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.
- Award ID(s):
- 1934230
- NSF-PAR ID:
- 10426194
- Date Published:
- Journal Name:
- Frontiers in Mechanical Engineering
- Volume:
- 9
- ISSN:
- 2297-3079
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Wire arc additive manufacturing (WAAM) has gained attention as a feasible process in large-scale metal additive manufacturing due to its high deposition rate, cost efficiency, and material diversity. However, WAAM induces a degree of uncertainty in the process stability and the part quality owing to its non-equilibrium thermal cycles and layer-by-layer stacking mechanism. Anomaly detection is therefore necessary for the quality monitoring of the parts. Most relevant studies have applied machine learning to derive data-driven models that detect defects through feature and pattern learning. However, acquiring sufficient data is time- and/or resource-intensive, which introduces a challenge to applying machine learning-based anomaly detection. This study proposes a multisource transfer learning method that generates anomaly detection models for balling defect detection, thus ensuring quality monitoring in WAAM. The proposed method uses convolutional neural network models to extract sufficient image features from multisource materials, then transfers and fine-tunes the models for anomaly detection in the target material. Stepwise learning is applied to extract image features sequentially from individual source materials, and composite learning is employed to assign the optimal frozen ratio for converging transferred and present features. Experiments were performed using a gas tungsten arc welding-based WAAM process to validate the classification accuracy of the models using low-carbon steel, stainless steel, and Inconel.
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Andrew Yeh-Ching Nee, editor-ion-chief (Ed.)Wire arc additive manufacturing (WAAM) has received increasing use in 3D printing because of its high deposition rates suitable for components with large and complex geometries. However, the lower forming accuracy of WAAM than other metal additive manufacturing methods has imposed limitations on manufacturing components with high precision. To resolve this issue, we herein implemented the hybrid manufacturing (HM) technique, which integrated WAAM and subtractive manufacturing (via a milling process), to attain high forming accuracy while taking advantage of both WAAM and the milling process. We describe in this paper the design of a robot-based HM platform in which the WAAM and CNC milling are integrated using two robotic arms: one for WAAM and the other for milling immediately following WAAM. The HM was demonstrated with a thin-walled aluminum 5356 component, which was inspected by X-ray micro-computed tomography (μCT) for porosity visualization. The temperature and cutting forces in the component under milling were acquired for analysis. The surface roughness of the aluminum component was measured to assess the surface quality. In addition, tensile specimens were cut from the components using wire electrical discharge machining (WEDM) for mechanical testing. Both machining quality and mechanical properties were found satisfactory; thus the robot-based HM platform was shown to be suitable for manufacturing high-quality aluminum parts.more » « less
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Herein, the feasibility of the gas tungsten arc welding‐based wire + arc additive manufacturing process for fabricating thin wall structures of niobium‐1 wt% zirconium (NbZr1) alloy is investigated. Three different heat input conditions (low, medium, and high) have been selected for fabricating it. The microstructure is characterized by using optical microscopy, scanning electron microscopy, X‐ray diffraction, energy‐dispersive spectroscopy, and electron backscattered diffraction (EBSD). The microstructure shows the columnar dendritic structure elongated in the build direction. No cracks or porosity are observed in the structure. Average Vickers hardness for low, medium, and high heat input conditions are 146.6, 162.1, and 163.5 HV, respectively. There is an increasing trend of microhardness value along the deposition height, which can be attributed to the difference in secondary dendritic arm spacing and the formation of precipitates. The tensile strength of the specimen is comparable to the conventional and additively manufactured structures. EBSD analysis confirms that possible subgrains are responsible for good mechanical properties at room temperature. In the majority of the tensile samples, the failure mechanism has been identified as a ductile fracture. The mechanical characteristics fluctuate with locations in each of the thin walls, suggesting anisotropy in the deposits.
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S. Kapoor, editor-in-chief (Ed.)In this paper, a novel hybrid wire arc additive manufacturing (WAAM) and ultrasonic nanocrystal surface modification (UNSM) on porosity manipulation and surface properties of aluminum 5356 alloys was studied. The goal is to improve the quality of the WAAM-built part by eliminating bigger pores and reducing its size, reducing surface roughness, and increasing surface hardness. The as-built WAAM and WAAM-UNSM-treated samples were quantitatively studied for porosity using an X-ray micro-computed tomography (μ-CT). The surface roughness was measured on the surface profile of the same samples before and after UNSM treatment. Followed by the Vickers micro-hardness tests to evaluate the hardness modified by the influence of the UNSM treatment. It was found that the bigger pores in the as-built WAAM samples were eliminated and the medium-sized pores were shrunk to almost half the size after the UNSM treatment. Further, the UNSM treatment showed a significant improvement in both surface roughness and hardness on the WAAM Al5356 samples. This experimental work demonstrates the critical advantages of hybrid WAAM-UNSM in improving the qualities of the WAAM processed parts.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fmech.2023.1130407
