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Investigation on the scattering characteristics and unsupervised clustering of 3D printed samples

Published online by Cambridge University Press:  24 June 2020

Mostafa Elsaadouny Open the ORCID record for Mostafa Elsaadouny [Opens in a new window] ,
Jan Barowski ,
Jochen Jebramcik  and
Ilona Rolfes
Mostafa Elsaadouny*
Affiliation:
Institute of Microwave Systems, Ruhr University Bochum, Germany
Jan Barowski
Affiliation:
Institute of Microwave Systems, Ruhr University Bochum, Germany
Jochen Jebramcik
Affiliation:
Institute of Microwave Systems, Ruhr University Bochum, Germany
Ilona Rolfes
Affiliation:
Institute of Microwave Systems, Ruhr University Bochum, Germany
*
Author for correspondence: Mostafa Elsaadouny, E-mail: mostafa.elsaadouny@rub.de
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Abstract

In this work, the scattering characteristics of 3D-printed samples are being investigated by using a single-polarized and a cross-polarized radar system. The 3D-printed technology participates in a wide range of applications nowadays. The idea of synthetic aperture radar (SAR) has been utilized to investigate the reflected electromagnetic energy from the 3D-printed samples by setting each of the radar systems in a fixed position and the mounting sample on an x-y positioning table which has been used to achieve rectangular-scan mode for SAR. The data have been ported and processed by the matched filter approach. For better image interpretation, the data have been further processed by the median filter in order to reduce noise level while preserving the main image details. Afterwards, the data have been further investigated for determining and classifying any possible defects. This process has been accomplished by deploying the unsupervised learning concept to cluster the SAR responses into two groups, compromising the defected positions responses and the non-defected responses. The obtained results of both radar sensors have been compared and evaluated using different quality assessment factors. Moreover, unsupervised learning techniques have been investigated and the obtained results show a high degree of efficiency in clustering the SAR responses.

Keywords

RadarSAR imagingimage processing3D printingmachine learningunsupervised clustering
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 12 , Special Issue 9: EuMCE 2019 Special Issue (Part I) , November 2020 , pp. 862 - 869
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Copyright
Copyright © Cambridge University Press and the European Microwave Association 2020

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