In situ process quality monitoring and defect detection for direct metal laser melting.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
19 May 2022
19 May 2022
Historique:
received:
26
09
2021
accepted:
14
04
2022
entrez:
19
5
2022
pubmed:
20
5
2022
medline:
20
5
2022
Statut:
epublish
Résumé
Quality control and quality assurance are challenges in direct metal laser melting (DMLM). Intermittent machine diagnostics and downstream part inspections catch problems after undue cost has been incurred processing defective parts. In this paper we demonstrate two methodologies for in-process fault detection and part quality prediction that leverage existing commercial DMLM systems with minimal hardware modification. Novel features were derived from the time series of common photodiode sensors along with standard machine control signals. In one methodology, a Bayesian approach attributes measurements to one of multiple process states as a means of classifying process deviations. In a second approach, a least squares regression model predicts severity of certain material defects.
Identifiants
pubmed: 35589844
doi: 10.1038/s41598-022-12381-4
pii: 10.1038/s41598-022-12381-4
pmc: PMC9119964
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8503Subventions
Organisme : Air Force Research Laboratory
ID : FA8650-14-C-5702
Informations de copyright
© 2022. The Author(s).
Références
Spears, T. G. & Gold, S. A. In-process sensing in selective laser melting (slm) additive manufacturing. Integr. Mater. Manuf. Innov. 5(1), 16–40 (2016).
doi: 10.1186/s40192-016-0045-4
Chua, Z. Y., Ahn, I. H. & Moon, S. K. Process monitoring and inspection systems in metal additive manufacturing: Status and applications. Int. J. Precision Eng. Manuf. Green Technol. 4(2), 235–245 (2017).
doi: 10.1007/s40684-017-0029-7
Grasso, M. & Colosimo, B. M. Process defects and in situ monitoring meth- ods in metal powder bed fusion: A review. Meas. Sci. Technol. 28(4), 044005 (2017).
doi: 10.1088/1361-6501/aa5c4f
Malekipour, E. & El-Mounayri, H. Common defects and contributing pa- rameters in powder bed fusion am process and their classification for online monitoring and control: A review. Int. J. Adv. Manuf. Technol. 95(1–4), 527–550 (2018).
doi: 10.1007/s00170-017-1172-6
Grasso, M., Demir, A., Previtali, B. & Colosimo, B. In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. Robot. Comput. Integr. Manuf. 49, 229–239 (2018).
doi: 10.1016/j.rcim.2017.07.001
Imani, F. et al. Process mapping and in-process monitoring of porosity in laser powder bed fusion using layerwise optical imaging. J. Manuf. Sci. Eng. 140(10) (2018).
Khanzadeh, M. et al. Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Addit. Manuf. 23, 443–456 (2018).
Lu, Q., Nguyen, N., Hum, A., Tran, T. & Wong, C. Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density. J. Mater. Process. Technol. 271, 520–531 (2019).
doi: 10.1016/j.jmatprotec.2019.04.026
Ye, D., Hong, G. S., Zhang, Y., Zhu, K. & Fuh, J. Y. H. Defect detection in selective laser melting technology by acoustic signals with deep belief networks. Int. J. Adv. Manuf. Technol. 96(5–8), 2791–2801 (2018).
doi: 10.1007/s00170-018-1728-0
Yuan, B. et al. Machine-learning-based monitoring of laser powder bed fusion. Adv. Mater. Technol. 3(12), 1800136 (2018).
doi: 10.1002/admt.201800136
Zhang, Y., Hong, G. S., Ye, D., Zhu, K. & Fuh, J. Y. Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion am process monitoring. Mater. Des. 156, 458–469 (2018).
doi: 10.1016/j.matdes.2018.07.002
Zhang, B., Liu, S. & Shin, Y. C. In-process monitoring of porosity during laser additive manufacturing process. Addit. Manuf. 28, 497–505 (2019).
