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dc.contributor.authorChang, Eldridge Wen Weien_US
dc.identifier.citationChang, E. W. W. (2022). Data-driven modelling of mechanical properties. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractComputational simulation of mechanical structures is an important tool for the design and optimization of structural components and the control of new processes. However, for simulations to be predictive, it is necessary to generate models that can accurately describe the mechanical properties of materials under different states of stress. These constitutive laws are often determined on an ad-hoc basis, by fitting the results of standard mechanical tests to phenomenological models. In this Final Year Project, machine learning tools including Genetic Algorithm are utilized to develop a data-driven model for materials behaviour. We compare these models with conventionally fitted ones derived for analytical formulations. We test our strategy by fitting these models to experimental data from a historical dataset for rubber. The fitting strategy of our model is to take data from one stress state for training and validate our model by comparing its performance in two other stress states.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Materials::Non-metallic materialsen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleData-driven modelling of mechanical propertiesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorUpadrasta Ramamurtyen_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeBachelor of Engineering (Mechanical Engineering)en_US
dc.contributor.organizationA*STAR Institute of High Performance Computingen_US
dc.contributor.supervisor2Mark Hyunpong Jhonen_US
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Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)
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