Publication | ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2022

Material Prediction For Design Automation Using Graph Representation Learning

Presentation of this project at the 2022 IDETC-CIE Conference (Design Automation Track).

Following the Assembly Graph project, the paper represents CAD data with graphs in order to leverage Graph Neural Networks for a material prediction task of each part in the assemblies of the Fusion Gallery Dataset.

Download publication

Abstract

Material Prediction For Design Automation Using Graph Representation Learning

Shijie Bian, Daniele Grandi, Kaveh Hassani, Bingbing Li

ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2022

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.

As illustrated in Figure 1, the proposed framework consists of three main modules: feature encoding, graph construction, and learning framework.

Associated Researchers

Shijie Bian

University of California

Kaveh Hassani

Autodesk Research

Elliot Sadler

California State University, Northridge

Bodia Borijin

California State University, Northridge

Axel Fernandes

California State University, Northridge

Andrew Wang

Portola High School

Thomas Lu

Jet Propulsion Laboratory

Richard Otis

Jet Propulsion Laboratory

Nhut Ho

California State University, Northridge

Bingbing Li

California State University, Northridge

View all researchers

Related Resources

Publication

2016

The Effect of Visual Appearance on the Performance of Continuous Sliders and Visual Analogue Scales

Sliders and Visual Analogue Scales (VASs) are input mechanisms which…

Publication

2019

Design optimization of dynamic flexible multibody systems using the discrete adjoint variable method

The design space of dynamic multibody systems (MBSs), particularly…

Publication

2015

A Novel Application of Gamification for Collecting High-Level Design Information

This paper presents a novel application of gamification for collecting…

Publication

2003

The Effects of Posture on Forearm Muscle Loading During Gripping

The purpose of this study was to quantify the response of the forearm…

Get in touch

Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.

Contact us