Electrochemical surface smoothing of spark erosion treated Zr-based bulk metallic glasses in NaCl-ethylene glycol electrolyte

Bulk metallic glasses have application potential in engineering structures due to their exceptional strength and fracture toughness. Their fatigue resistance is very important for the application as well. We report the tension-tension fatigue damage behavior of a Zr61Ti2Cu25Al12 bulk metallic glass, which has the highest fracture toughness among BMGs. The Zr61Ti2Cu25Al12 glass exhibits a tension-tension fatigue endurance limit of 195 MPa, which is higher than that of high-toughness steels. The fracture morphology of the specimens depends on the applied stress amplitude. We found flocks of shear bands, which were perpendicular to the loading direction, on the surface of the fatigue test specimens with stress amplitude higher than the fatigue limit of the glass. The fatigue cracking of the glass initiated from a shear band in a shear band flock. Our work demonstrated that the Zr61Ti2Cu25Al12 glass is a competitive structural material and shed light on improving the fatigue resistance of bulk metallic glasses.

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Effects of Al replacement on glass forming ability and mechanical properties of Zr-based bulk metallic glasses

Journal of Non-Crystalline Solids ◽

10.1016/j.jnoncrysol.2021.120962 ◽

2021 ◽

Vol 568 ◽

pp. 120962

Author(s):

Y. Tan ◽

Y.W. Wang ◽

X.W. Cheng ◽

Q. Fu ◽

Z.H. Xin ◽

...

Keyword(s):

Mechanical Properties ◽

Metallic Glasses ◽

Bulk Metallic Glasses ◽

Glass Forming Ability ◽

Glass Forming

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Inverse Design of Fe-Based Bulk Metallic Glasses Using Machine Learning

Metals ◽

10.3390/met11050729 ◽

2021 ◽

Vol 11 (5) ◽

pp. 729

Author(s):

Junhyub Jeon ◽

Namhyuk Seo ◽

Hwi-Jun Kim ◽

Min-Ha Lee ◽

Hyun-Kyu Lim ◽

...

Keyword(s):

Thermal Properties ◽

Metallic Glasses ◽

Alloy Composition ◽

Relevant Literature ◽

Bulk Metallic Glasses ◽

X Ray Diffraction ◽

X Ray ◽

Ann Models ◽

Wide Scale ◽

Artificial Neural Network Ann

Fe-based bulk metallic glasses (BMGs) are a unique class of materials that are attracting attention in a wide variety of applications owing to their physical properties. Several studies have investigated and designed the relationships between alloy composition and thermal properties of BMGs using an artificial neural network (ANN). The limitation of the wide-scale use of these models is that the required composition is yet to be found despite numerous case studies. To address this issue, we trained an ANN to design Fe-based BMGs that predict the thermal properties. Models were trained using only the composition of the alloy as input and were created from a database of more than 150 experimental data of Fe-based BMGs from relevant literature. We adopted these ANN models to design BMGs with thermal properties to satisfy the intended purpose using particle swarm optimization. A melt spinner was employed to fabricate the designed alloys. X-ray diffraction and differential thermal analysis tests were used to evaluate the specimens.

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