Development of Ultrafine Grain IF Steel via Differential Speed Rolling Technique

The aim of this paper was to investigate the microstructural development and properties of interstitial free (IF) steel fabricated using the DSR (differential speed rolling) process. Severe plastic deformation of the DSR passes was imposed on the sample for up to four passes, leading to ~1.7 total strain with a speed ratio of 1:4 between the two rolls. Microstructural observation revealed that the equiaxed grain size of ~0.7 µm, including the formation of grain boundaries with a high angle of misorientation, was reached after four operations of DSR, which was attributed to the grain subdivision of severely elongated ferrite grain. Since the deformation mode of the DSR operation was dominated by severe shear deformation, the main shear texture of the bcc components appeared in all DSR operations in which the α-fiber of the {110} slip became a main component in accommodating the severe plastic deformation of the DSR process. The intensity of the shear texture, the {110} and {112} slip, increased by increasing the number of passes. Moreover, the γ-fiber of the -type planes was activated as a result of the alternation of the shear direction during sample rotation. The microhardness and room temperature tensile tests revealed that the strength of the IF steel improved as the amount of strain increased, and this was attributed to the grain refinement and texture characteristics of the samples after the DSR processing

Development of Ultrafine Grain IF Steel via Differential Speed Rolling Technique

The aim of this paper was to investigate the microstructural development and properties of interstitial free (IF) steel fabricated using the DSR (differential speed rolling) process. Severe plastic deformation of the DSR passes was imposed on the sample for up to four passes, leading to ~1.7 total strain with a speed ratio of 1:4 between the two rolls. Microstructural observation revealed that the equiaxed grain size of ~0.7 µm, including the formation of grain boundaries with a high angle of misorientation, was reached after four operations of DSR, which was attributed to the grain subdivision of severely elongated ferrite grain. Since the deformation mode of the DSR operation was dominated by severe shear deformation, the main shear texture of the bcc components appeared in all DSR operations in which the α-fiber of the {110} slip became a main component in accommodating the severe plastic deformation of the DSR process. The intensity of the shear texture, the {110} and {112} slip, increased by increasing the number of passes. Moreover, the γ-fiber of the <112>-type planes was activated as a result of the alternation of the shear direction during sample rotation. The microhardness and room temperature tensile tests revealed that the strength of the IF steel improved as the amount of strain increased, and this was attributed to the grain refinement and texture characteristics of the samples after the DSR processing

Annealing Behavior of 6061 Al Alloy Subjected to Differential Speed Rolling Deformation

This study investigated the effects of heat treatment (annealing) on the microstructure of ultrafine grained 6061 Al alloy samples fabricated by a differential speed rolling (DSR) process. The samples were fabricated using two passes DSR with 75% thickness reduction and a speed ratio of 1:4. The DSR-deformed 6061 Al alloy sample exhibited a lamellar boundary structure composed mostly of subgrains surrounded by low-angle grain boundaries. After annealing, the DSR-deformed 6061 Al alloy samples exhibited coarse grained structure and transformed from lamellar to equiaxed, where both the grain size and grain shape aspect ratio increased with increasing annealing temperature. The fraction of grain boundaries with high misorientation angles increased progressively during annealing, to ~77% at annealing temperature of 350 °C

A Short Review on the Machine Learning-Guided Oxygen Uptake Prediction for Sport Science Applications

In recent years, the rapid improvement in computing facilities combined with that achieved in algorithms and the immense amount of available data led to a great interest in machine learning (ML), which is a subset of artificial intelligence. Nowadays, the ML technique is used mostly in all applications for various purposes, whereby ML will be possible to learn from data, predict, identify patterns, and make decisions. In this regard, the ML was successfully used to predict the oxygen uptake during physical activity without the need for complicated procedures used in the direct measurement. Accordingly, in the present work, the state-of-art and recent advances related to the oxygen uptake prediction using ML were presented. Various exercise and non-exercise predictive models also were discussed

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations

Interpretable Machine Learning Analysis of Stress Concentration in Magnesium: An Insight beyond the Black Box of Predictive Modeling

In the present work, machine learning (ML) was employed to build a model, and through it, the microstructural features (parameters) affecting the stress concentration (SC) during plastic deformation of magnesium (Mg)-based materials are determined. As a descriptor for the SC, the kernel average misorientation (KAM) was used, and starting from the microstructural features of pure Mg and AZ31 Mg alloy, as recorded using electron backscattered diffraction (EBSD), the ML model was trained and constructed using various types of ML algorithms, including Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Naive Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Extremely Randomized Trees (ERT). The results show that the accuracy of the ERT-based model was higher compared to other models, and accordingly, the nine most-important features in the ERT-based model, those with a Gini impurity higher than 0.025, were extracted. The feature importance showed that the grain size is the most effective microstructural parameter for controlling the SC in Mg-based materials, and according to the relative Accumulated Local Effects (ALE) plot, calculated to show the relationship between KAM and grain size, it was found that SC occurs with a lower probability in the fine range of grain size. All findings from the ML-based model built in the present work were experimentally confirmed through EBSD observations

Brittle and ductile characteristics of intermetallic compounds in magnesium alloys: A large-scale screening guided by machine learning

In the present work, we have employed machine learning (ML) techniques to evaluate ductile-brittle (DB) behaviors in intermetallic compounds (IMCs) which can form magnesium (Mg) alloys. This procedure was mainly conducted by a proxy-based method, where the ratio of shear (G)/bulk (B) moduli was used as a proxy to identify whether the compound is ductile or brittle. Starting from compounds information (composition and crystal structure) and their moduli, as found in open databases (AFLOW), ML-based models were built, and those models were used to predict the moduli in other compounds, and accordingly, to foresee the ductile-brittle behaviors of these new compounds. The results reached in the present work showed that the built models can effectively catch the elastic moduli of new compounds. This was confirmed through moduli calculations done by density functional theory (DFT) on some compounds, where the DFT calculations were consistent with the ML prediction. A further confirmation on the reliability of the built ML models was considered through relating between the DB behavior in MgBe13 and MgPd2, as evaluated by the ML-predicted moduli, and the nature of chemical bonding in these two compounds, which in turn, was investigated by the charge density distribution (CDD) and electron localization function (ELF) obtained by DFT methodology. The ML-evaluated DB behaviors of the two compounds was also consistent with the DFT calculations of CDD and ELF. These findings and confirmations gave legitimacy to the built model to be employed in further prediction processes. Indeed, as examples, the DB characteristics were investigated in IMCs that might from in three Mg alloy series, involving AZ, ZX and WE

Investigating the Microstructure, Crystallographic Texture and Mechanical Behavior of Hot-Rolled Pure Mg and Mg-2Al-1Zn-1Ca Alloy

In this study, the microstructure, crystallographic texture and the mechanical performance of hot-rolled pure Mg and Mg-2Al-1Zn-1Ca (herein inferred as AZX211) were thoroughly investigated. The results showed that the designed AZX211 alloy exhibited an exceptional strength/ductility synergy where an almost 40% increase in ductility was received for AZX211. The microstructural characterization revealed the grain refinement in the AZX211, where the grain size was reduced by more than 50% (24.5 µm, 10 µm for the pure Mg and the AZX211, respectively). Moreover, a discernible number of precipitates were dispersed in the AZX211, which were confirmed to be (Mg, Al)2Ca. The pure Mg showed a conventional strong basal texture while a significantly weakened split basal texture was received for the AZX211. The fraction of basal-oriented grains was 21% for the pure Mg and 5% for the AZX211. The significant texture weakening for the AZX211 can be attributed to the precipitation and co-segregation that triggered the preferential evolution of the non-basal grains while impeding the growth of the basal grains. This was also confirmed by the crystal orientation and the pseudo-rocking curves. The higher ductility of the AZX211 was explained based on the texture softening and Schmid factor for the basal and non-basal slip systems

A Further Improvement in the Room-Temperature Formability of Magnesium Alloy Sheets by Pre-Stretching

Pre-stretching experiments were carried out on AZ31–0.5Ca magnesium alloy to alter the microstructure and texture for enhancing room-temperature formability. Compared to as-received alloy, the formability of a 5%-stretched sample was improved by 15%. This was attributed to enhanced strain hardening capability related to the weakening of basal texture and less homogeneous microstructure. In addition, in-grain misorientation axis analysis performed on the samples (as-received and stretched) also confirmed the higher activity of the non-basal slip systems in the 5%-stretched sample