From sketches to graphs: A deep learning based method for detection and contextualisation of principle sketches in the early phase of product development
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nadège Troussier
Author: Bickel, Sebastian; Goetz, Stefan; Wartzack, Sandro
Series: ICED
Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
Section: Design Methods
Page(s): 1975-1984
DOI number: https://doi.org/10.1017/pds.2023.198
ISBN: -
ISSN: -
Abstract
The digitalization trend is finding its way more and more into product development, resulting in new frameworks to enhance product engineering. An integral element is the application of new techniques to existing data, which offers an enormous potential for time and cost savings, because duplicate work in product design and subsequent steps is avoided. The reduction of costs can be further increased through the application as early as possible in the product development process. One solution is outlined in this publication, where the source of available data is principle sketches from engineering design. These represent the basic solution for technical products in a simplified way and are often deployed in the early stages of the development process. This representation enables not only a search of similar sketches but also other fields of interest such as product optimization or the search of CAD-geometries. To utilize this data in a practical way, a procedure is presented which recognizes the symbols of the sketches and subsequently converts them into graphs. An exemplary dataset from different gearbox layouts is used to present the application opportunities by performing similarity searches with multiple input formats.
Keywords: Early design phases, Machine learning, Artificial intelligence, object detection, deep learning