Hyperspectral imaging for prediction of surface roughness in laser powder bed fusion
Open accessAbstract
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 R z 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 R z 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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Powder-based processes are prone to issues of porosity, lack-of-fusion, balling, and cracks, while geometry deviation, shape shrinkage, surface imperfections, layer misalignment, and adhesion issues are the main concerns in the FFF processes.
“Powder-based processes are prone to issues of porosity, lackof-fusion, balling, and cracks [428,430,431], while geometry deviation, shape shrinkage, surface imperfections, layer misalignment, and adhesion issues [432-435] are the main concerns in the FFF processes.”
From A comprehensive review of lunar-based manufacturing and construction (2024) · cites this paper as [435]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Additive manufacturing (3D printing): A review of materials, methods, applications and challenges
Ngo, T. D., Kashani, A., Imbalzano, G., Nguyen, K. T. Q., & Hui, D. (2018) · Composites Part B: Engineering · doi:10.1016/j.compositesb.2018.02.012 · Cited by 8,431 · see citing works
Metal Additive Manufacturing: A Review of Mechanical Properties
Lewandowski, J. J., & Seifi, M. (2016) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070115-032024 · Cited by 1,744 · see citing works
Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing
Everton, S. K., Hirsch, M., Stravroulakis, P., Leach, R. K., & Clare, A. T. (2016) · Materials & Design · doi:10.1016/j.matdes.2016.01.099 · Cited by 1,469 · see citing works
Laser based additive manufacturing in industry and academia
Schmidt, M., Merklein, M., Bourell, D. L., Dimitrov, D., Hausotte, T., Wegener, K., Overmeyer, L., Vollertsen, F., & Levy, G. N. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.05.011 · Cited by 604 · see citing works
Self-citationCharacterization of Metal Powders Used for Additive Manufacturing
Slotwinski, J. A., Garboczi, E. J., Stutzman, P. E., et al. (2014) · Journal of Research of the National Institute of Standards and Technology · doi:10.6028/jres.119.018 · Cited by 507 · see citing works
In-process sensing in selective laser melting (SLM) additive manufacturing
Spears, T., & Gold, S. A. (2016) · Integrating materials and manufacturing innovation · doi:10.1186/s40192-016-0045-4 · Cited by 503 · see citing works
Machine Learning in Additive Manufacturing: A Review
Meng, L., McWilliams, B., Jarosinski, W., Park, H., Jung, Y., Lee, J.-H., & Zhang, J. (2020) · JOM · doi:10.1007/s11837-020-04155-y · Cited by 476 · see citing works
Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R., & Phoha, S. (2018) · Additive manufacturing · doi:10.1016/j.addma.2018.04.005 · Cited by 448 · see citing works
Clijsters, S., Craeghs, T., Buls, S., et al. (2014) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-014-6214-8 · Cited by 441 · see citing works
Scime, L., & Beuth, J. (2018) · Additive manufacturing · doi:10.1016/j.addma.2018.11.010 · Cited by 372 · see citing works
Grasso, M., Demir, A. G., Previtali, B., et al. (2017) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2017.07.001 · Cited by 234 · see citing works
Malekipour, E., & El-Mounayri, H. A. (2017) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-1172-6 · Cited by 233 · see citing works
Mani, M., Lane, B. M., Donmez, M. A., et al. (2016) · International Journal of Production Research · doi:10.1080/00207543.2016.1223378 · Cited by 229 · see citing works
Bär, F., Berger, L., Jauer, L., et al. (2019) · Acta Biomaterialia · doi:10.1016/j.actbio.2019.05.056 · Cited by 200 · see citing works
Machine‐Learning‐Based Monitoring of Laser Powder Bed Fusion
Yuan, B., Guss, G. M., Wilson, A. C., et al. (2018) · Advanced Materials Technologies · doi:10.1002/admt.201800136 · Cited by 162 · see citing works
