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DETECTION AND CLASSIFICATION OF SYMBOLS IN PRINCIPLE SKETCHES USING DEEP LEARNING

Published online by Cambridge University Press:  27 July 2021

Sebastian Bickel ,
Benjamin Schleich  and
Sandro Wartzack
Sebastian Bickel*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Benjamin Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
*
Bickel, Sebastian, Friedrich-Alexander-Universität Erlangen-Nürnberg, Engineering Design, Germany, [email protected]

Abstract

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Data-driven methods from the field of Artificial Intelligence or Machine Learning are increasingly applied in mechanical engineering. This refers to the development of digital engineering in recent years, which aims to bring these methods into practice in order to realize cost and time savings. However, a necessary step towards the implementation of such methods is the utilization of existing data. This problem is essential because the mere availability of data does not automatically imply data usability. Therefore, this paper presents a method to automatically recognize symbols from principle sketches, which allows the generation of training data for machine learning algorithms. In this approach, the symbols are created randomly and their illustration varies with each generation. . A deep learning network from the field of computer vision is used to test the generated data set and thus to recognize symbols on principle sketches. This type of drawing is especially interesting because the cost-saving potential is very high due to the application in the early phases of the product development process.

Keywords

Artificial intelligenceMachine learningEarly design phases
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 1183 - 1192
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), 2021. Published by Cambridge University Press

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