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Commercial pure Mg specimens were processed through equal channel angular pressing (ECAP) using two dies with die angles of 90° and 120°. Mg billets were processed up to four passes via different route types. Machine learning (ML) techniques were adopted to estimate the ECAP parameters and verify the experimental findings. Several ML techniques were employed to estimate the effect ECAP parameters of pure Mg on microstructural evolution, Vicker's microhardness (HV), and tensile properties for ECAP billets and their as-annealed (AA) counterparts. Electron back-scatter diffraction (EBSD) was applied to determine the structural evolution and crystallographic texture both prior to and following the ECAP process for the Mg billets. EBSD analysis showed that route Bc is the most effective route in grain refinement, and four passes of route Bc experienced a significant refinement of 86% compared to the AA condition. Furthermore, the crystallographic texture showed that four passes of route Bc produced the most robust texture that was greater than 26.21 times random. ML findings revealed that the grain size demonstrated a strong correlation of −0.67 with rising number of passes, while ϕ affected the grain size strongly with 0.83. When adopting a 90°-die to accumulate the plastic strain up to 4Bc, the subsequent HV was indeed 111% higher than that of the AA equivalent. From ML findings it was clear that the number of passes was the most significant parameter on the Mg HV values, while ECAP channel angle (ϕ) revealed high correlation factor with HV values as well. Furthermore, four passes of route Bc with ϕ = 90° and 120° led to a significant increase of the tensile strength by 44.7%% and 35.7%, respectively, compared to the AA counterpart. ML findings revealed that the tensile strength was affected by the increasing number of passes with a strong correlation of 0.81, while affecting ductility moderately with 0.47. [ABSTRACT FROM AUTHOR]

Several physics-based models have been utilized in material design for the simulation and prediction of material properties. In this study, several machine-learning (ML) approaches were used to construct a prediction model to analyze the influence of equal-channel angular pressing (ECAP) parameters on the microstructural, corrosion and mechanical behavior of the biodegradable magnesium alloy ZK30. The ML approaches employed were linear regression, the Gaussian process, and support vector regression. For the optimization of the alloy's performance, experiments were conducted on ZK30 billets using different ECAP routes, channel angles, and number of passes. The adopted ML model is an adequate predictive model which agreed with the experimental results. ECAP die angles had an insignificant effect on grain refinement, compared to the route type. ECAP via four passes of route Bc (rotating the sample 90° on its longitudinal axis after each pass in the same direction) was the most effective condition producing homogenous ultrafine grain distribution of 1.92 µm. Processing via 4-Bc and 90° die angle produced the highest hardness (97-HV) coupled with the highest tensile strength (344 MPa). The optimum corrosion rate of 0.140 mils penetration per year (mpy) and the optimum corrosion resistance of 1101 Ω·cm2 resulted from processing through 1-pass using the 120°-die. Grain refinement resulted in reducing the corrosion rates and increased corrosion resistance, which agreed with the ML findings. [ABSTRACT FROM AUTHOR]

Copper and its related alloys are frequently adopted in contemporary industry due to their outstanding properties, which include mechanical, electrical, and electronic applications. Equal channel angular pressing (ECAP) is a novel method for producing ultrafine-grained or nanomaterials. Modeling material design processes provides exceptionally efficient techniques for minimizing the efforts and time spent on experimental work to manufacture Cu or its associated alloys through the ECAP process. Although there have been various physical-based models, they are frequently coupled with several restrictions and still require significant time and effort to calibrate and enhance their accuracies. Machine learning (ML) techniques that rely primarily on data-driven models are a viable alternative modeling approach that has recently achieved breakthrough achievements. Several ML algorithms were used in the modeling training and testing phases of this work to imitate the influence of ECAP processing parameters on the mechanical and electrical characteristics of pure Cu, including the number of passes (N), ECAP die angle (φ), processing temperature, and route type. Several experiments were conducted on pure commercial Cu while altering the ECAP processing parameters settings. Linear regression, regression trees, ensembles of regression trees, the Gaussian process, support vector regression, and artificial neural networks are the ML algorithms used in this study. Model predictive performance was assessed using metrics such as root-mean-squared errors and R2 scores. The methodologies presented here demonstrated that they could be effectively used to reduce experimental effort and time by reducing the number of experiments runs required to optimize the material attributes aimed at modeling the ECAP conditions for the following performance characteristics: impact toughness (IT), electrical conductivity (EC), hardness, and tensile characteristics of yield strength (σy), ultimate tensile strength (σu), and ductility (Du) [ABSTRACT FROM AUTHOR]

