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  4. Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals
 
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April 14, 2022
Journal Article
Title

Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals

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.
Author(s)
Marko, Angelina
Bähring, Stefan
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Raute, Maximilian Julius
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Biegler, Max  
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Rethmeier, Michael  
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Journal
Applied Sciences  
Project(s)
Certify as you build - Qualitätssicherung beim Laser-Pulver-Auftragschweißen
Funder
Bundesministerium für Wirtschaft und Energie BMWi (Deutschland)
Open Access
DOI
10.3390/app12083955
File(s)
applsci-12-03955.pdf (1.41 MB)
Rights
CC BY
Language
English
Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK  
Keyword(s)
  • artificial neural network

  • data preparation

  • quality assurance

  • process monitoring

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