The temperature dependence of strengthening mechanisms in Ni-based superalloys: A newly re-defined cuboidal model and its implications for strength design

Wu, Liberty T.; Osada, Toshio; Yokokawa, Toshio; Chang, Ya‐Ling; Kawagishi, Kyoko

doi:10.1016/j.jallcom.2022.167508

Cited by 12 publications

(4 citation statements)

References 39 publications

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“…Generally, the γ' secondary phase is a strengthening phase in these materials to achieve higher mechanical strength [22][23][24][25][26][27] . In this study, a graphical representation was generated to illustrate the relationship between γ' size and their respective 0.2% proof stress values derived from approximately 100 NIA patterns identi ed through AI methodologies, depicted in Fig.…”

Section: Microstructural Characteristicsmentioning

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: Microstructural Characteristicsmentioning

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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“…In addition, the precipitation hardening by the γ′ precipitates and solid solution hardening significantly improves the mechanical properties in high-temperature alloys 1 , 15 , 16 , 20 , 22 . The morphology and volume fraction of γ′-precipitate revealed by the aging treatment history and critically affect the mechanical properties of the alloys by acting as barriers to dislocation motions 19 , 23 – 28 . Wu et al found that the precipitates can change from small, spherical particles to larger, elongated particles as aging time increases 29 .…”

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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“…Therefore, the precipitation hardening by the γ' precipitates and solid solution hardening signi cantly improves the mechanical properties 2,[11][12][13] . The morphology and volume fraction of γ'-precipitate expose the HT history and critically affects the alloy's mechanical properties by acting as barriers to dislocation motions [14][15][16][17][18][19][20] .…”

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

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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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The temperature dependence of strengthening mechanisms in Ni-based superalloys: A newly re-defined cuboidal model and its implications for strength design

Cited by 12 publications

References 39 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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