In this study, billets of the ZK30 (Mg-3Zn-0.6 Zr-0.4 Mn, wt%) alloy were Equal Channel Angle Pressing (ECAP) processed for up to four passes of routes Bc (with rotating the sample 90° in the same direction between the subsequent passes), A (without sample rotation), and C (with sample rotating 180°) after each pass at a temperature of 250 °C and a ram speed of 10 mm/min using a die with an internal channel angle of 90°. The microstructural evolution and the crystallographic texture were investigated using a Scanning Electron Microscope (SEM) equipped with the Electron Back-Scatter Diffraction (EBSD) technique. Corrosion measurements were conducted in ringer lactate which is a simulated body fluid. The Vickers microhardness test and tensile tests were conducted for the alloy before and after processing. The as-annealed billets exhibited a bimodal structure as fine grains (more than 3.39 µm) coexisted with almost-equiaxed coarse grains (less than 76.73 µm); the average grain size was 26.69 µm. Further processing until four passes resulted in enhanced grain refinement and full Dynamic Recrystallization (DRX). ECAP processing through 4-Bc, 4-A, and 4-C exhibited significant reductions in grain size until they reached 1.94 µm, 2.89 µm, and 2.25 µm, respectively. Four-pass processing also resulted in the transformation of low-angle grain boundaries into high-angle grain boundaries. The previous conclusion was drawn from observing the simultaneous decrease in the fraction of low-angle grain boundaries and an increase in the fraction of high-angle grain boundaries. The pole figures revealed that 4-Bc, 4-A, and 4-C reduced the maximum texture intensity of the as-annealed billets. The potentiodynamic polarization findings revealed that route Bc is the most effective route in improving the corrosion rate, whereas the Electrochemical Impedance Spectroscopy (EIS) revealed that routes A and Bc improved the corrosion resistance with nearly identical values. Finally, 4-Bc resulted in the highest increase in Vickers hardness, yield stress, and ultimate tensile strength with values of 80.8%, 19.3%, and 44.5%, alongside a 31% improvement in ductility, all compared to the AA condition. [ABSTRACT FROM AUTHOR]

In this study, a quantitative evaluation approach was used to investigate how certain ECAP processing parameters affect the microstructural evolution, Vicker's microhardness values and tensile properties of pure Mg. The ECAP processing parameters were number of passes, ECAP die channel angle and processing route type. The response surface methodology (RSM) technique was used to design 16 runs of the experiment using Stat-Ease design expert software. Billets of pure Mg were processed up to four passes of routes Bc, A and C at 225 °C. Two ECAP dies were used with internal channel angles of 90° and 120°. Experimental findings were used to establish empirical models to assess the influence of the ECAP processing parameters on grain size and mechanical properties of ECAPed billets. The established relationships were examined and validated for their adequacy and significance using ANOVA as well as several statistical criteria. Response surface plots and contour graphs were established to offer better understanding of the intended relationships. In addition, the optimum processing parameters for grain size, hardness values and tensile properties were defined. Both experimental results and the theoretical model revealed that route Bc is the most effective route in grain refining. The experimental findings showed that four passes of route Bc through the die channel angle 90° revealed a significant reduction in the grain size by 86% compared to the as-annealed counterparts. Similar to the grain size refining, four-passes processing through the ECAP die with an internal channel angle of 90° leads to improved Vicker's microhardness values. Additionally, four passes of route Bc using the 90° die angle recorded a significant HV increase at the edge and central areas by 112% and 78%, respectively, compared to the as-annealed counterpart. On the other hand, according to the optimization findings, two passes of route Bc using a die angle of 120° resulted in the best ultimate tensile strength for pure Mg, whereas four passes of route Bc revealed the optimum ductility at fracture. [ABSTRACT FROM AUTHOR]