Material Prediction for Design Automation Using Graph Representation
Learning
- URL: http://arxiv.org/abs/2209.12793v1
- Date: Mon, 26 Sep 2022 15:49:35 GMT
- Title: Material Prediction for Design Automation Using Graph Representation
Learning
- Authors: Shijie Bian, Daniele Grandi, Kaveh Hassani, Elliot Sadler, Bodia
Borijin, Axel Fernandes, Andrew Wang, Thomas Lu, Richard Otis, Nhut Ho,
Bingbing Li
- Abstract summary: We introduce a graph representation learning framework that supports the material prediction of bodies in assemblies.
We formulate the material selection task as a node-level prediction task over the assembly graph representation of CAD models and tackle it using Graph Neural Networks (GNNs)
The proposed framework can scale to large datasets and incorporate designers' knowledge into the learning process.
- Score: 5.181429907321226
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Successful material selection is critical in designing and manufacturing
products for design automation. Designers leverage their knowledge and
experience to create high-quality designs by selecting the most appropriate
materials through performance, manufacturability, and sustainability
evaluation. Intelligent tools can help designers with varying expertise by
providing recommendations learned from prior designs. To enable this, we
introduce a graph representation learning framework that supports the material
prediction of bodies in assemblies. We formulate the material selection task as
a node-level prediction task over the assembly graph representation of CAD
models and tackle it using Graph Neural Networks (GNNs). Evaluations over three
experimental protocols performed on the Fusion 360 Gallery dataset indicate the
feasibility of our approach, achieving a 0.75 top-3 micro-f1 score. The
proposed framework can scale to large datasets and incorporate designers'
knowledge into the learning process. These capabilities allow the framework to
serve as a recommendation system for design automation and a baseline for
future work, narrowing the gap between human designers and intelligent design
agents.
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