A data-driven topology optimization approach to handle geometrical manufacturing constraints in the earlier steps of the design phase-Reference-Cited by-同舟云学术

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A data-driven topology optimization approach to handle geometrical manufacturing constraints in the earlier steps of the design phase

Published:2023 Issue: Volume:119 Page:377-383
ISSN:2212-8271
Container-title:Procedia CIRP
language:en
Short-container-title:Procedia CIRP

Author:

ALMASRI Waad,DANGLADE Florence,BETTEBGHOR Dimitri,ADJED Faouzi,ABABSA Fakhreddine

Funder

ANRT

Publisher

Elsevier BV

Subject

General Medicine

Reference25 articles.

1. Design for additive manufacturing—element transitions and aggregated structures;Adam;CIRP Journal of Manufacturing Science and Technology,2014

2. Deep learning architecture for topological optimized mechanical design generation with complex shape criterion;Almasri,2021

3. Gmcad: an original synthetic dataset of 2d designs along their geometrical and mechanical conditions;Almasri;Procedia Computer Science 200,2022

4. Deep learning for additive manufacturing-driven topology optimization;Almasri,2022

5. Optimal shape design as a material distribution problem;Bendsøe;Structural optimization 1,1989

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AMGPT: A large language model for contextual querying in additive manufacturing;Additive Manufacturing Letters;2024-12

2. Amgpt: A Large Language Model for Contextual Querying in Additive Manufacturing;2024

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