Aging temperature role on precipitation hardening in a non-equiatomic AlCoCrFeNiTi high-entropy alloy

Nandal, Vickey; Hariharan, K.; Sarvesha, R.; Singh, Sudhanshu; Huang, E-Wen; Chang, Young-Il; Yeh, An‐Chou; Neelakantan, Suresh; Jain, Jayant

doi:10.1080/02670836.2021.1996104

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…The high-temperature mechanical properties of Ni-based superalloys are nally controlled by the aging heat treatment, which determines the two-phase microstructure consisting of γ' precipitate and γ matrix [1][2][3] in the system. Currently, the aging treatment of alloys is carried out through conventional isothermal methods 4,5 . However, when examining different alloy systems such as Fe-Cu 6 , 2A14 Al 7 Al-Zn-Mg-Cu 8-10 , Al-Cu-Mg-Si 11 , Al-Zn-Mg 12 and Mg-4Y-2.5Nd-1.2Gd-0.5Zr 13 in the literature, it has been observed that non-isothermal aging (NIA) leads to enhanced mechanical properties compared to the conventional isothermal aging methods.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Aging Pattern Analysis for Improving 0.2% Proof Stress in Binary Ni-Al Alloys with γ – γ' Two-Phase Structure

Nandal,

Dieb,

Bulgarevich

et al. 2024

Preprint

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This study presents the comprehensive analysis of flexible non-isothermal aging (NIA) patterns discovered through artificial intelligence (AI) to improve the mechanical strength (0.2% proof stress) in γ – γ' two-phase, binary Ni-Al alloys. In our recent investigation, we found that the AI algorithm could propose aging patterns with superior strength compared to conventional isothermal aging. In this current study, we continued our extensive exploration of AI methodologies, uncovering diverse patterns that also surpassed the isothermal aging benchmark. Remarkably, out of 2823 NIA schedules, we found 173 ones outperforming the isothermal aging benchmark. Furthermore, we conducted a detailed analysis of newly AI-discovered patterns. We identified two critical factors for strength improvement: exposure at 700 ℃ and the number of consecutive 700 ℃ exposures (optimally set at two), alongside non-consecutive steps (up to five). The insights gained from these findings may demonstrate the potential of AI-driven approaches to yield ideas on how to achieve improved strength in Ni-Al alloys.

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Section: Introductionmentioning

confidence: 99%

AI-Driven Aging Pattern Analysis for Improving 0.2% Proof Stress in Binary Ni-Al Alloys with γ – γ' Two-Phase Structure

Nandal,

Dieb,

Bulgarevich

et al. 2024

Preprint

Self Cite

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“…Another critical reason is that it has been exceedingly challenging to investigate a large number of different types of aging treatment scheduling. Obviously, such optimization is time-consuming and costly with traditional methods due to aging treatment multidimensionality 1 , 12 , 37 , 38 . As a result, it is challenging to determine whether the best solution has been found experimentally.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys

Nandal

Dieb

Bulgarevich

et al. 2023

Sci Rep

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In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardening of Ni-based alloy to predict more flexible non-isothermal aging (NIA) and to examine the possible routes for the enhancement in strength that may be practically achieved. Additionally, AI is used to integrate with Materials Integration by Network Technology, which is a computational workflow utilized to model the microstructure evolution and evaluate the 0.2% proof stress for isothermal aging and NIA. As a result, it is possible to find enhanced 0.2% proof stress for NIA for a fixed time of 10 min compared to the isothermal aging benchmark. The entire search space for aging scheduling was ~ 3 billion. Out of 1620 NIA schedules, we succeeded in designing the 110 NIA schedules that outperformed the isothermal aging benchmark. Interestingly, it is found that early-stage high-temperature aging for a shorter time increases the γ′ precipitate size up to the critical size and later aging at lower temperature increases the γ′ fraction with no anomalous change in γ′ size. Therefore, employing this essence from AI, we designed an optimum aging route in which we attained an outperformed 0.2% proof stress to AI-designed NIA routes.

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“…Another critical reason is that it has been exceedingly challenging to investigate a large number of candidates' HT scheduling. Obviously, such optimization is time-consuming and costly with traditional methods due to HT multidimensionality 2,26,28,29 . As a result, it is challenging to determine whether the best solution has been found experimentally.…”

mentioning

confidence: 99%

Artificial Intelligence Inspired Design of Non-Isothermal Heat Treatments for γ - γ' Two-phase Ni-based Alloys

Nandal

Dieb

Bulgarevich

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardenable Ni-based alloy to predict more flexible non-isothermal heat treatment and to examine the possible heat treatment routes for the enhancement in strength that may be practically achieved. Additionally, AI is used to integrate with Materials Integration by Network Technology, which is a computational workflow utilized to model the microstructure evolution and evaluate the 0.2% proof stress for isothermal heat treatment (IHT) and non-isothermal heat treatment (non-IHT). As a result, it is possible to find enhanced 0.2% proof stress for non-IHTs for a fixed time of 10 minutes compared to the IHT benchmark. The entire search space for heat treatment scheduling was ~ 3 billion. Out of 1620 non-IHTs, we succeeded in designing the 110 non-IHTs schedules that outperformed the IHT benchmark. Interestingly, it is found that early-stage high-temperature for a shorter time increases the γ' precipitate size up to the critical size and later heat treatment at lower temperature increases the γ' fraction with no anomalous change in γ' size. Therefore, employing this essence from AI, we designed a heat treatment route in which we attained an outperformed 0.2% proof stress to AI-designed non-IHT routes.

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Aging temperature role on precipitation hardening in a non-equiatomic AlCoCrFeNiTi high-entropy alloy

Cited by 9 publications

References 24 publications

AI-Driven Aging Pattern Analysis for Improving 0.2% Proof Stress in Binary Ni-Al Alloys with γ – γ' Two-Phase Structure

AI-Driven Aging Pattern Analysis for Improving 0.2% Proof Stress in Binary Ni-Al Alloys with γ – γ' Two-Phase Structure

Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys

Artificial Intelligence Inspired Design of Non-Isothermal Heat Treatments for γ - γ' Two-phase Ni-based Alloys

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