Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data

[Display omitted] •A novel digital twin method for real-time flaw detection in laser powder bed fusion.•Approach combines in-situ meltpool temperature measurements with computational predictions.•Three types of flaws considered from processing, machine, and cyber intrusions.•Digital twin approach de...

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Bibliographic Details
Published in:	Materials & design Vol. 211; p. 110167
Main Authors:	Yavari, R., Riensche, A., Tekerek, E., Jacquemetton, L., Halliday, H., Vandever, M., Tenequer, A., Perumal, V., Kontsos, A., Smoqi, Z., Cole, K., Rao, P.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01-12-2021 Elsevier
Subjects:	Digital Twin Flaw detection Laser powder bed fusion Meltpool monitoring Thermal Simulations Thermal Simulations Laser powder bed fusion Flaw detection Meltpool monitoring Digital Twin
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Summary:	[Display omitted] •A novel digital twin method for real-time flaw detection in laser powder bed fusion.•Approach combines in-situ meltpool temperature measurements with computational predictions.•Three types of flaws considered from processing, machine, and cyber intrusions.•Digital twin approach detects all the three types of flaws.•Characterization of flaws and microstructure analysis confirms the proposed approach. The goal of this research is the in-situ detection of flaw formation in metal parts made using the laser powder bed fusion (LPBF) additive manufacturing process. This is an important area of research, because, despite the considerable cost and time savings achieved, precision-driven industries, such as aerospace and biomedical, are reticent in using LPBF to make safety–critical parts due to tendency of the process to create flaws. Another emerging concern in LPBF, and additive manufacturing in general, is related to cyber security – malicious actors may tamper with the process or plant flaws inside a part to compromise its performance. Accordingly, the objective of this work is to develop and apply a physics and data integrated strategy for online monitoring and detection of flaw formation in LPBF parts. The approach used to realize this objective is based on combining (twinning) in-situ meltpool temperature measurements with a graph theory-based thermal simulation model that rapidly predicts the temperature distribution in the part (thermal history). The novelty of the approach is that the temperature distribution predictions provided by the computational thermal model were updated layer-by-layer with in-situ meltpool temperature measurements. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts made using a commercial LPBF system. Four such impellers are produced emulating three pathways of flaw formation in LPBF parts, these are: changes in the processing parameters (process drifts); machine-related malfunctions (lens delamination), and deliberate tampering with the process to plant flaws inside the part (cyber intrusions). The severity and nature of the resulting flaws, such as porosity and microstructure heterogeneity, are characterized ex-situ using X-ray computed tomography, optical and scanning electron microscopy, and electron backscatter diffraction. The digital twin approach is shown to be effective for detection of the three types of flaw formation causes studied in this work.
ISSN:	0264-1275 1873-4197
DOI:	10.1016/j.matdes.2021.110167