In situ surface topography of laser powder bed fusion using fringe projection
Zhang, B., Ziegert, J. C., Farahi, F., et al. (2016) · Additive manufacturing · doi:10.1016/j.addma.2016.08.001 · Cited by 159 · see citing works
Riener, K., Nikolaj, A., Ziegelmeier, S., Ramakrishnan, R., Haferkamp, L., Spierings, A. B., & Leichtfried, G. J. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101286 · Cited by 156 · see citing works
Layerwise Monitoring of the Selective Laser Melting Process by Thermography
Krauss, H., Zeugner, T., & Zaeh, M. F. (2014) · Physics Procedia · doi:10.1016/j.phpro.2014.08.097 · Cited by 130 · see citing works
Part geometry and conduction-based laser power control for powder bed fusion additive manufacturing
Yeung, H., Lane, B. M., & Fox, J. C. (2019) · Additive manufacturing · doi:10.1016/j.addma.2019.100844 · Cited by 114 · see citing works
Optimising metal powders for additive manufacturing
Clayton, J. (2014) · Metal Powder Report · doi:10.1016/s0026-0657(14)70223-1 · Cited by 88 · see citing works
In situ multi-elemental analysis by laser induced breakdown spectroscopy in additive manufacturing
Lednev, V. N., Sdvizhenskii, P. A., Asyutin, R. D., et al. (2018) · Additive manufacturing · doi:10.1016/j.addma.2018.10.043 · Cited by 62 · see citing works
In situ process monitoring in selective laser sintering using optical coherence tomography
Gardner, M. R., Lewis, A., Park, J., et al. (2018) · Optical Engineering · doi:10.1117/1.oe.57.4.041407 · Cited by 60 · see citing works
Selective Laser Melting of Magnesium and Magnesium Alloys
Gieseke, M., Noelke, C., Kaierle, S., Wesling, V., & Haferkamp, H. (2013) · Springer eBooks · doi:10.1007/978-3-319-48150-0_11 · Cited by 55 · see citing works
Self-citationSelective Laser Melting of Magnesium and Magnesium Alloys
Gieseke, M., Noelke, C., Kaierle, S., et al. (2013) · doi:10.1002/9781118663004.ch11 · Cited by 11 · see citing works
Self-citationDevelopment of a hyperspectral imaging technique for monitoring laser-based material processing
Staudt, T., Tenner, F., Klämpfl, F., et al. (2017) · Journal of Laser Applications · doi:10.2351/1.4983265 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Bhavar V, Kattire P, Patil V, Khot S, Gujar K, Singh R (2014) A review on powder bed fusion technology of metal additive manufacturing. In: 4th international conference and exhibition on additive manufacturing technologies-AM-2014, pp 1-2
Cited by
Sorted by citations
A comprehensive review of lunar-based manufacturing and construction
Azami, M., Kazemi, Z., Moazen, S., Dubé, M., Potvin, M.-J., & Skonieczny, K. (2024) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2024.101045 · Cited by 57 · see citing works
- Authors
- Gerdes, N., Hoff, C., Hermsdorf, J., Kaierle, S., & Overmeyer, L.
- Affiliations
- Laser Zentrum Hannover (DE)
- Corresponding author
- Gerdes, Niklas
- Venue
- The International Journal of Advanced Manufacturing Technology
- Published
- 2021-06-12
- Volume
- 115
- Issue
- 4
- Pages
- 1249-1258
- ISSN
- 0268-3768 (ISSN-L)
- 1433-3015
- OpenAlex
- W3170810166
- Cited by
- 36
- References
- 25
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 9 · 2023: 9 · 2022: 2 · 2021: 1
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The International Journal of Advanced Manufacturing Technology (publishedVersion, cc-by)
- Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) (submittedVersion, cc-by-nc-nd)
- Leibniz Universität Hannover (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (SPP 2122)
- Laser Zentrum Hannover e.V.
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Computational Mechanics › Surface Roughness and Optical Measurements
- Keywords
- hyperspectral imaging, laser powder bed fusion, surface roughness prediction, in-situ process monitoring, metal additive manufacturing, WE43 magnesium alloy, biomedical implants, convolutional neural networks, hyperparameter optimization, quality assurance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
