LEADER 04469nam 22007215 450 001 996499867603316 005 20240222145403.0 010 $a981-19-6278-2 024 7 $a10.1007/978-981-19-6278-3 035 $a(MiAaPQ)EBC7150323 035 $a(Au-PeEL)EBL7150323 035 $a(CKB)25504286400041 035 $a(DE-He213)978-981-19-6278-3 035 $a(PPN)26635288X 035 $a(EXLCZ)9925504286400041 100 $a20221129d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Applied to Composite Materials$b[electronic resource] /$fedited by Vinod Kushvaha, M. R. Sanjay, Priyanka Madhushri, Suchart Siengchin 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (202 pages) 225 1 $aComposites Science and Technology,$x2662-1827 311 08$aPrint version: Kushvaha, Vinod Machine Learning Applied to Composite Materials Singapore : Springer,c2023 9789811962776 320 $aIncludes bibliographical references and index. 327 $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 fiber?s mechanical properties and fiber matrix adhesion strength to design biocomposite -- Comparison of various machine learning algorithms to predict material behavior in GFRP. 330 $aThis book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of material composite modelling and design. 410 0$aComposites Science and Technology,$x2662-1827 606 $aComposite materials 606 $aMachine learning 606 $aComputational intelligence 606 $aMaterials science$xData processing 606 $aComposites 606 $aMachine Learning 606 $aComputational Intelligence 606 $aComputational Materials Science 606 $aMaterials compostos$2thub 606 $aSimulaciķ per ordinador$2thub 606 $aAprenentatge automātic$2thub 608 $aLlibres electrōnics$2thub 615 0$aComposite materials. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aMaterials science$xData processing. 615 14$aComposites. 615 24$aMachine Learning. 615 24$aComputational Intelligence. 615 24$aComputational Materials Science. 615 7$aMaterials compostos 615 7$aSimulaciķ per ordinador 615 7$aAprenentatge automātic 676 $a006.31 702 $aKushvaha$b Vinod 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996499867603316 996 $aMachine learning applied to composite materials$93088803 997 $aUNISA