Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-15638
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Main Title: Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals
Author(s): Marko, Angelina
Bähring, Stefan
Raute, Julius
Biegler, Max
Rethmeier, Michael
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/16860
http://dx.doi.org/10.14279/depositonce-15638
License: https://creativecommons.org/licenses/by/4.0/
Abstract: The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
Subject(s): DED
artificial neural network
data preparation
quality assurance
process monitoring
Issue Date: 14-Apr-2022
Date Available: 10-May-2022
Language Code: en
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Journal Title: Applied Sciences
Publisher: MDPI
Volume: 12
Issue: 8
Article Number: 3955
Publisher DOI: 10.3390/app12083955
EISSN: 2076-3417
TU Affiliation(s): Fak. 5 Verkehrs- und Maschinensysteme » Inst. Werkzeugmaschinen und Fabrikbetrieb » FG Fügetechnik
Appears in Collections:Technische Universität Berlin » Publications

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