In-situ defect detection and material property evaluationin additive manufacturing using acoustic signal and machinelearning
In-situ monitoring is crucial for detecting defects and estimating material properties to ensure the quality of printed parts in additive manufacturing. Acoustic signals produced during the interaction between the laser and material contain critical information about complex physical mechanism...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-03-01
|
| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30802 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In-situ monitoring is crucial for detecting defects and
estimating material properties to ensure the quality of
printed parts in additive manufacturing. Acoustic signals
produced during the interaction between the laser and
material contain critical information about complex
physical mechanisms such as crack formation. However,
acoustic-based monitoring in laser powder bed fusion (LPBF)
has received little attention due to the noisy environment.
This study presents an acoustic-based real-time process
monitoring method integrated with machine learning for LPBF
process. The key contribution lies in developing a feature
extraction approach that utilize machine learning models
such as random forests and k-nearest neighbors (KNN), and
wavelet transform for defect detection and material
property classification. Microphone data collected during
LPBF experiments capture both laser-material interaction
signals and environmental noise, including contributions
from the laser, fan, and powder flow. A bandpass filter is
applied to isolate relevant signals, followed by wavelet
transform in time- and frequency-domains to obtain
representation of the laser-material interaction. The
results show that the machine learning models achieve an
average material property estimation accuracy of 89%,
highlighting its effectiveness in enhancing the monitoring
process parameters.
|
|---|---|
| ISSN: | 1435-4934 |