Evolution of Grain Refinement in AA5083 Sheet Metal Processed by ECAP

Illgen, Christian; Frint, Philipp; Gruber, Maximilian; Volk, Wolfram; Wagner, Martin F.‐X.

doi:10.1007/978-3-030-36408-3_52

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…In contrast to route Bc, processing through 6-C resulted in grains that were more coarsely equiaxed (Figure 6e). It is important to note that following route C processing even passes, the plastic strain from the earlier odd passes is reverted, leaving the equiaxed grains intact, which was consistent with previous research [51]. In addition, the Cu billet was processed via 6-Bc utilizing a 90-degree die at 200 • C to assess the processing temperature effect on the microstructure of the ECAP processed Cu, as presented in Figure 6f, where raising the processing temperature up to 200 • C had a substantial influence on grain size, with coarser equiaxed grains dominating the microstructure compared to the RT processed equivalents.…”

Section: Microstructural Evolutionsupporting

confidence: 90%

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

et al. 2022

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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)

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Section: Microstructural Evolutionsupporting

confidence: 90%

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

et al. 2022

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“…Accordingly, these areas of high dislocation densities improve the corrosion resistance of the Mg alloy as it forms protective hydroxide and oxide layers (Mg(OH) 2 and MgO) [ 5 , 21 , 24 , 77 ]. Furthermore, multiple-pass ECAP processing increases the thickness, stability, and coherency of the protective layer as well [ 49 , 77 ]. Figure 13 shows SEM micrographs of the AA and ECAPed ZK30 billets after corrosion tests.…”

Section: Resultsmentioning

confidence: 99%

“…Similar findings were reported by Sankuru et al [ 47 ] who reported that 4-Bc was the most effective route in improving the hardness values of pure Mg; however, no significant difference in hardness values was recorded among all ECAP processing routes. Illgen et al [ 49 ] reported that route Dc (rotating the sample 90° in the same direction along the ND between subsequent passes) was the most effective.…”

Section: Resultsmentioning

confidence: 99%

“…Amongst the mentioned routes, route Bc provides the most grain refinement [ 30 ], whereas route A produces the highest density of elongated grains [ 48 ]. However, applying route C an odd number of times results in elongated grains while applying it an even number of times results in matching strain profiles across the sample and produces equiaxed grains [ 49 ]. Different route types induce varying effects on the crystallographic texture and its intensity, which in turn affects the sample’s properties [ 50 ].…”

Section: Introductionmentioning

confidence: 99%

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Effect of ECAP Route Type on the Microstructural Evolution, Crystallographic Texture, Electrochemical Behavior and Mechanical Properties of ZK30 Biodegradable Magnesium Alloy

et al. 2022

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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.

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Thermomechanical Analysis and Experimental Validation of ECAP for Aluminum Sheet Metal

et al. 2021

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Evolution of Grain Refinement in AA5083 Sheet Metal Processed by ECAP

Cited by 10 publications

References 19 publications

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

Investigation of the Effect of ECAP Parameters on Hardness, Tensile Properties, Impact Toughness, and Electrical Conductivity of Pure Cu through Machine Learning Predictive Models

Effect of ECAP Route Type on the Microstructural Evolution, Crystallographic Texture, Electrochemical Behavior and Mechanical Properties of ZK30 Biodegradable Magnesium Alloy

Thermomechanical Analysis and Experimental Validation of ECAP for Aluminum Sheet Metal

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