RESUMEN
This study investigates the tensile behaviors of additively manufactured (AM) 17-4PH stainless steels heat-treated within various temperature ranges from 400 °C to 700 °C in order to identify the effective aging temperature. Despite an aging treatment of 400-460 °C increasing the retained austenite content, an enhancement of the tensile properties was achieved without a strength-ductility trade-off owing to precipitation hardening by the Cu particles. Due to the intricate evolution of the microstructure, aging treatments above 490 °C led to a loss in yield strength and ductility. A considerable rise in strength and a decrease in ductility were brought about by the increase in the fraction of precipitation-hardened martensitic matrix in aging treatments over 640 °C. The impact of heat-treatment pathways on aging effectiveness and tensile anisotropy was then examined. Direct aging at 482 °C for an hour had hardly any effect on wrought 17-4PH, but it increased the yield strength of AM counterparts from 436-457 to 588-604 MPa. A solid-solution treatment at 1038 °C for one hour resulted in a significant drop in the austenite fraction, which led to an increase in the yield (from 436-457 to 841-919 MPa) and tensile strengths (from 1106-1127 to 1254-1256 MPa) with a sacrifice in ductility. Improved strength and ductility were realized by a solid-solution followed by an aging treatment, achieving 1371-1399 MPa. The tensile behaviors of AM 17-4PH were isotropic both parallel and perpendicular to the building direction.
RESUMEN
Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, we propose and analyze a deep learning model based on recurrent neural networks (RNNs) optimized for the MI sensor, such that it can detect and classify data that are relatively irregular and diverse compared to the EMG and acoustic signals. Our proposed method combines the long short-term memory (LSTM) and gated recurrent unit (GRU) models to detect and classify metal objects from signals acquired by an MI sensor. First, we configured various layers used in RNN with a basic model structure and tested the performance of each layer type. In addition, we succeeded in increasing the accuracy by processing the sequence length of the input data and performing additional work in the prediction process. An MI sensor acquires data in a non-contact mode; therefore, the proposed deep learning approach can be applied to drone control, electronic maps, geomagnetic measurement, autonomous driving, and foreign object detection.
RESUMEN
In this study, we manufactured a non-equiatomic (CoNi)74.66Cr17Fe8C0.34 high-entropy alloy (HEA) consisting of a single-phase face-centered-cubic structure. We applied in situ neutron diffraction coupled with electron backscattered diffraction (EBSD) and transmission electron microscopy (TEM) to investigate its tensile properties, microstructural evolution, lattice strains and texture development, and the stacking fault energy. The non-equiatomic (CoNi)74.66Cr17Fe8C0.34 HEA revealed a good combination of strength and ductility in mechanical properties compared to the equiatomic CoNiCrFe HEA, due to both stable solid solution and precipitation-strengthened effects. The non-equiatomic stoichiometry resulted in not only a lower electronegativity mismatch, indicating a more stable state of solid solution, but also a higher stacking fault energy (SFE, ~50 mJ/m2) due to the higher amount of Ni and the lower amount of Cr. This higher SFE led to a more active motion of dislocations relative to mechanical twinning, resulting in severe lattice distortion near the grain boundaries and dislocation entanglement near the twin boundaries. The abrupt increase in the strain hardening rate (SHR) at the 1~3% strain during tensile deformation might be attributed to the unusual stress triaxiality in the {200} grain family. The current findings provide new perspectives for designing non-equiatomic HEAs.
RESUMEN
The present work extends the examination of selective laser melting (SLM)-fabricated 15-5 PH steel with the 8%-transient-austenite-phase towards fully-reversed strain-controlled low-cycle fatigue (LCF) test. The cyclic-deformation response and microstructural evolution were investigated via in-situ neutron-diffraction measurements. The transient-austenite-phase rapidly transformed into the martensite phase in the initial cyclic-hardening stage, followed by an almost complete martensitic transformation in the cyclic-softening and steady stage. The compressive stress was much greater than the tensile stress at the same strain amplitude. The enhanced martensitic transformation associated with lower dislocation densities under compression predominantly governed such a striking tension-compression asymmetry in the SLM-built 15-5 PH.
RESUMEN
In-situ thermal cycling neutron diffraction experiments were employed to unravel the effect of thermal history on the evolution of phase stability and internal stresses during the additive manufacturing (AM) process. While the fully-reversible martensite-austenite phase transformation was observed in the earlier thermal cycles where heating temperatures were higher than Af, the subsequent damped thermal cycles exhibited irreversible phase transformation forming reverted austenite. With increasing number of thermal cycles, the thermal stability of the retained austenite increased, which decreased the coefficient of thermal expansion. However, martensite revealed higher compressive residual stresses and lower dislocation density, indicating inhomogeneous distributions of the residual stresses and microstructures on the inside and on the surface of the AM component. The compressive residual stresses that acted on the martensite resulted preferentially from transformation strain and additionally from thermal misfit strain, and the decrease in the dislocation density might have been due to the strong recovery effect near the Ac1 temperature.
