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1. In-Situ Characterization of Pore Formation Dynamics in Pulsed Wave Laser Powder Bed Fusion

   https://doi.org/10.3390/ma14112936  

   Hojjatzadeh, S. Mohammad ; Guo, Qilin ; Parab, Niranjan D. ; Qu, Minglei ; Escano, Luis I. ; Fezzaa, Kamel ; Everhart, Wes ; Sun, Tao ; Chen, Lianyi ( June 2021 , Materials)

   null (Ed.)

   Laser powder bed fusion (LPBF) is an additive manufacturing technology with the capability of printing complex metal parts directly from digital models. Between two available emission modes employed in LPBF printing systems, pulsed wave (PW) emission provides more control over the heat input compared to continuous wave (CW) emission, which is highly beneficial for printing parts with intricate features. However, parts printed with pulsed wave LPBF (PW-LPBF) commonly contain pores, which degrade their mechanical properties. In this study, we reveal pore formation mechanisms during PW-LPBF in real time by using an in-situ high-speed synchrotron x-ray imaging technique. We found that vapor depression collapse proceeds when the laser irradiation stops within one pulse, resulting in occasional pore formation during PW-LPBF. We also revealed that the melt ejection and rapid melt pool solidification during pulsed-wave laser melting resulted in cavity formation and subsequent formation of a pore pattern in the melted track. The pore formation dynamics revealed here may provide guidance on developing pore elimination approaches. 

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2. Controlling process instability for defect lean metal additive manufacturing

   https://doi.org/10.1038/s41467-022-28649-2  

   Qu, Minglei ; Guo, Qilin ; Escano, Luis I. ; Nabaa, Ali ; Hojjatzadeh, S. Mohammad H. ; Young, Zachary A. ; Chen, Lianyi ( February 2022 , Nature Communications)

   The process instabilities intrinsic to the localized laser-powder bed interaction cause the formation of various defects in laser powder bed fusion (LPBF) additive manufacturing process. Particularly, the stochastic formation of large spatters leads to unpredictable defects in the as-printed parts. Here we report the elimination of large spatters through controlling laser-powder bed interaction instabilities by using nanoparticles. The elimination of large spatters results in 3D printing of defect lean sample with good consistency and enhanced properties. We reveal that two mechanisms work synergistically to eliminate all types of large spatters: (1) nanoparticle-enabled control of molten pool fluctuation eliminates the liquid breakup induced large spatters; (2) nanoparticle-enabled control of the liquid droplet coalescence eliminates liquid droplet colliding induced large spatters. The nanoparticle-enabled simultaneous stabilization of molten pool fluctuation and prevention of liquid droplet coalescence discovered here provide a potential way to achieve defect lean metal additive manufacturing.

    

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3. A Numerical Study on the Keyhole Formation During Laser Powder Bed Fusion Process

   https://doi.org/10.1115/1.4044100  

   Shrestha, Subin ; Kevin Chou, Y. ( October 2019 , Journal of Manufacturing Science and Engineering)

   The dynamic phenomenon of a melt pool during the laser powder bed fusion (LPBF) process is complex and sensitive to process parameters. As the energy density input exceeds a certain threshold, a huge vapor depression may form, known as the keyhole. This study focuses on understanding the keyhole behavior and related pore formation during the LPBF process through numerical analysis. For this purpose, a thermo-fluid model with discrete powder particles is developed. The powder distribution, obtained from a discrete element method (DEM), is incorporated into the computational domain to develop a 3D process physics model using flow-3d. The melt pool formation during the conduction mode and the keyhole mode of melting has been discerned and explained. The high energy density leads to the formation of a vapor column and consequently pores under the laser scan track. Further, the keyhole shape resulted from different laser powers and scan speeds is investigated. The numerical results indicated that the keyhole size increases with the increase in the laser power even with the same energy density. The keyhole becomes stable at a higher power, which may reduce the occurrence of pores during laser scanning. 

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4. Uncovering acoustic signatures of pore formation in laser powder bed fusion

   https://doi.org/10.1007/s00170-023-12771-6  

   Tempelman, Joshua R. ; Mudunuru, Maruti K. ; Karra, Satish ; Wachtor, Adam J. ; Ahmmed, Bulbul ; Flynn, Eric B. ; Forien, Jean-Baptiste ; Guss, Gabe M. ; Calta, Nicholas P. ; DePond, Phillip J. ; et al ( December 2023 , The International Journal of Advanced Manufacturing Technology)

   We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom$$\varvec{k}$$$k$-means clustering (NMF$$\varvec{k}$$$k$) to learn the underlying spectral patterns associated with pore formation. NMF$$\varvec{k}$$$k$returned a library of basis signals and matching coefficients toblindlyconstruct a feature space based on the PSD estimates in anoptimizedfashion. Moreover, the NMF$$\varvec{k}$$$k$decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMFk, the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

    

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5. Critical instability at moving keyhole tip generates porosity in laser melting

   https://doi.org/10.1126/science.abd1587  

   Zhao, Cang ; Parab, Niranjan D. ; Li, Xuxiao ; Fezzaa, Kamel ; Tan, Wenda ; Rollett, Anthony D. ; Sun, Tao ( November 2020 , Science)

   Laser powder bed fusion is a dominant metal 3D printing technology. However, porosity defects remain a challenge for fatigue-sensitive applications. Some porosity is associated with deep and narrow vapor depressions called keyholes, which occur under high-power, low–scan speed laser melting conditions. High-speed x-ray imaging enables operando observation of the detailed formation process of pores in Ti-6Al-4V caused by a critical instability at the keyhole tip. We found that the boundary of the keyhole porosity regime in power-velocity space is sharp and smooth, varying only slightly between the bare plate and powder bed. The critical keyhole instability generates acoustic waves in the melt pool that provide additional yet vital driving force for the pores near the keyhole tip to move away from the keyhole and become trapped as defects.

    

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