Details
| Original language | English |
|---|---|
| Pages (from-to) | 1249-1258 |
| Number of pages | 10 |
| Journal | International Journal of Advanced Manufacturing Technology |
| Volume | 115 |
| Issue number | 4 |
| Early online date | 12 Jun 2021 |
| Publication status | Published - Jul 2021 |
| Externally published | Yes |
Abstract
This article discusses the relevance of in situ quality assurance in metal additive manufacturing for cost-efficient product qualification. It presents an approach for monitoring the laser powder bed fusion (LPBF) process using an area-scan hyperspectral camera to predict the surface roughness Rz with the help of a convolutional neural network. These investigations were carried out during LPBF processing of the magnesium alloy WE43 that, due to its bioresorbability and compatibility, holds significant potential for biomedical implants. A data acquisition and processing methodology has been set up to enable efficient management of the hyperspectral data. The hyperspectral images obtained from the process were labeled with the surface roughness Rz as determined by a confocal microscope. The data was used to train a convolutional neural network whose hyperparameters were optimized in a hyperparameter tuning process. The resulting network was able to predict the surface roughness within a mean absolute error (MAE) of 4.1 μm over samples from three different parameter sets. Since this is significantly smaller than the spread of the actual roughness measured (MAE = 14.3 μm), it indicates that the network identified features in the hyperspectral data linking to the roughness. These results provide the basis for future research aiming to link hyperspectral process images to further part properties relevant for quality assurance.
Keywords
- Hyperspectral imaging, Laser powder bed fusion, Machine learning, Metal additive manufacturing, Process monitoring
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Software
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: International Journal of Advanced Manufacturing Technology, Vol. 115, No. 4, 07.2021, p. 1249-1258.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Hyperspectral imaging for prediction of surface roughness in laser powder bed fusion
AU - Gerdes, Niklas
AU - Hoff, Christian
AU - Hermsdorf, Jörg
AU - Kaierle, Stefan
AU - Overmeyer, Ludger
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. The authors gratefully acknowledge the funding by the German Research Foundation (DFG) within the priority program (SPP) 2122 “Materials for Additive Manufacturing (MATframe)”.
PY - 2021/7
Y1 - 2021/7
N2 - This article discusses the relevance of in situ quality assurance in metal additive manufacturing for cost-efficient product qualification. It presents an approach for monitoring the laser powder bed fusion (LPBF) process using an area-scan hyperspectral camera to predict the surface roughness Rz with the help of a convolutional neural network. These investigations were carried out during LPBF processing of the magnesium alloy WE43 that, due to its bioresorbability and compatibility, holds significant potential for biomedical implants. A data acquisition and processing methodology has been set up to enable efficient management of the hyperspectral data. The hyperspectral images obtained from the process were labeled with the surface roughness Rz as determined by a confocal microscope. The data was used to train a convolutional neural network whose hyperparameters were optimized in a hyperparameter tuning process. The resulting network was able to predict the surface roughness within a mean absolute error (MAE) of 4.1 μm over samples from three different parameter sets. Since this is significantly smaller than the spread of the actual roughness measured (MAE = 14.3 μm), it indicates that the network identified features in the hyperspectral data linking to the roughness. These results provide the basis for future research aiming to link hyperspectral process images to further part properties relevant for quality assurance.
AB - This article discusses the relevance of in situ quality assurance in metal additive manufacturing for cost-efficient product qualification. It presents an approach for monitoring the laser powder bed fusion (LPBF) process using an area-scan hyperspectral camera to predict the surface roughness Rz with the help of a convolutional neural network. These investigations were carried out during LPBF processing of the magnesium alloy WE43 that, due to its bioresorbability and compatibility, holds significant potential for biomedical implants. A data acquisition and processing methodology has been set up to enable efficient management of the hyperspectral data. The hyperspectral images obtained from the process were labeled with the surface roughness Rz as determined by a confocal microscope. The data was used to train a convolutional neural network whose hyperparameters were optimized in a hyperparameter tuning process. The resulting network was able to predict the surface roughness within a mean absolute error (MAE) of 4.1 μm over samples from three different parameter sets. Since this is significantly smaller than the spread of the actual roughness measured (MAE = 14.3 μm), it indicates that the network identified features in the hyperspectral data linking to the roughness. These results provide the basis for future research aiming to link hyperspectral process images to further part properties relevant for quality assurance.
KW - Hyperspectral imaging
KW - Laser powder bed fusion
KW - Machine learning
KW - Metal additive manufacturing
KW - Process monitoring
UR - http://www.scopus.com/inward/record.url?scp=85107742757&partnerID=8YFLogxK
U2 - 10.1007/s00170-021-07274-1
DO - 10.1007/s00170-021-07274-1
M3 - Article
AN - SCOPUS:85107742757
VL - 115
SP - 1249
EP - 1258
JO - International Journal of Advanced Manufacturing Technology
JF - International Journal of Advanced Manufacturing Technology
SN - 0268-3768
IS - 4
ER -