The softening process leads to a decrease in the strength properties of the alloys by reducing the strengthening mechanisms. Simulation and prediction of the Vickers hardness of AZ91 magnesium alloy using artificial neural network models. Crystals.
Effect of ECAP on the Microstructure and Mechanical Properties of a Rolled Mg-2Y-0.6Nd-0.6Zr
Magnesium Alloy
- Introduction
- Materials and Methods
- Results 1. Microstructure
- Discussion
- Conclusions
After one pass of ECAP, the average grain size of the alloy decreased from ~11.2 μm to ~2.43 μm. Finally, after one pass of ECAP, the strength of the alloy decreased and the plasticity increased.
Comparison of the Crystal Structure and Wear Resistance of Co-Based Alloys with Low Carbon
Results
The most characteristic feature of the Co-SLS structure is the presence of numerous stacking faults (SFs), which are arranged in two directions (indicated by red lines) and the presence of nanoprecipitates of two types (elongate and spherical), which are also evenly distributed (Figure 3b). The presence of the σ phase was confirmed by SAED from the area marked in Figure 10.
The Effect of Lath Martensite Microstructures on the Strength of Medium-Carbon Low-Alloy Steel
Experimental Procedures 1. Materials and Heat Treatment
The chemical composition of the experimental sample was determined with an inductively coupled plasma emission spectrometer (ICP-6300, Thermo Fisher Scientific Company, Waltham, USA), and the result is shown in Table 1. Figure 1 shows the dilametric curve of test sample, indicating that the Ac1 (initial point austenitization) and Ac3 (end point austenitization) were 735◦C and 818◦C, respectively.
Results and Discussion 1. Microstructural Characterization
The relationship between the width and strength of the laths only followed the Hall-Petch relationship in the medium carbon steel. It can therefore be concluded that the EGS of strength appears to be related to the carbon content. When the carbon content is low, HAGBs are the most significant barriers to the dislocation motion, and consequently the hindering effect of LAGB can be ignored.
Previous studies have shown that the drag effect increases when the carbon content is increased [36,39]. With the increase of carbon content in steel, the drag effect of the Cottrell atmosphere is greatly enhanced. Therefore, slat width becomes the EGS of strength in high carbon steels.
Conclusions
Effect of tempering temperature on the low-temperature impact strength of 42CrMo4-V steel. Metals Open Access Metall. Effect of block size on lath martensite strength in low carbon steels. Mater. Effect of quenching temperature on multi-level martensite microstructures and properties of strength and toughness in 20CrNi2Mo steel. Mater.
Interphase precipitation of nanometer-sized carbides in a titanium-molybdenum-bearing low-carbon steel. Acta Mater. Effect of hardening temperature on the microstructure and mechanical properties of mooring chain steel. Mater. Effects of austenitizing temperature on microstructure and mechanical properties of a 4-GPa-grade PM high-speed steel. Mater.
Microstructural Stability and Softening Resistance of a Novel Hot-Work Die Steel
Materials and Experiments 1. Materials and Heat Treatment
In terms of the H13 steel, the general heat treatment parameters were used as shown in Figure 2b. The effect of different austenitizing temperatures on (a) Charpy V-notch impact energy;. b) Charpy unnotched impact energy and (c) hardness; and (d) hardness value as a function of the tempering temperatures of the 5Cr5Mo2 steel with an austenitizing temperature of 1050◦C. Samples with dimensions of 12 mm×12 mm×10 mm before the pre-treatment were prepared by means of tempering, the duration of which varied between the samples to investigate the influence of the tempering duration.
The microstructural characteristics of the 5Cr5Mo2 and H13 samples were investigated using scanning electron microscopy (Inspect F50, FEI) and transmission electron microscopy (TEM) (Tecnai Spirit TEM T12, FEI and Tecnai G2 20, FEI). Second phase particles of the two steel alloys were characterized by electrolytic extraction in a 10 vol.% hydrochloric acid/methanol solution. The microstrain and the full width at half-maximum intensity (FWHM) of the selected peaks were determined by the Williamson-Hall method [20].
Results and Discussion 1. Alloy Design
The hardness of the 5Cr5Mo2 and H13 steel alloys as a function of the tempering duration is shown in Figure 5. However, the hardness of the H13 steel decreases faster than that of the 5Cr5Mo2 steel. In comparison, the softening rate of H13 steel is faster than that of 5Cr5Mo2 steel.
The softening of the 5Cr5Mo2 and H13 steel alloys during tempering is strongly linked to their microstructure. The softening curves of the two steel alloys given by Equation (8) and Equation (9) are shown in Figure 12b. However, the initial characteristics of the martensite morphology were more pronounced in the 5Cr5Mo2 steel.
Simulation and Prediction of the Vickers Hardness of AZ91 Magnesium Alloy Using Artificial Neural
Network Model
Experimental Procedures
Block samples with dimensions of 15 mm×15 mm×10 mm of the studied alloy were designed for hardness measurements and microstructure characterization. Temperature changes during the heat treatment were recorded by a 0.8 mm diameter chromel-alumel (type K) thermocouple, which was connected to a computer-based acquisition system. Later, all samples were immersed in water at 25◦C (room temperature) in order to quench and preserve the supersaturated solid solution.
To reveal the microstructure of the investigated samples, they were ground and polished according to the usual magnesium alloy procedures [31]. Then, the prepared specimens were etched for about 90 s in a nitric acid solution (4 mL HNO3 and 96 mL C2H5OH). The samples were then cleaned with anhydrous ethyl alcohol and then air-blast dried.
