A New Projection Based Method for the Classification of Mechanical Components Using Convolutional Neural Networks

DS 116: Proceedings of the DESIGN2022 17th International Design Conference

Year: 2022
Editor: Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović
Author: Sebastian Bickel, Benjamin Schleich, Sandro Wartzack
Series: DESIGN
Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Section: Artificial Intelligence and Data-Driven Design
Page(s): 1501-1510
DOI number: https://doi.org/10.1017/pds.2022.152
ISSN: 2732-527X (Online)

Abstract

Digital engineering is increasingly established in the industrial routine. Especially the application of machine learning on geometry data is a growing research issue. Driven by this, the paper presents a new method for the classification of mechanical components, which utilizes the projection of points onto a spherical detector surfaces to transfer the geometries into matrices. These matrices are then classified using deep learning networks. Different types of projection are examined, as are several deep learning models. Finally, a benchmark dataset is used to demonstrate the competitiveness.

Keywords: data-driven design, artificial intelligence (AI), data mining, deep learning, part classification