Topology optimization for manufacturability of Additive Manufacturing based on Deep Learning and Generative Adversarial Network, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
In recent years, Additive Manufacturing has been extensively used by various industries. These manufacturing processes produce components in a layer-by-layer manner; therefore, they do not impose any geometric constrains to engineers and provide designers with the freedom to design components. Nowadays, one of the primary goals of all industries is to utilize as few raw materials as possible; this way they can deal with the shortage of raw materials and improve their efficiency. Consequently, they implement topology optimization algorithms to design and produce their components. However, topology optimization algorithms result in complicated geometries that can only be fabricated by AM....
Cataloging briefTopology optimization for manufacturability of Additive Manufacturing based on Deep Learning and Generative Adversarial Network, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
In recent years, Additive Manufacturing has been extensively used by various industries. These manufacturing processes produce components in a layer-by-layer manner; therefore, they do not impose any geometric constrains to engineers and provide designers with the freedom to design components. Nowadays, one of the primary goals of all industries is to utilize as few raw materials as possible; this way they can deal with the shortage of raw materials and improve their efficiency. Consequently, they implement topology optimization algorithms to design and produce their components. However, topology optimization algorithms result in complicated geometries that can only be fabricated by AM....
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