Bisht, M., Ray, N., Verbist, F. & Coeck, S. Correlation of selective laser melting-melt pool events with the tensile properties of ti-6al-4v eli pro- cessed by laser powder bed fusion. Addit. Manuf. 22, 302–306 (2018).
Kolb, T., Müller, L., Tremel, J. & Schmidt, M. Melt pool monitoring for laser beam melting of metals: Inline-evaluation and remelting of surfaces. Procedia Cirp 74, 111–115 (2018).
doi: 10.1016/j.procir.2018.08.052
Coeck, S., Bisht, M., Plas, J. & Verbist, F. Prediction of lack of fusion porosity in selective laser melting based on melt pool monitoring data. Addit. Manuf. 25, 347–356 (2019).
Montazeri, M. & Rao, P. Sensor-based build condition monitoring in laser powder bed fusion additive manufacturing process using a spectral graph theoretic approach. J. Manuf. Sci. Eng. 140(9) (2018).
Grünberger, T. & Domröse, R. Direct metal laser sintering: Identification of process phenomena by optical in-process monitoring. Laser Technik J. 12(1), 45–48 (2015).
doi: 10.1002/latj.201500007
Sampson, R. et al. An improved methodology of melt pool monitoring of direct energy deposition processes. Optics Laser Technol. 127, 106194 (2020).
doi: 10.1016/j.optlastec.2020.106194
Scime, L. & Beuth, J. Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 25, 151–165 (2019).
Zhang, J., Wang, P. & Gao, R. X. Modeling of layer-wise additive manufacturing for part quality prediction. Procedia Manuf. 16, 155–162 (2018).
doi: 10.1016/j.promfg.2018.10.165
Seifi, S. H., Tian, W., Doude, H., Tschopp, M. A. & Bian, L. Layer-wise modeling and anomaly detection for laser-based additive manufacturing. J. Manuf. Sci. Eng. 141, 081013 (2019).
doi: 10.1115/1.4043898
Gaikwad, A. C. et al. Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion—a single-track study. additive manufacturing (Accepted, In-Press, 2020).
Li, R., Jin, M. & Paquit, V. C. Geometrical defect detection for additive manufacturing with machine learning models. Mater. Des. 206, 109726 (2021).
doi: 10.1016/j.matdes.2021.109726
Mohammadi, M. G., Mahmoud, D. & Elbestawi, M. On the application of machine learning for defect detection in L-PBF additive manufacturing. Opt. Laser Technol. 143, 107338 (2021).
doi: 10.1016/j.optlastec.2021.107338
Gobert, C., Reutzel, E. W., Petrich, J., Nassar, A. R. & Phoha, S. Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. Addit. Manuf. 21, 517–528 (2018).
Williams, J., Dryburgh, P., Clare, A., Rao, P., & Samal, A. Defect detection and monitoring in metal additive manufactured parts through deep learning of spatially resolved acoustic spectroscopy signals. Smart Sustain. Manuf. Syst. 2(1) (2018).
Chen, L., Yao, X., Xu, P., Moon, S. K. & Bi, G. Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning. Virtual Phys. Prototyping 16(1), 50–67 (2021).
doi: 10.1080/17452759.2020.1832695
Snow, Z., Diehl, B., Reutzel, E. W. & Nassar, A. Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. J. Manuf. Syst. 59, 12–26 (2021).
doi: 10.1016/j.jmsy.2021.01.008
Khan, M. F. et al. Real-time defect detection in 3D printing using machine learning. Mater. Today Proc. 42, 521–528 (2021).
doi: 10.1016/j.matpr.2020.10.482
Gaikwad, A. et al. Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion—A single-track study. Addit. Manuf. 36, 101659 (2020).
Carter, W. et al. An open-architecture multi-laser research platform for acceleration of large-scale additive manufacturing (ALSAM). In 2019 International Solid Freeform Fabrication Symposium. University of Texas at Austin (2019).
Blom, R. S., John, F., Dean M. R., Subhrajit, R., & Harry, K. M. Jr. Systems and method for advanced additive manufacturing. U.S. Patent 10,747,202, issued August 18, 2020.