2023 IDETC Hackathon
Automating Material Selection for Product Design

Welcome to the Autodesk problem statement for the 2023 IDETC hackathon!

In this challenge, your task is to come up with an open-ended solution to identify an appropriate material for a set of parts, based on their CAD geometries and assembly context.

Material Category	Definition	Example(s)
Metal_Aluminum	Aluminum-based metal	Aluminum alloy
Metal_Ferrous	Ferrous metal (excluding carbon steel)	Cast iron
Metal_Ferrous_Steel	Carbon steel	Stainless steel
Metal_Non-Ferrous	Non-Ferrous metal	Platinum, silver
Other	Uncategorized material	Glass, fabric, ceramic
Plastic	Plastic	Thermoplastic
Wood	Natural and engineered wood	Softwood

Problem Statement

• The hackathon's goal is to predict the material_category for each body in the test set. The train data contains material category labels for each body of each assembly in the assembly.json files.
• Link to PDF with more information.

Dataset

• Download instructions. Please note that the dataset has been modified for this hackathon, and you should only use data from these download instructions in your implementation.
  • The data has been modified to include in each assembly.json file a material_category label for each body. This label, which describes the material category of the body as defined in the table above, can be used to train your model.
• Dataset Specifications: Please refer to the original documentation of the Fusion 360 Gallery Dataset for information about the structure of the data in assembly.json and to find out more about the features in the dataset.

Baseline

• A GPT-based baseline can be found here.
• This can serve as an example of how to extract useful features from the data, and how to evaluate the method.

Submission

• Submission instructions.