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APPLICATION OF UNSUPERVISED LEARNING AND IMAGE PROCESSING INTO CLASSIFICATION OF DESIGNS TO BE FABRICATED WITH ADDITIVE OR TRADITIONAL MANUFACTURING

Published online by Cambridge University Press:  19 June 2023

Baris Ördek  and
Yuri Borgianni
Baris Ördek*
Affiliation:
Free University of Bozen-Bolzano
Yuri Borgianni
Affiliation:
Free University of Bozen-Bolzano
*
Ördek, Baris, Free University of Bozen-Bolzano, Italy, boerdek@unibz.it

Abstract

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Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications.

Keywords

Computer Aided Design (CAD)Machine learningAdditive ManufacturingImage ProcessingUnsupervised Learning
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 613 - 622
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), 2023. Published by Cambridge University Press

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