001451531 000__ 03929cam\a2200529\a\4500
001451531 001__ 1451531
001451531 003__ OCoLC
001451531 005__ 20230310004704.0
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001451531 007__ cr\un\nnnunnun
001451531 008__ 221201s2022\\\\si\\\\\\o\\\\\000\0\eng\d
001451531 019__ $$a1352975112$$a1354206593$$a1354570084
001451531 020__ $$a9789811962783$$q(electronic bk.)
001451531 020__ $$a9811962782$$q(electronic bk.)
001451531 020__ $$z9811962774
001451531 020__ $$z9789811962776
001451531 0247_ $$a10.1007/978-981-19-6278-3$$2doi
001451531 035__ $$aSP(OCoLC)1352413369
001451531 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dAU@$$dEBLCP$$dOCLCF$$dUKAHL$$dOCLCQ$$dN$T
001451531 049__ $$aISEA
001451531 050_4 $$aTA418.9.C6
001451531 08204 $$a620.1180285631$$223/eng/20221215
001451531 24500 $$aMachine learning applied to composite materials /$$cVinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin, editors.
001451531 260__ $$aSingapore :$$bSpringer,$$c2022.
001451531 300__ $$a1 online resource.
001451531 338__ $$aonline resource$$bcr$$2rdacarrier
001451531 4901_ $$aComposites science and technology
001451531 5050_ $$aImportance of machine learning in material science -- Machine Learning: A methodology to explain and predict material behavior -- Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network -- Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites -- Forward machine learning technique to predict dynamic fracture behavior of particulate composite -- Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates -- Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates -- Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning -- Effect of natural fibers mechanical properties and fiber matrix adhesion strength to design biocomposite -- Comparison of various machine learning algorithms to predict material behavior in GFRP.
001451531 506__ $$aAccess limited to authorized users.
001451531 520__ $$aThis book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of material composite modelling and design.
001451531 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 15, 2022).
001451531 650_0 $$aComposite materials$$xDesign.
001451531 650_0 $$aComposite materials$$xComputer simulation.
001451531 650_0 $$aMachine learning.
001451531 655_0 $$aElectronic books.
001451531 7001_ $$aKushvaha, Vinod,$$eeditor.
001451531 7001_ $$aSanjay, M. R.$$eeditor.
001451531 7001_ $$aMadhushri, Priyanka,$$eeditor.
001451531 7001_ $$aSiengchin, Suchart,$$eeditor.
001451531 77608 $$iPrint version:$$z9811962774$$z9789811962776$$w(OCoLC)1337855328
001451531 830_0 $$aComposites science and technology (Springer (Firm))
001451531 852__ $$bebk
001451531 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-6278-3$$zOnline Access$$91397441.1
001451531 909CO $$ooai:library.usi.edu:1451531$$pGLOBAL_SET
001451531 980__ $$aBIB
001451531 980__ $$aEBOOK
001451531 982__ $$aEbook
001451531 983__ $$aOnline
001451531 994__ $$a92$$bISE