Effect of Al–5Ti–1B Addition on Solidification Microstructure and Hot Deformation Behavior of DC-Cast Al–Zn–Mg–Cu Alloy

This study investigated the effect of adding Al–5Ti–1B grain refiner on the solidification microstructure and hot deformation behavior of direct-chill (DC) cast Al–Zn–Mg–Cu alloys. The grain refiner significantly decreased the grain size and modified the morphology. Fine-grained (FG) alloys with grain refiners exhibit coarse secondary phases with a reduced number density compared to coarse-grained (CG) alloys without grain refiners. Dynamic recrystallization (DRX) was enhanced at higher compression temperatures and lower strain rates in the CG and FG alloys. Both particle stimulated nucleation (PSN) and continuous dynamic recrystallization (CDRX) are enhanced in the FG alloys, resulting in decreased peak stress values (indicating DRX onset) at 450°C. The peak stress of the FG alloys was higher at 300-400°C than that of the CG alloys because of grain refinement hardening over softening by enhanced DRX

Effects of TiC Addition on Strain-Induced Martensite Transformation and Mechanical Properties of Nanocrystalline Fe-Mn Alloy Fabricated by Spark Plasma Sintering

The effect of TiC content on the microstructure and mechanical properties of a nanocrystalline Fe-Mn alloy was investigated by XRD analysis, TEM observation, and mechanical tests. A sintered Fe-Mn alloy sample with nano-sized crystallites was obtained using spark plasma sintering. Crystallite size, which is used as a hardening mechanism, was measured by X-ray diffraction peak analysis. It was observed that the addition of TiC influenced the average size of crystallites, resulting in a change in austenite stability. Thus, the volume fraction of austenite at room temperature afterthe sintering process was also modified by the TiC addition. The martensite transformation during cooling was suppressed by adding TiC, which lowered the martensite start temperature. The plastic behavior and the strain-induced martensite kinetics formed during plastic deformation are discussed with compressive stress-strain curves and numerical analysis for the transformation kinetics

Microstructural and Mechanical Characteristics of Non-Equiatomic High Entropy Alloy FeMnCoCr Prepared by Spark Plasma Sintering

In this study, a non-equiatomic high entropy alloy was fabricated using the spark plasma sintering method, and its microstructural features and mechanical properties were investigated. The chemical composition of FeMnCoCr was determined by using the entropy calculation related to the design of high entropy alloys. A bulk sample with the same composition was also prepared using the conventional metallurgical processes of casting and hot rolling. The microstructures of the samples fabricated by these different processes were compared by microscope observation, and a quantitative phase analysis was carried out using FE-SEM. Hardness measurement was used to evaluate mechanical properties. Particular attention was paid to microstructural changes due to heat treatment, which was analyzed by considering how austenite stability is affected by grain refinement

Austenitic Stability and Strain-Induced Martensitic Transformation Behavior of Nanocrystalline FeNiCrMoC HSLA Steels

The austenitic stability and strain-induced martensitic transformation behavior of a nanocrystalline FeNiCrMoC alloy were investigated. The alloy was fabricated by high-energy ball milling and spark plasma sintering. The phase fraction and grain size were measured using X-ray diffraction. The grain sizes of the milled powder and the sintered alloy were confirmed to be on the order of several nanometers. The variation in the austenite fraction according to compressive deformation was measured, and the austenite stability and strain-induced martensitic transformation behavior were calculated. The hardness was measured to evaluate the mechanical properties according to compression deformation, which confirmed that the hardness increased to 64.03 HRC when compressed up to 30%

Effect of Composition on Strain-Induced Martensite Transformation of FeMnNiC Alloys Fabricated by Powder Metallurgy

We investigated the austenite stability and mechanical properties in FeMnNiC alloy fabricated by spark plasma sintering. The addition of Mn, Ni, and C, which are known austenite stabilizing elements, increases its stability to a stable phase existing above 910°C in pure iron; as a result, austenitic microstructure can be observed at room temperature, depending on the amounts of Mn, Ni, and C added. Depending on austenite stability and the volume fraction of austenite at a given temperature, strain-induced martensite transformation during plastic deformation may occur. Both stability and the volume fraction of austenite can be controlled by several factors, including chemical composition, grain size, dislocation density, and so on. The present study investigated the effect of carbon addition on austenite stability in FeMnNi alloys containing different Mn and Ni contents. Microstructural features and mechanical properties were analyzed with regard to austenite stability

Prediction and mechanism explain of austenite-grain growth during reheating of alloy steel using XAI

Austenite-grain growth is an important factor in heat treatments, such as annealing and normalizing, for controlling the microstructures and overall properties of alloy steels. Thus, several researchers have proposed empirical equations for predicting austenite-grain growth in the reheating process. However, it is still important to improve the accuracy of the prediction model and analyze the model mechanisms and variable importance. Machine-learning models are key to enhancing prediction accuracy without the need for additional experiments. Therefore, machine-learning models are applied to predict austenite-grain growth with greater accuracy. The explainable artificial intelligence (XAI) is adopted to discuss the variable importance and mechanisms of the machine-learning model. 458 useable data points are collected from the literature, and then analyzed and eliminated outliers using a boxplot. The hyperparameters are adjusted using five-fold cross-validation and a grid search. Random forest regression (RFR) is selected based on its accuracy. The RFR is compared with an empirical equation to confirm the enhancement of the model accuracy. The variable importance and mechanisms of the machine-learning model are then discussed using the SHAP analysis

Effect of Sintering Holding Time and Cooling Rate on the Austenite Stability and Mechanical Properties of Nanocrystalline FeCrC Alloy

The effects of the sintering holding time and cooling rate on the microstructure and mechanical properties of nanocrystalline Fe-Cr-C alloy were investigated. Nanocrystalline Fe-1.5Cr-1C (wt.%) alloy was fabricated by mechanical alloying and spark plasma sintering. Different process conditions were applied to fabricate the sintered samples. The phase fraction and grain size were measured using X-ray powder diffraction and confirmed by electron backscatter diffraction. The stability and volume fraction of the austenite phase, which could affect the mechanical properties of the Fe-based alloy, were calculated using an empirical equation. The sample names consist of a number and a letter, which correspond to the holding time and cooling method, respectively. For the 0A, 0W, 10A, and 10W samples, the volume fraction was measured at 5.56, 44.95, 6.15, and 61.44 vol.%. To evaluate the mechanical properties, the hardness of 0A, 0W, 10A, and 10W samples were measured as 44.6, 63.1, 42.5, and 53.8 HRC. These results show that there is a difference in carbon diffusion and solubility depending on the sintering holding time and cooling rate

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution