The testing and study of emerging materials-such as additively manufactured materials-demands for specimen designs that are cost effective and time saving. The design of a small-sized bending-fatigue test specimen for an ultrasonic fatigue testing system is reported in this paper. The design is optimized based on the finite element analysis and analytical-solution results to achieve the proper vibration shape and stress distribution. The proposed design is evaluated in the high-and very-high-cycle fatigue regimes under 20-kHz frequency. Both simulation and testing results confirm that the desirable vibration mode occurs and the specimen fails at the designated test (gauge) section, where the maximum stress exists. The stress-life (S-N) curve is obtained for Inconel alloy 718 and indicates an expected trend.
In this paper, small blocks of 17-4 PH stainless steel were manufactured via extrusion-based bound powder extrusion (BPE)/atomic diffusion additive manufacturing (ADAM) technology in two different orientations. Ultrasonic bending-fatigue and uniaxial tensile tests were carried out on the test specimens prepared from the AM blocks. Specifically, a recently-introduced small-size specimen design is employed to carry out time-efficient fatigue tests. Based on the results of the testing, the stress–life (S-N) curves were created in the very high-cycle fatigue (VHCF) regime. The effects of the printing orientation on the fatigue life and tensile strength were discussed, supported by fractography taken from the specimens’ fracture surfaces. The findings of the tensile test and the fatigue test revealed that vertically-oriented test specimens had lower ductility and a shorter fatigue life than their horizontally-oriented counterparts. The resulting S-N curves were also compared against existing data in the open literature. It is concluded that the large-sized pores (which originated from the extrusion process) along the track boundaries strongly affect the fatigue life and elongation of the AM parts.
In this paper, the phase structure, composition distribution, grain morphology, and hardness of Al6061 alloy samples made with additive friction stir deposition (AFS-D) were examined. A nearly symmetrical layer-by-layer structure was observed in the cross section (vertical with respect to the fabrication-tool traversing direction) of the as-deposited Al6061 alloy samples made with a back-and-forth AFS-D strategy. Equiaxed grains were observed in the region underneath the fabrication tool, while elongated grains were seen in the “flash region” along the mass flow direction. No clear grain size variance was discovered along the AFS-D build direction except for the last deposited layer. Grains were significantly refined from the feedstock (~163.5 µm) to as-deposited Al6061 alloy parts (~8.5 µm). The hardness of the as-fabricated Al6061 alloy was lower than those of the feedstock and their heat-treated counterparts, which was ascribed to the decreased precipitate content and enlarged precipitate size.
Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.
The solid-state additive friction stir deposition (AFSD) process is a layer-by-layer metal 3D-printing technology. In this study, AFSD is used to fabricate Al–Cu–Li 2050 alloy parts. The hardness values for various regions of the as-deposited built parts are measured, and the results are contrasted with those of the feedstock material. The as-fabricated Al2050 parts are found to have a unique hardness distribution due to the location-specific variations in the processing temperature profile. The XRD results indicate the presence of the secondary phases in the deposited parts, and EDS mapping confirms the formation of detectable alloying particles in the as-deposited Al2050 matrix. The AFSD thermal–mechanical process causes the unique hardness distribution and the reduced microhardness level in the AFSD components, in contrast to those of the feedstock material.
Solid-state Friction Stir Additive Manufacturing has recently gained attention as a result of its capacity to fabricate large-scale parts while preserving the mechanical properties of the feedstock material. However, the correlation between the quality of layer-by-layer bonding of the deposited metal and processing parameters has remained unknown. Neutron imaging techniques, with 90% total transmission per cm, are employed for Al6061 parts fabricated by MELD® Technology as a non-destructive evaluation approach for the first time to investigate the layer-by-layer structure of a stadium-shaped ingot in different sections. The post-processed results show the fabricated parts with an optimized set of processing parameters are void-free. However, the hydrocarbon-based feedstock lubricant segregates between the layers, which consequently may lead to non-uniform weaker mechanical properties along the build direction and stimulate crack initiation during mechanical loading. The tensile test results show 14% lower strain-to-failure values in alleged contaminated areas in transmission imaging results. Additionally, layer bonding is significantly impacted by hot-on-hot and hot-on-cold layer deposition schemes, especially for larger layer thicknesses.
Laser powder bed fusion (LPBF)-based additive manufacturing (AM) has the flexibility in fabricating parts with complex geometries. However, using non-optimized processing parameters or using certain feedstock powders, internal defects (pores, cracks, etc.) may occur inside the parts. Having a thorough and statistical understanding of these defects can help researchers find the correlations between processing parameters/feedstock materials and possible internal defects. To establish a tool that can automatically detect defects in AM parts, in this research, X-ray CT images of Inconel 939 samples fabricated by LPBF are analyzed using U-Net architecture with different sets of hyperparameters. The hyperparameters of the network are tuned in such a way that yields maximum segmentation accuracy with reasonable computational cost. The trained network is able to segment the unbalanced classes of pores and cracks with a mean intersection over union (mIoU) value of 82% on the test set, and has reduced the characterization time from a few weeks to less than a day compared to conventional manual methods. It is shown that the major bottleneck in improving the accuracy is uncertainty in labeled data and the necessity for adopting a semi-supervised approach, which needs to be addressed first in future research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.