Fracture toughness of Al?Zn?Mg?Cu alloys

Kudryashov, V. G.; Teleshov, V. V.; Rudnitskii, E. N.; Kopeliovich, B. A.; Vainblat, Yu. M.

doi:10.1007/bf01528750

“…Fig. 9 Comparison of ultimate tensile strength and elongation of high-strength aluminum alloy 6,[38][39][40][41][42][43][44][45][46][47][48][49][50] . CP is the conventional industrial production process, SD is the spray deposition process, PM is the powder metallurgy process, and SPD is the severe plastic deformation process.…”

Section: Data Preparationmentioning

confidence: 99%

A rapid and effective method for alloy materials design via sample data transfer machine learning

Jiang

¹

,

Zhang

²

,

Hu

³

et al. 2023

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One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials. Here, a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data. A new type of aluminum alloy (E2 alloy) with ultra strength and high toughness previously developed by the authors is used as an example. An optimal three-stage solution-aging treatment process (T66R) was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum alloys. It realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously, which is of great significance for lightweight of high-end equipment. Meanwhile, the microstructure analysis clarifies the mechanism of alloy performance improvement. This study shows that transferring the existing alloy data is an effective method to design new alloys.

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“…Fig. 9 Comparison of ultimate tensile strength and elongation of high-strength aluminum alloy [6,[40][41][42][43][44][45][46][47][48][49][50][51][52]. CP is the conventional industrial production process, SD is the spray deposition process, PM is the powder metallurgy process, and SPD is the severe plastic deformation process.…”

Section: Analysis Of the Mechanism Of The Improved Strength And Ducti...mentioning

confidence: 99%

A Novel Method for Alloy Materials Design via Sample Data Transfer Machine Learning

Xie

¹

,

Jiang

²

,

Zhang

³

et al. 2022

Preprint

0

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One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials. Here, a novel method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data. A new type of aluminum alloy with ultra strength and high toughness (so-called E2 alloy) previously developed by the authors is used as an example, and an optimal three-stage solution-aging treatment process (which is very difficult to design by trial-and-error method, T66R process for short) was rapidly designed based on 1053 pieces of heat treatment process data of AA7xxx series commercial high-strength aluminum alloy and a total of 29 groups of experimental data of E2 alloy. It overcomes the bottleneck of expensive theoretical calculations and more than millions of possible process combinations in the experimental trial-and-error space in the process design of complex alloy heat treatment. Compared with the common T6 process in the aluminum industry, the T66R process can increase the ultimate tensile strength and elongation of the E2 alloy simultaneously, from 715 ± 6 MPa and 8.4 ± 0.4% to 767 ± 6 MPa and 13.4 ± 0.5%, respectively. The results of microstructure characterization indicate that employing the optimal T66R process, the amount of micron-scale insoluble phases in the E2 alloy is sharply reduced, and the number of nano-precipitates is significantly increased, which leads to the simultaneous improvement of the strength and plasticity of the alloy. This study shows that transferring the existing alloy data is an effective method to design new alloys.

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“…The same phenomenon has been observed by other authors. In particular, the stepwise propagation of a fatigue crack with brittle zones formed at the values of the maximum stress intensity factor within a cycle ranging from 0.7 to 1.0 of K , was observed on 3% Si steel [28], 055 and 0.07% C steels [29] and on high-strength aluminium alloys [3,30,31].…”

Section: Fractographic Investigationmentioning

confidence: 99%

Cyclic Loading and Fracture Toughness of Steels

Troshchenko¹,

Prokopenko²,

Pokrovsky³

1979

Fatigue Fract Eng Mat Struct

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Abstract-The paper considers the effect of cyclic loading and loading rate upon fracture toughness characteristics of steels at room and low temperatures. It is shown that fracture toughness of a low-alloy ferrite-pearlite steel with 0.1% C (steel 1) and for 15G2AFDps steel of the same class (steel 2) are 2 to 2.5 times lower under cyclic loading (50 and 0-05 Hz) and dynamicloading (K = 1.5 x 1O6MPa Jm s-') than under static loading (K = 0.6 to 9MPaJm s -l ) . For quenched and low-tempered 45 steel at 293 K and for armco-iron at 77 K fracture toughness characteristics do not depend on the loading condition. Macro-and micro-fractographic investigations revealed a correlation between the plastic zone size and the length of brittle fracture areas which are formed in steels 1 and 2, and in armco-iron during unstable propagation of the fatigue crack. Dependence of the decrease of the critical stress intensity factor under cyclic loading on the number of load cycles are obtained for repeating (R = 0) and alternating bending (R = -1) of specimens with a crack. A model for the transition from stable to unstable crack propagation is proposed involving crack velocity in the zone ahead of the crack tip damaged by cyclic plastic deformation. A new approach is suggested to the classification of materials on the basis of the sensitivity of fracture toughness characteristics to cyclic conditions of loading.

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Fracture toughness of Al?Zn?Mg?Cu alloys

Cited by 4 publications

References 1 publication

A rapid and effective method for alloy materials design via sample data transfer machine learning

A rapid and effective method for alloy materials design via sample data transfer machine learning

A Novel Method for Alloy Materials Design via Sample Data Transfer Machine Learning

Cyclic Loading and Fracture Toughness of Steels

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