English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the most promising developments in computer vision in recent years isthe use of generative neural networks for functionality condition-based 3Ddesign reconstruction and generation. Here, neural networks learn dependenciesbetween functionalities and a geometry in a very effective way. For a neuralnetwork the functionalities are translated in conditions to a certain geometry.But the more conditions the design generation needs to reflect, the moredifficult it is to learn clear dependencies. This leads to a multi criteriadesign problem due various conditions, which are not considered in the neuralnetwork structure so far.  In this paper, we address this multi-criteria challenge for a 3D design usecase related to an unmanned aerial vehicle (UAV) motor mount. We generate10,000 abstract 3D designs and subject them all to simulations for threephysical disciplines: mechanics, thermodynamics, and aerodynamics. Then, wetrain a Conditional Variational Autoencoder (CVAE) using the geometry andcorresponding multicriteria functional constraints as input. We use our trainedCVAE as well as the Marching cubes algorithm to generate meshes for simulationbased evaluation. The results are then evaluated with the generated UAVdesigns. Subsequently, we demonstrate the ability to generate optimized designsunder self-defined functionality conditions using the trained neural network.

A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle