This work studies Metal Inert Gas (MIG) based Wire Arc Additive Manufacturing (WAAM) for nanoparticle enhanced AA7075. MIG WAAM is important for production and large structures due to its high deposition rates compared to Tungsten Inert Gas (TIG) or powder-based AM processes. Both MIG and TIG take advantage of wire feedstock, which is more readily available than powdered metals since the welding technology has been established for decades. Powder based processes allow for more complicated geometries but take significantly more time to produce and can suffer from voids which lead to non-uniform part density. TIG is normally used in welding of aluminum because it results in fewer defects, but the TiC/TiB2 nanoparticles eliminate solidification cracking normally associated with high strength aluminum alloys during welding. Porosity is another problem faced when welding aluminum, which can be affected by many things including deposition parameters, atmosphere and even the welding equipment used. Effects of different deposition parameters have been comprehensively studied including the deposition geometry and metallurgical properties. The process is also monitored with current/voltage measurement and high-speed imaging to understand the droplet transfer mode and molten pool development. The results are used to optimize process parameters to achieve the fewest defects possible while comparing different metal transfer modes. Multi-scale characterizations will be performed to examine the porosity distribution, solidification mode and grain size through optical microscopy. Future works will explore the distribution of secondary phases, precipitates, and nanoparticles through scanning electron microscopy (SEM) as well as conducting some mechanical testing of the as built structures such as hardness mapping and tensile tests.
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.
more » « less- Award ID(s):
- 2002840
- NSF-PAR ID:
- 10363994
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Laser powder bed fusion (LPBF) is a 3D printing technology that can print metal parts with complex geometries without the design constraints of traditional manufacturing routes. However, the parts printed by LPBF normally contain many more pores than those made by conventional methods, which severely deteriorates their properties. Here, by combining in-situ high-speed high-resolution synchrotron x-ray imaging experiments and multi-physics modeling, we unveil the dynamics and mechanisms of pore motion and elimination in the LPBF process. We find that the high thermocapillary force, induced by the high temperature gradient in the laser interaction region, can rapidly eliminate pores from the melt pool during the LPBF process. The thermocapillary force driven pore elimination mechanism revealed here may guide the development of 3D printing approaches to achieve pore-free 3D printing of metals.
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Three typical types of defects, i.e., keyholes, lack of fusion (LoF), and gas-entrapped pores (GEP), characterized by various features (e.g., volume, surface area, etc.), are generated under different process parameters of laser beam powder bed fusion (L-PBF) processes in additive manufacturing (AM). The different types of defects deteriorate the mechanical performance of L- PBF components, such as fatigue life, to a different extent. However, there is a lack of recognized approaches to classify the defects automatically and accurately in L-PBF components. This work presents a novel hierarchical graph convolutional network (H-GCN) to classify different types of defects by a cascading GCN structure with a low-level feature (e.g., defect features) layer and a high-level feature (e.g., process parameters) layer. Such an H-GCN not only leverages the multi- level information from process parameters and defect features to classify the defects but also explores the impact of process parameters on defect types and features. The H-GCN is evaluated through a case study with X-ray computed tomography (CT) L-PBF defect datasets and compared with several machine learning methods. H-GCN exhibits an outstanding classification performance with an F1-score of 1.000 and reveals the potential effect of process parameters on three types of defects.more » « less
