Next Article in Journal
Effects of Time and Temperature on Stability of Bioactive Molecules, Color and Volatile Compounds during Storage of Grape Pomace Flour
Previous Article in Journal
Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

1
Institute of Machine Tools and Factory Management, Technical University Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
2
Fraunhofer Institute of Production Systems and Design Engineering (IPK), Pascalstraße 8-9, 10587 Berlin, Germany
3
Federal Institute of Materials Research and Testing (BAM), Unter den Eichen 87, 12205 Berlin, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(8), 3955; https://doi.org/10.3390/app12083955
Submission received: 21 March 2022 / Revised: 6 April 2022 / Accepted: 12 April 2022 / Published: 14 April 2022
(This article belongs to the Topic Additive Manufacturing)

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.
Keywords: DED; artificial neural network; data preparation; quality assurance; process monitoring DED; artificial neural network; data preparation; quality assurance; process monitoring

Share and Cite

MDPI and ACS Style

Marko, A.; Bähring, S.; Raute, J.; Biegler, M.; Rethmeier, M. Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals. Appl. Sci. 2022, 12, 3955. https://doi.org/10.3390/app12083955

AMA Style

Marko A, Bähring S, Raute J, Biegler M, Rethmeier M. Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals. Applied Sciences. 2022; 12(8):3955. https://doi.org/10.3390/app12083955

Chicago/Turabian Style

Marko, Angelina, Stefan Bähring, Julius Raute, Max Biegler, and Michael Rethmeier. 2022. "Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals" Applied Sciences 12, no. 8: 3955. https://doi.org/10.3390/app12083955

APA Style

Marko, A., Bähring, S., Raute, J., Biegler, M., & Rethmeier, M. (2022). Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals. Applied Sciences, 12(8), 3955. https://doi.org/10.3390/app12083955

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop