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- W4383648052 abstract "This paper improves on the performance of the Deep Learning Additive Manufacturing driven Topology Optimization (DL-AM-TO) approach that was proposed in [4]. DL-AM-TO is a data-driven generative method that integrates the mechanical and geometrical constraints concurrently at the same conceptual level and generates a 2D design accordingly. Furthermore, DL-AM-TO tailors the design's geometry to comply with manufacturing criteria, which facilitates the designer's interpretation phase and prevents him/her from getting stuck in a loop of drawing the CAD and testing its performance. The geometry needs less support structure and hence is printed faster. Consequently, DL-AM-TO accelerates the Design for AM process." @default.
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- W4383648052 date "2023-01-01" @default.
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- W4383648052 title "A data-driven topology optimization approach to handle geometrical manufacturing constraints in the earlier steps of the design phase" @default.
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- W4383648052 doi "https://doi.org/10.1016/j.procir.2023.02.143" @default.
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