publishers such as Elsevier, Springer, Taylor & Francis, Wiley), and also presented research papers at national/international conferences. He is a lead editor of Several special issues. In addition, 1 Thailand Patent and 2 Indian patents are granted. He has delivered keynote and invited talks at various international conferences and workshops. He has received a 'Top Peer Reviewer 2019' award, Global Peer Review Awards, Powered by Publons, Web of Science Group. The KMUTNB selected him for the 'Outstanding Young Researcher' Award 2020 and 'Outstanding Researcher' Award 2021. He is recognized by Stanford University's list of the world's Top 2% of the Most-Cited Scientists in Single Year Citation Impact 2019 and also for the year 2020.
Dr. Priyanka Madhushri is the Internet of Things (IoT) Ideation Research Engineer at Stanley Black and Decker (SBD), Atlanta. Dr. Madhushri earned her Ph.D. in Electrical Engineering from the University of Alabama in Huntsville, USA. She works with the innovation team and brings new ideas to various projects. As a researcher, she provides Proof of Concept (POC) to various SBD teams and assists in developing the company's software, hardware, and data analytics. Her research interests include predictive analyses using Machine Learning, material modeling, Internet of Things (IoT), mobile computing, etc. She has published in various engineering fields, including materials journals, where her work focused on utilizing machine learning algorithms to predict and explain the mechanical behavior of advanced engineering materials.
This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of materialcomposite modelling and design.
| Erscheint lt. Verlag | 29.11.2022 |
|---|---|
| Reihe/Serie | Composites Science and Technology | Composites Science and Technology |
| Zusatzinfo | VI, 198 p. 71 illus., 61 illus. in color. |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik ► Bauwesen | |
| Technik ► Maschinenbau | |
| Schlagworte | Artificial Neural Network • Carbon Fiber-Reinforced Laminates • Composite Material Design • Fracture Toughness Prediction • Glass Fiber Reinforced Polymer (GFRP) • Machine Learning (ML) • Materials Modelling • Natural Fiber Biocomposite • Particulate Polymer Composite • Silica-Filled Polymer Composite |
| ISBN-10 | 981-19-6278-2 / 9811962782 |
| ISBN-13 | 978-981-19-6278-3 / 9789811962783 |
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