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.
more »
« less
Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning
Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the electron backscatter diffraction patterns, enabling a quantitative measure of the microstructure differences in a reduced representation domain. We also demonstrate the capability of predicting new microstructures within the representation domain using a regeneration neural network, from which we are able to explore the physical insights into the implicitly expressed microstructure descriptors by mapping the regenerated microstructures as a function of principal component values. We validate the effectiveness of the framework using samples fabricated by a solid-state AM technology, additive friction stir deposition, which typically results in equiaxed microstructures.
more »
« less
- Award ID(s):
- 1825646
- NSF-PAR ID:
- 10335818
- Date Published:
- Journal Name:
- Journal of Materials Research
- Volume:
- 35
- Issue:
- 15
- ISSN:
- 0884-2914
- Page Range / eLocation ID:
- 1936 to 1948
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Owing to the reduced defects, low cost, and high efficiency, the additive manufacturing (AM) technique has attracted increasingly attention and has been applied in high-entropy alloys (HEAs) in recent years. It was found that AM-processed HEAs possess an optimized microstructure and improved mechanical properties. However, no report has been proposed to review the application of the AM method in preparing bulk HEAs. Hence, it is necessary to introduce AM-processed HEAs in terms of applications, microstructures, mechanical properties, and challenges to provide readers with fundamental understanding. Specifically, we reviewed (1) the application of AM methods in the fabrication of HEAs and (2) the post-heat treatment effect on the microstructural evolution and mechanical properties. Compared with the casting counterparts, AM-HEAs were found to have a superior yield strength and ductility as a consequence of the fine microstructure formed during the rapid solidification in the fabrication process. The post-treatment, such as high isostatic pressing (HIP), can further enhance their properties by removing the existing fabrication defects and residual stress in the AM-HEAs. Furthermore, the mechanical properties can be tuned by either reducing the pre-heating temperature to hinder the phase partitioning or modifying the composition of the HEA to stabilize the solid-solution phase or ductile intermetallic phase in AM materials. Moreover, the processing parameters, fabrication orientation, and scanning method can be optimized to further improve the mechanical performance of the as-built-HEAs.more » « less
-
null (Ed.)Production-volume and cost requirements currently limit machine tool manufacturers’ ability to produce application-specific tooling with traditional methods, motivating the development of innovative manufacturing technologies. To this end, we detail a manufacturing framework for the design and production of application-specific cutting tools based on industry standard tungsten carbide-cobalt (WC-Co)-based “carbide” cutting materials using additive manufacturing (AM). Herein, novel diamond-reinforced carbide structures were designed and manufactured via AM and subsequently tested in comparison to current commercial products that are traditionally-processed. The resulting diamond-reinforced composites were free from large scale cracking and maintained microstructures with multiple reinforcing phases. Diamond incorporation had a remarkable effect on the processing, microstructure, and machining performance of the WC-Co based material in comparison to a commercial carbide cutting tool of similar composition as well as the base WC-Co matrix. Detailed microstructure and phase analysis, as well as machining experiments, demonstrate the ability to exploit laser-based directed energy deposition (DED)-based AM to create multifunctional cutting tools that can be designed to meet ever-increasing manufacturing demands across many industries.more » « less
-
Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties.more » « less
-
null (Ed.)316L stainless steel (SS) to Al12Si aluminum alloy structures were processed, tailoring the compositionally graded interface on a SS 316 substrate using a directed energy deposition (DED)-based additive manufacturing (AM) process. Applying such a compositionally graded transition on bimetallic materials, especially joining two dissimilar metals, could avoid the mechanical property mismatch. This study's objective was to understand the processing parameters that influence the properties of AM processed SS 316L to Al12Si bimetallic structures. Two different approaches fabricated these bimetallic structures. The results showed no visible defects on the as-fabricated samples using 4 layers of Al-rich mixed composition as the transition section. The microstructural characterization showed a unique morphology in each section. Both cooling rate and compositional variations caused microstructural variation. FeAl, Fe2Al5, and FeAl3 intermetallic phases were formed at the compositionally graded transition section. After stress relief heat-treatment of SS 316L/Al12Si bimetallic samples, diffused intermetallic phases were seen at the compositionally graded transition. At the interface, as processed, bimetallic structures had a microhardness value of 834.2 ± 107.1 HV0.1, which is a result of the FeAl3 phase at the compositionally graded transition area. After heat-treatment, the microhardness value reduced to 578.7 ± 154.1 HV0.1 due to more Fe dominated FexAly phase formation. The compression test results showed that the non-HT and HT SS 316L/Al12Si bimetallic structures had a similar maximum compressive strength of 299.4 ± 22.1 MPa and 270.1 ± 27.1 MPa, respectively.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1557/jmr.2020.120
