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Additive manufacturing (AM) of metals attracts attention because it can produce complex structures in a single step without part-specific tooling. Wire arc additive manufacturing (WAAM), a welding-based method that deposits metal layer by layer, is gaining popularity due to its low cost of operation, feasibility for large-scale part fabrication, and ease of operation. This article presents the fabrication of cylindricalshaped mild steel (ER70S-6) samples with a gas metal arc (MIG)—based hybrid WAAM system. A mechanism for actively cooling the substrate is implemented. Deposition parameters are held constant to evaluate the impact of active cooling on deposition quality, inter-pass cooling time, and internal defects. Surface and volume defects can be seen on the cylindrical sample fabricated without an active cooling setup. Defect quantification and phase analysis are performed. The primary phase formed was α-iron in all samples. Actively cooled deposition cross section showed a 99% decrease of incomplete fusion or porosity, with temperature measured 60 s after deposition averaging 235°C less than non-cooled. Microstructural analysis revealed uniformity along the build direction for actively cooled deposition but non-uniform microstructures without cooling. Hardness decreased by approximately 22HV from the first layer to the final layer in all cases. Property variation can be attributed to the respective processing strategies. The current study has demonstrated that active cooling can reduce production time and porosity while maintaining uniform microstructure along the build direction. Such an approach is expected to enhance the reliability of WAAM-processed parts in the coming days.
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Experimental Analysis of Metal Inert Gas Based Wire Arc Additive Manufacturing of Aluminum Nanocomposite AA7075
Abstract 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.
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- Award ID(s):
- 2044526
- PAR ID:
- 10384992
- Date Published:
- Journal Name:
- ASME 2022 17th International Manufacturing Science and Engineering Conference
- Volume:
- 85819
- Page Range / eLocation ID:
- V002T05A033
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The goal of this work is to detect flaw formation in wire arc additive manufacturing (WAAM). This process uses an electric arc as the energy source in order to melt metallic wire and deposit the new material, similar to metal inert gas (MIG) welding. Industry has been slow to adopt WAAM due to the lack of process consistency and reliability. The WAAM process is susceptible to a multitude of stochastic disturbances that cause instability in the electric arc. These arc instabilities eventually lead to flaw formation such as porosity, spatter, and excessive deviations in the desired geometry. Therefore, the objective of this work is to detect flaw formation using in-situ acoustic (sound) data from a microphone installed near the electric arc. This data was processed using a novel wavelet integrated graph theory approach. This approach detected the onset of multiple types of flaw formations with a false alarm rate of less than 2%. Using this method, this work demonstrates the potential for in-situ monitoring and flaw detection of the WAAM process in a computationally tractable manner.more » « less
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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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/MSEC2022-85413
