Publications

Non-Destructive Testing of 3D-printed Samples based on Machine Learning

Elsaadouny, M. and Barowski, J. and Rolfes, I.

IMWS-AMP 2019 - 2019 IEEE MTT-S INTERNATIONAL MICROWAVE WORKSHOP SERIES ON ADVANCED MATERIALS AND PROCESSES FOR RF AND THZ APPLICATIONS
Volume: Pages: 22-24
DOI: 10.1109/IMWS-AMP.2019.8880141
Published: 2019

Abstract
The three dimensional printing is a very important technology that participates in many applications. In this paper, we present an approach for the Non-Destructive Testing (NDT) of the three dimensional printed objects. This methodology solves the image classification problem by using the Neural Networks as they are capable of making good decisions and classifying images by proper training. The network has been trained by a large number of images of the tested sample layers. The proposed solution has been used for testing different sets of actual data for monitoring the performance under different scenarios, and the obtained results show a high degree of accuracy regarding image classification and defect detection. © 2019 IEEE.

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