Dynamics of pore formation during laser powder bed fusion additive manufacturing

Martin, Aiden A.; Calta, Nicholas P.; Khairallah, Saad A.; Wang, Jenny; Depond, Philip J.; Fong, Anthony Y.; Thampy, Vivek; Guss, Gabe; Kiss, Andrew M.; Stone, Kevin H.; Tassone, Christopher J.; Weker, Johanna Nelson; Toney, Michael F.; Buuren, T. van; Matthews, Manyalibo J.

doi:10.1038/s41467-019-10009-2

Cited by 508 publications

(194 citation statements)

References 43 publications

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“…In our experimental condition, the pores could not travel far away from the fluctuating keyhole channel and, therefore, were prone to merging with the channel. Another typically reported source of porosity is by the fast collapse of the keyhole channel at the end of the process, which often leads to vapor being trapped in the melt pool and forming pores 11,48,49 . This phenomenon was also observed in our work and can be seen in Fig.…”

Section: Resultsmentioning

confidence: 99%

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Shevchik

Le-Quang

Meylan

et al. 2020

Sci Rep

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Laser welding is a key technology for many industrial applications. However, its online quality monitoring is an open issue due to the highly complex nature of the process. this work aims at enriching existing approaches in this field. We propose a method for real-time detection of process instabilities that can lead to defects. Hard X-ray radiography is used for the ground truth observations of the sub-surface events that are critical for the quality. A deep artificial neural network is applied to reveal the unique signatures of those events in wavelet spectrograms from the laser back-reflection and acoustic emission signals. The autonomous classification of the revealed signatures is tested on reallife data, while the real-time performance is reached by means of parallel computing. The confidence of the quality classification ranges between 71% and 99%, with a temporal resolution down to 2 ms and a computation time per classification task as low as 2 ms. This approach is a new paradigm in the digitization of industrial processes and can be exploited to provide feedbacks in a closed-loop quality control system. The introduction of laser technology in metal welding of metals is dated back to the late 1960s 1,2 when it immediately showed advantages as compared to traditional arc welding 3. The attractions of this technique are in the non-contact processing, the absence of tool wear, high aspect ratio of the melt pool, better material fusion, possibility to process refractory materials, low running costs and high processing speed 3,4. Today, laser welding is a key technology in many fields e.g. automotive 5 and aerospace 3,6 industries, naval and heavy machinery production 7 , medicine and micromechanics 3. Unfortunately, the potential of this technology is not fully exploited, particularly in applications that require the guarantee of high weld quality. The reason is the non-linear nature of light-matter interactions, which complicates the reproducibility of the weld quality in mass production 8-10. The complex dynamics of the process, especially in keyhole welding regime, and its instabilities can cause various defects at the joint 3,10-12. A defect type of particular interest is porosity, which is a hidden threat for the mechanical properties of the workpieces 3,9-11. Obviously, an adequate, robust and low cost quality monitoring system is of great desire. The major challenge in developing such technique is in the difficulties to inspect directly the sub-surface behavior of the process zone in real-life conditions 13. Multiple approaches have been proposed, which are mostly based on mathematical modeling aiming to reconstruct the under surface dynamics using inspections of the surface via measurements of temperature 11,12,14,15 , optical 16,17 and/or acoustic 18,19 emissions (AE). However, those approaches face three main problems. Firstly, modeling often suffers inaccuracies originating from the deviations of the model assumptions from the real parameters' values. More complicated assumptions can be used to imp...

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Section: Resultsmentioning

confidence: 99%

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Shevchik

Le-Quang

Meylan

et al. 2020

Sci Rep

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“…An excessive energy input process region was not determined but experimentation at higher levels of energy density will probably yield greater porosity caused by gas entrapment due to the keyhole formation. This type of defect formation at elevated energy densities levels has been previously denoted by Cunnigham et al [40] through x-ray microtomographies and by Martin et al [41] by means of high speed synchrotron x-ray imaging. Microhardness measurements are reported in figure 11 as a function of energy density.…”

Section: Resultsmentioning

confidence: 73%

Defect-free laser powder bed fusion of Ti–48Al–2Cr–2Nb with a high temperature inductive preheating system

et al. 2020

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In the industrial panorama, laser powder bed fusion (LPBF) systems enable the near net shaping of metal powders into complex geometries with unique design features. This makes the technology appealing for many industrial applications, which require high performance materials combined with lightweight design, lattice structures and organic forms. However, many of the alloys that would be ideal for the realisation of these functional components are classified as difficult to weld due to their cracking sensitivity. γ-TiAl alloys are currently processed via electron beam melting (EBM) to produce components for energy generation applications. The EBM process provides crack-free processing thanks to the preheating stages between layers, but lacks geometrical precision. The use of LPBF could provide the means for higher precision, and therefore an easier post-processing stage. However, industrial LPBF systems employ resistive heating elements underneath the base plate which do not commonly reach the high temperatures required for the processing of γ-TiAl alloys. Thus, elevated temperature preheating of the build part and control over the cooling rate after the deposition process is concluded are amongst the features which require further investigations. In this work, the design and implementation of a novel inductive high temperature LPBF system to process Ti-48Al-2Cr-2Nb is presented. Specimens were built with preheating at 800°C and the cooling rate at the end of the build was controlled at 5°C min −1 . Crack formation was suppressed and apparent density in excess of 99% was achieved.

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“…This data correlates with the smallest porosity percentage of 0.07 %. Martin et al [19] demonstrated that the laser turn forms the major pores value. This explains the low porosity for the "Line" scan strategy.…”

Section: Scan Strategymentioning

confidence: 99%

Structure control of 316L stainless steel through an additive manufacturing

et al. 2019

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The 3D printing process is a recent technique, which allows one to produce parts of complex geometry. The influence of printing parameters on the mechanical and structural properties of many materials has been extensively studied. However, despite the considerable amount of research, the task of comparing the results of different scientific groups is complicated. Each research group performs the investigation with different printing conditions. A lot of works contain not full information about the printing process parameters which were applied. This paper presents the results on the mechanical and structural properties of 316L stainless steel according to variable printing parameters, such as laser density energy, scan strategy, and build direction at other fixed conditions. The results reveal a parabolic dependency between the mechanical properties and the laser density energy. The laser density energy of 161 J/mm 3 leads to the best mechanical characteristics (yield strength of 530 MPa, ultimate strength of 580 MPa, and ductility of 63.2%). Scan strategy does not influence the mechanical properties of the samples printed in the vertical direction. At the same time, the strong scan strategy effect is observed for the samples printed in horizontal direction. The difference in the ultimate strength between the vertically and horizontally printed samples reaches up to 70 MPa.

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Dynamics of pore formation during laser powder bed fusion additive manufacturing

Cited by 508 publications

References 43 publications

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance

Defect-free laser powder bed fusion of Ti–48Al–2Cr–2Nb with a high temperature inductive preheating system

Structure control of 316L stainless steel through an additive manufacturing

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