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Filtering by: Funder 科学技術振興機構 Type of work Publication Language English

Descriptor extraction on inherent creep strength of carbon steel by exhaustive search

Description/Abstract:

炭素鋼の基底クリープ強度領域におけるクリープ寿命モデルを提案し、ベイズ統計によるモデル選択手法を適用することで、Moが単独で効果を有することを見出した。

Keyword:

Bayesian inference, Carbon steel, Creep rupture, Exhaustive search, Inherent creep strength, Larson–miller parameter, and Model selection

Resource Type:

Article

Author:

Junya Sakurai, Masahiko Demura, Yoh-ichi Mototake, Masato Okada, Masayoshi Yamazaki, and Junya Inoue

Journal:

Science and Technology of Advanced Materials: Methods

Date Uploaded:

24/10/2023
Artificial intelligence inspired design of non-isothermal aging for γ–γ′ two-phase, Ni–Al alloys

Description/Abstract:

最先端のAIアルゴリズムを活用して，ニッケル基合金において，高温強度をより高くするための時効熱処理を考案した．これまでは等温時効が一般であったところを昇温や降温を含めた複雑な時効熱処理パターンの膨大組み合わせ（約35億通り）から，等温時効を凌駕するパターンを見出すことに挑戦...

Keyword:

Artificial Intelligence, Gamma and Gamma' two-phase microstructure, Heat resistant alloys, High-temperature strength, Inverse design, Ni-Al alloy, and non-isothermal aging

Resource Type:

Article

Author:

Vickey Nandal, Sae Dieb, Dmitry S. Bulgarevich, Toshio Osada, Toshiyuki Koyama, Satoshi Minamoto, and Masahiko Demura

Journal:

Scientific Reports

Date Uploaded:

09/08/2023

Descriptor extraction on inherent creep strength of carbon steel by exhaustive search

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

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