Artificial Neural Network (ANN)
A Shimadzu D6000 X-ray diffractometer (Shimadzu Corporation, Tokyo, Japan) with Cu-α radiation operating at 30 kV and 30 mA (wavelength of 0.15406 nm) was used to distinguish the formed phases. The age hardening response was investigated using a Vickers hardness testing machine under a load of 0.5 kg for 10 s at room temperature. Both the back propagation error (BPE) and the training data, in the training algorithm, are fed to the networks by adjusting the weights and biases until the expected values for the network are consistent with the actual values.
This adjustment is made with the BPE training algorithm by the comparison between the actual value tij and the expected values aij of the network by calculating the total sum of squared error (SSE) for the n data of the training dataset. In this study, the multilayer perceptron-based ANN model was used to calculate Vickers hardness values. The ANN model is configured to have aging temperatures and aging times as inputs and Vickers hardness values as outputs, as shown in Figure 3.
Results and Discussion
As the aging duration increased up to 192 h, the interrupted precipitation colonies of the β(Mg17Al12) phase (dark contrast of the second phase) started to form within the primary α-Mg matrix (light background) (Figure 5b). Unlike the microstructure of the sample aged for 92 h (Figure 7c), many lattice-shaped precipitates occupied the entire sample and no discontinuous precipitates were detected (Figure 7d). It is clearly seen that the trend of the lattice parameter, a, was contrary to the behavior of thec/aratio (Figure 9c) for samples aged at 150, 200 and 250◦C.
On the other hand, their values were approximately constant with the variation of the aging time at 100 and 300◦C. Figure 9b explains the variations of the lattice parameter, c, with aging time at different aging temperatures. The performance of the ANN model was validated by comparing the predicted values at 300◦C with the measured experimental data.
Mathematical Modeling and Analysis of Tribological Properties of AA6063 Aluminum Alloy Reinforced
Surface Methodology
Experimental 1. Composites Fabrication
In the present study FA was observed to be a significant contributor to component wear performance. The multiple regression technique used to develop the mathematical model to formulate the influence of parameters on both responses was achieved through design of experiments analysis software, Design-Expert V10.0. The highlighted results (Appendix A), which represent 20% of the total data, were not used in the model building process.
The remainder of the data was entered into a full factorial design using response surface method with user-defined choice. The software will then automatically evaluate all possible combinations of the factors and produce results for the significant and alias model terms. In summary, the methods of the present work include a quantitative (wear test) and statistical analyzes (randomized RSM approach), whereby the statistical method provides a way to assess the effect of all three factors, namely the fly shaft content (FA), load (L) and sliding speed (S) on the wear characteristics with a quadratic design model.
![Table 1. Chemical composition of tested AA6063 in wt% [16].](https://thumb-ap.123doks.com/thumbv2/1libvncom/9201061.0/80.723.99.630.140.178/table-chemical-composition-tested-aa-wt.webp)
Results and Discussion 1. Statistical Analyses
The essential effects of the parameters on the responses, i.e., wear rate and friction coefficient, are further illustrated using contour lines and 3D surface plots. Figure 2 further presents the perturbation plot of the wear rate data on the average experimental values of the process parameters. The quadratic curves describing the influence of the parameters on the consumption rate were attributed to the quadratic terms in the model.
Figure 3a shows a contour plot of the effect of S and FA on the wear rate Wr. Similarly, the previous analyzes described in Section 3.2 were repeated for the case of the friction coefficient response. The effect of the parameters on the response is further illustrated by contour and 3D surface plots.
Hot Deformation Behavior and Microstructure Characterization of an Al-Cu-Li-Mg-Ag Alloy
Results and Discussion 1. Original Microstructure
Figure 1 shows the EBSD orientation map of the Al-Cu-Li-Mg-Ag alloy after homogenization. Therefore, the microstructures of the samples compressed in different domains of the processing map, i.e., the safe domains with high power dissipation (domain A) and low power dissipation (domain B) and the unstable domain (domain C) will be discussed (Figure 6b). Figure 7 shows the Zener-Hollomon parameter of the deformed Al-Cu-Li-Mg-Ag alloy under different deformation conditions with a true strain of 0.8.
The microstructures show a typical deformed and repaired structure, i.e. the grains extend perpendicular to the direction of compression and many subgrain boundaries are formed within the grains. Figure 9 shows the EBSD morphology of the Al-Cu-Li-Mg-Ag alloy, deformed at 0.01 s−1 and different temperatures. When the deformation temperature rises to 470◦C (lnZ=21.1), the enhancement of grain boundary mobility favors nuclei and the growth of recrystallized grains around the original grain boundaries.
Effect of Equal Channel Angular Pressing on Microstructure and Mechanical Properties of a
The mechanism of the inhomogeneity phenomenon in the alloy during the ECAP process was discussed. The hardness of the hot-rolled Cu-Mg alloy was very low (75 ± 1 HV) but the hardness distribution was homogeneous (Figure 2a). Figure 6 shows the microstructure information in the central and lower regions of the longitudinal section of the ECAPed Cu-Mg alloy.
Calculated and experimental [15] yield strengths in the central and lower regions of the ECAPed Cu-Mg alloy. This also leads to the overestimation of the grain boundary strengthening and the underestimation of the dislocation strengthening. Figure 8 shows the relationship between the calculated yield strength and the measured hardness in the central and lower regions of the ECAPed Cu-Mg alloy.
MDPI
