Dataset for Classification of Forming Tool Types for Aircraft Parts Based on Neural Network Models Using CAD

Description

The dataset consists of aircraft part images related to the fluid cell hydroforming process. The images were captured using CATIA from CAD data obtained from publicly available datasets and additional CAD data generated based on aircraft design guidelines. Since the features of aircraft parts are not fully revealed in 2D images, impact performance may be negatively impacted. To minimize the loss of information, the images were captured in three view types—normal view, view with hidden lines, and wireframe view—supported by the CATIA program. Additionally, two other measures were implemented: the use of isometric views and capturing each CAD model from various angles and orientations.

Institutions

• Gyeongsang National University

Categories

Funders

• Ministry of Science and ICT

  South Korea

Licence