Strength–Ductility Synergy of Lightweight High Entropy Alloys DOI Creative Commons
Fainah Madewu, N. Malatji, Mxolisi Brendon Shongwe

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(3)

Published: March 1, 2025

ABSTRACT Lightweight high entropy alloys (LWHEAs) are a unique class of materials that combine numerous principal elements such as Al, Mg, and Ti, in equiatomic or near‐equiatomic ratios. These suitable for high‐performance applications the aerospace, automotive, defense industries due to their exceptional balance lightweight, strength, superior ductility. The biggest obstacle development LWHEAs is attain strength–ductility synergy. mechanical performance these influenced by intricate interactions between solid‐solution strengthening, lattice distortion, phase stability mechanisms, well deformation processes like transformation‐induced plasticity (TRIP) twinning‐induced (TWIP). There remains critical knowledge gap regarding how process parameters processing methods influence properties microstructure, which key factors determining synergy LWHEAs. This study evaluated figured out strength ductility can be enhanced optimizing microstructure through customized alloying heat treatments. Various strategies, including introduction coherent precipitates, hierarchical structures, grain refinement have also demonstrated usefulness enhancing performance. article presented detailed review recent progress attainment

Language: Английский

A review of strategies to control process-induced cracks in metal additive manufacturing and remanufacturing DOI
Xiaoting Hong, Tao Liu, Junjie Zhang

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 111801 - 111801

Published: Feb. 1, 2025

Language: Английский

Citations

1
Multi-objective optimization in machine learning assisted materials design and discovery DOI Open Access
Pengcheng Xu, Yingying Ma, Wencong Lu

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(2)

Published: March 24, 2025

Over the past decades, machine learning has kept playing an important role in materials design and discovery. In practical applications, usually need to fulfill requirements of multiple target properties. Therefore, multi-objective optimization based on become one most promising directions. This review aims provide a detailed discussion learning-assisted discovery combined with recent research progress. First, we briefly introduce workflow learning. Then, Pareto fronts corresponding algorithms are summarized. Next, strategies demonstrated, including front-based strategy, scalarization function, constraint method. Subsequently, progress is summarized different discussed. Finally, propose future directions for learning-based materials.

Language: Английский

Citations

1
Lattice-inspired NiTi-based metamaterials with widely tunable mechanical-superelastic synergy DOI Creative Commons
Zhi Zhang, Jianbao Gao, Shuaishuai Wei

et al.

Virtual and Physical Prototyping, Journal Year: 2025, Volume and Issue: 20(1)

Published: Jan. 5, 2025

Inspired by austenite and martensite crystal lattices in the NiTi microstructure with versatile performances, bionic microlattice metamaterials strut diameter from 0.4∼0.8 mm were constructed prepared laser powder bed fusion for expanding tailored mechanical-superelastic range, machine learning was utilized mapping relationship of various parameters. The highly related to orientation, martensite-inspired metamaterial x-axis loading direction (M-x) possessed higher mechanical properties than that z-axis (M-z). For properties, M-x highest Young's modulus (E=1001.5∼3720.4 MPa) simultaneously widest range (87.32%), while austenite-inspired microlattices (A) exhibited a fully ability yield strength (σ). superelastic, austenite- had superior superelasticity (98.10%∼99.36% recoverability) wide volume tuning space, recoverability narrow range. relation between different parameters superelastic established through learning, multiple performance optimizations carried out vascular stents as typical application objectives. This research provides novel ideas designing components, contributing future developments applications.

Language: Английский

Citations

0
Screening the TiZrHfNbVMoTa refractory high-entropy alloys with multi-property constraints DOI

Ruixia Sun,

Haiqing Yin, Jie Liu

et al.

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 179284 - 179284

Published: Feb. 1, 2025

Language: Английский

Citations

0
An overview of high‐throughput synthesis for advanced high‐entropy alloys DOI Creative Commons

Tongbin Xie,

Weidong Li, Gihan Velişa

et al.

Materials Genome Engineering Advances, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Abstract High‐entropy alloys (HEAs) have revolutionized alloy design by integrating multiple principal elements in equimolar or near‐equimolar ratios to form solid solutions, vastly expanding the compositional space beyond traditional based on a primary element. However, immense complexity presents significant challenges designing with targeted properties, as billions of new systems emerge. High‐throughput approaches, which allow parallel execution numerous experiments, are essential for accelerated HEA navigate this extensive and fully exploit their potential. Here, we reviewed how advancements high‐throughput synthesis tools database development. We also discussed advantages limitations each fabrication methodology, understanding these is vital achieving precise design.

Language: Английский

Citations

0
Accelerated design of a novel wide thermal hysteresis NiTi-based shape memory alloy based on interpretable information machine learning DOI
Xiaohua Tian,

Yulin Pan,

Jian Li

et al.

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 179334 - 179334

Published: Feb. 1, 2025

Language: Английский

Citations

0
Active learning-based alloy design strategy for improving the strength/ductility balance of Al-Mg-Zn alloys DOI Creative Commons
Mo Wang, Yao Xiao,

Yushen Huang

et al.

Materials & Design, Journal Year: 2025, Volume and Issue: unknown, P. 113772 - 113772

Published: Feb. 1, 2025

Language: Английский

Citations

0
On the work hardening behavior of machining WNbMoTaZrx (x = 0.5 and 1.0) refractory high entropy alloys DOI
Haidong Yang, Ruilong Sheng, Lin He

et al.

Journal of Materials Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

Citations

0
Strength–Ductility Synergy of Lightweight High Entropy Alloys DOI Creative Commons
Fainah Madewu, N. Malatji, Mxolisi Brendon Shongwe

et al.

Engineering Reports, Journal Year: 2025, Volume and Issue: 7(3)

Published: March 1, 2025

ABSTRACT Lightweight high entropy alloys (LWHEAs) are a unique class of materials that combine numerous principal elements such as Al, Mg, and Ti, in equiatomic or near‐equiatomic ratios. These suitable for high‐performance applications the aerospace, automotive, defense industries due to their exceptional balance lightweight, strength, superior ductility. The biggest obstacle development LWHEAs is attain strength–ductility synergy. mechanical performance these influenced by intricate interactions between solid‐solution strengthening, lattice distortion, phase stability mechanisms, well deformation processes like transformation‐induced plasticity (TRIP) twinning‐induced (TWIP). There remains critical knowledge gap regarding how process parameters processing methods influence properties microstructure, which key factors determining synergy LWHEAs. This study evaluated figured out strength ductility can be enhanced optimizing microstructure through customized alloying heat treatments. Various strategies, including introduction coherent precipitates, hierarchical structures, grain refinement have also demonstrated usefulness enhancing performance. article presented detailed review recent progress attainment

Language: Английский

Citations

0