Artificial Neural Network Approach to Determine Elastic Modulus of Carbon Fiber-Reinforced Laminates
Recognized as a prominent material for engineering applications, carbon fiber-reinforced laminated composites require significant effort to characterize due to their anisotropic structure and viscoelastic nature. Dynamic mechanical analysis has been used to accelerate the testing process by transfor...
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| Veröffentlicht in: | JOM (1989) 2019-11, Vol.71 (11), p.4015-4023 |
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| creator | Xu, Xianbo Gupta, Nikhil |
| description | Recognized as a prominent material for engineering applications, carbon fiber-reinforced laminated composites require significant effort to characterize due to their anisotropic structure and viscoelastic nature. Dynamic mechanical analysis has been used to accelerate the testing process by transforming the measured viscoelastic properties to elastic modulus. To expand the transformation to anisotropic materials, artificial neural network approach is used to build the master relationship of the storage modulus in three in-plane directions. Using rotation transformation, the stiffness tensor can be calculated to extrapolate the frequency domain viscoelastic properties in any orientation with respect to the fiber direction. The viscoelastic properties are transformed to time domain relaxation function using the linear relationship of viscoelasticity. Stress response with a certain strain history is predicted and the elastic modulus is extracted. Compared to the experimental flexural test results, the artificial neural network-based method achieved an error of less than 7.3%. The results show that the transformation can predict the anisotropic material behavior at a wide range of temperatures and strain rates. |
| doi_str_mv | 10.1007/s11837-019-03666-7 |
| format | Article |
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Dynamic mechanical analysis has been used to accelerate the testing process by transforming the measured viscoelastic properties to elastic modulus. To expand the transformation to anisotropic materials, artificial neural network approach is used to build the master relationship of the storage modulus in three in-plane directions. Using rotation transformation, the stiffness tensor can be calculated to extrapolate the frequency domain viscoelastic properties in any orientation with respect to the fiber direction. The viscoelastic properties are transformed to time domain relaxation function using the linear relationship of viscoelasticity. Stress response with a certain strain history is predicted and the elastic modulus is extracted. Compared to the experimental flexural test results, the artificial neural network-based method achieved an error of less than 7.3%. The results show that the transformation can predict the anisotropic material behavior at a wide range of temperatures and strain rates.</description><identifier>ISSN: 1047-4838</identifier><identifier>EISSN: 1543-1851</identifier><identifier>DOI: 10.1007/s11837-019-03666-7</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial neural networks ; Behavior ; Carbon fiber reinforcement ; Carbon fibers ; Chemistry/Food Science ; Composite materials ; Dynamic mechanical analysis ; Earth Sciences ; Elastic anisotropy ; Elastic properties ; Engineering ; Environment ; Fiber reinforced materials ; Laminar composites ; Laminates ; Mathematical functions ; Modeling and Simulation of Composite Materials ; Modulus of elasticity ; Neural networks ; Optimization ; Physics ; Polymers ; Stiffness ; Storage modulus ; Strain ; Temperature ; Tensors ; Transformations (mathematics) ; Viscoelasticity</subject><ispartof>JOM (1989), 2019-11, Vol.71 (11), p.4015-4023</ispartof><rights>The Minerals, Metals & Materials Society 2019</rights><rights>Copyright Springer Nature B.V. 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Dynamic mechanical analysis has been used to accelerate the testing process by transforming the measured viscoelastic properties to elastic modulus. To expand the transformation to anisotropic materials, artificial neural network approach is used to build the master relationship of the storage modulus in three in-plane directions. Using rotation transformation, the stiffness tensor can be calculated to extrapolate the frequency domain viscoelastic properties in any orientation with respect to the fiber direction. The viscoelastic properties are transformed to time domain relaxation function using the linear relationship of viscoelasticity. Stress response with a certain strain history is predicted and the elastic modulus is extracted. Compared to the experimental flexural test results, the artificial neural network-based method achieved an error of less than 7.3%. 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Gupta, Nikhil</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-3686719fce8620550edae46bc2d9902b3317e0367e5b33e6aa85799dcfc5bf553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Behavior</topic><topic>Carbon fiber reinforcement</topic><topic>Carbon fibers</topic><topic>Chemistry/Food Science</topic><topic>Composite materials</topic><topic>Dynamic mechanical analysis</topic><topic>Earth Sciences</topic><topic>Elastic anisotropy</topic><topic>Elastic properties</topic><topic>Engineering</topic><topic>Environment</topic><topic>Fiber reinforced materials</topic><topic>Laminar composites</topic><topic>Laminates</topic><topic>Mathematical functions</topic><topic>Modeling and Simulation of Composite Materials</topic><topic>Modulus of elasticity</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Physics</topic><topic>Polymers</topic><topic>Stiffness</topic><topic>Storage modulus</topic><topic>Strain</topic><topic>Temperature</topic><topic>Tensors</topic><topic>Transformations (mathematics)</topic><topic>Viscoelasticity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Xianbo</creatorcontrib><creatorcontrib>Gupta, Nikhil</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>University Readers</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Trade & Industry (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Materials Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Trade & Industry</collection><collection>Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>JOM (1989)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Xianbo</au><au>Gupta, Nikhil</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Neural Network Approach to Determine Elastic Modulus of Carbon Fiber-Reinforced Laminates</atitle><jtitle>JOM (1989)</jtitle><stitle>JOM</stitle><date>2019-11-01</date><risdate>2019</risdate><volume>71</volume><issue>11</issue><spage>4015</spage><epage>4023</epage><pages>4015-4023</pages><issn>1047-4838</issn><eissn>1543-1851</eissn><abstract>Recognized as a prominent material for engineering applications, carbon fiber-reinforced laminated composites require significant effort to characterize due to their anisotropic structure and viscoelastic nature. Dynamic mechanical analysis has been used to accelerate the testing process by transforming the measured viscoelastic properties to elastic modulus. To expand the transformation to anisotropic materials, artificial neural network approach is used to build the master relationship of the storage modulus in three in-plane directions. Using rotation transformation, the stiffness tensor can be calculated to extrapolate the frequency domain viscoelastic properties in any orientation with respect to the fiber direction. The viscoelastic properties are transformed to time domain relaxation function using the linear relationship of viscoelasticity. Stress response with a certain strain history is predicted and the elastic modulus is extracted. Compared to the experimental flexural test results, the artificial neural network-based method achieved an error of less than 7.3%. 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| subjects | Artificial neural networks Behavior Carbon fiber reinforcement Carbon fibers Chemistry/Food Science Composite materials Dynamic mechanical analysis Earth Sciences Elastic anisotropy Elastic properties Engineering Environment Fiber reinforced materials Laminar composites Laminates Mathematical functions Modeling and Simulation of Composite Materials Modulus of elasticity Neural networks Optimization Physics Polymers Stiffness Storage modulus Strain Temperature Tensors Transformations (mathematics) Viscoelasticity |
| title | Artificial Neural Network Approach to Determine Elastic Modulus of Carbon Fiber-Reinforced Laminates |
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