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dc.contributor.author Herath, S
dc.contributor.author Haputhanthri, U
dc.date.accessioned 2020-12-18T08:32:50Z
dc.date.available 2020-12-18T08:32:50Z
dc.date.issued 2020
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/16157
dc.description.abstract Abstract—Topology optimization is the tool of choice in obtaining the initial design of structural components. The resulting optimal design from topology optimization will be the input for subsequent structural optimizations with regard to shape, size, and layout. In reality, however, iterative solvers used in conventional SIMP (Simplified Isotropic Material with Penalization) based topology optimization schemes consume a very high computational power and therefore act as a bottleneck in the manufacturing process. In this work, an accelerated topology optimization technique based on deep learning is presented. Conditional Generative Adversarial Network (cGAN) architecture is used to predict the optimal topology of a given structure subject to a set of input parameters. Next, stress contours are mapped onto the optimal structure to give accurate stress distribution over the structural domain. The predicted maximum Von-mises stress can directly be compared to the yield strength of the material for failure analysis. This technique is proven to arrive at the optimal design of a structure within a negligible amount of time. Also, this method is capable of predicting the stresses in the optimal design and hence plays a decisive role in the integrity of the optimal structure. en_US
dc.language.iso en en_US
dc.subject Conditional Generative Adversarial Networks, Topology Optimization, Stress Prediction, Von-mises stress en_US
dc.title Optimal design and failure prediction using neural networks en_US
dc.type Conference-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Civil Engineering en_US
dc.identifier.year 2020 en_US
dc.identifier.conference Young Members' Technical Conference 2020 en_US
dc.identifier.place BMICH, Colombo en_US
dc.identifier.email sthh2@cam.ac.uk en_US
dc.identifier.email 170208k@uom.lk en_US


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