The effect of the shape and size of initial flaws on crack propagation in uniaxially compressed linear brittle materials

Iskander, Mina; Shrive, Nigel G.

doi:10.1016/j.tafmec.2020.102742

Cited by 10 publications

(1 citation statement)

References 23 publications

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“…Vekinis et al investigated alumina with defined distributed flaws [20], Wang et al focused on the fraction theory behind and considered different models and fracture criteria [21]. Still today the question of how cracks propagate in uniaxially compressed linear brittle materials moves researchers to investigate this phenomenon [22]. Crack initiation starts not only at one specific point within the sample but within the sample volume at faulty or weak points or otherwise generated local stress increases.…”

Section: Crack Initiation and Growth Theorymentioning

confidence: 99%

2D and 3D in-situ mechanical testing of an interpenetrating metal ceramic composite consisting of a slurry-based ceramic foam and AlSi10Mg

Schukraft

Lohr

Weidenmann

2021

Composite Structures

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Section: Crack Initiation and Growth Theorymentioning

confidence: 99%

2D and 3D in-situ mechanical testing of an interpenetrating metal ceramic composite consisting of a slurry-based ceramic foam and AlSi10Mg

Schukraft

Lohr

Weidenmann

2021

Composite Structures

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The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel

Li,

Wang

et al. 2024

Materials Genome Engineering Advances

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Traditional alloy design typically relies on a trial‐and‐error approach, which is both time‐consuming and expensive. Whilst physical metallurgical (PM) models offer some predictive capabilities, their reliability is limited by errors accumulating across space scales. To address this, this study proposes a novel framework that combines PM knowledge graphs (PMKGs) with graph neural networks (GNNs) to predict the tensile properties of quenching and partitioning steels, using genetic algorithms for dual‐objective optimization. Compared to traditional artificial intelligence (AI) models, this framework shows significant advantages in predicting ultimate tensile strength (UTS) and total elongation (TEL) with higher accuracy and stability. Notably, the R2 for TEL prediction improved by approximately 15%. Furthermore, this framework successfully balances UTS and TEL, resulting in the design of alloys with superior overall properties. The designed alloys, with a composition of approximately 0.3 wt.% C, 3 wt.% Mn, 1.2 wt.% Si, and minor amounts of Cr and Al, achieve a UTS exceeding 1500 MPa and TEL near 20%, aligning with PM principles and validating the rationality and feasibility of this method. This study offers new insights into applying AI in complex multi‐objective alloy design, highlighting the potential of integrating expert knowledge with GNNs.

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On a Universal Failure Criterion for Brittle Materials

Ahmed,

Iskander,

Shrive

2024

Lecture Notes in Civil Engineering

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The effect of the shape and size of initial flaws on crack propagation in uniaxially compressed linear brittle materials

Cited by 10 publications

References 23 publications

2D and 3D in-situ mechanical testing of an interpenetrating metal ceramic composite consisting of a slurry-based ceramic foam and AlSi10Mg

2D and 3D in-situ mechanical testing of an interpenetrating metal ceramic composite consisting of a slurry-based ceramic foam and AlSi10Mg

The role of physical metallurgical relationships in enhancing alloy properties prediction and design: A case study on Q&P steel

On a Universal Failure Criterion for Brittle Materials

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