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2018 | Supplement | Chapter

In Situ and Real-Time Monitoring of Powder-Bed AM by Combining Acoustic Emission and Artificial Intelligence

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Abstract

At present, the quality control in additive manufacturing is diligently based on temperature of the process zone or high resolution imaging. Hence, various sensors such as pyrometers, photo diodes and matrix CCD detectors are used. The discrepancies in temperature measurements and the real temperature distribution inside the powder medium reduce the reliability of this method. The high resolution imaging monitors the quality post factum, after a part is manufactured. So far, no methods are known to monitor the quality of additive manufacturing in situ and in real-time. To achieve the goal of accurate real-time quality control, we propose an approach that relies on acoustic emission, which is further analyzed within artificial intelligence framework. We show that the additive manufacturing process has a number of unique acoustic signatures that can be detected, extracted and interpreted in terms of quality.
In this contribution, the processing parameters for the selective laser melting of a 316L steel were modified to create a specimen consisting of sections with three quality levels. During the process, the acoustic emission data were acquired and then processed prior validation. The confidence level achieved in the classification is 79–84% that demonstrates the applicability of this approach for in situ and real-time quality monitoring in additive manufacturing. Finally, the proposed method is very flexible in terms of realization and can be integrated in any additive manufacturing machine.

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Metadata
Title
In Situ and Real-Time Monitoring of Powder-Bed AM by Combining Acoustic Emission and Artificial Intelligence
Authors
K. Wasmer
C. Kenel
C. Leinenbach
S. A. Shevchik
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-66866-6_20

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