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

Published online by Cambridge University Press:  26 May 2022

S. Bickel ,
B. Schleich  and
S. Wartzack
S. Bickel*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
B. Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
*
bickel@mfk.fau.de

Abstract

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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 designartificial intelligence (AI)data miningdeep learningpart classification

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1501 - 1510
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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