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  • Summary & Details

Development of Modeling Method for the Self Switchable Hydromount by Machine Learning

機械学習を用いた自立切替式液封マウントのモデル化技術開発

Detailed Information

Category(J)技術論文

Translation

Category(E)TechnicalPaper
Author(J)1) 中津川 英治, 2) 山下 誠二, 3) 堂上 靖史, 4) 伊藤 友文, 5) 森田 英憲, 6) Dirk Hoffmann, 7) Peter Mas
Author(E)1) Eiji Nakatsugawa, 2) Seiji Yamashita, 3) Yasushi Dohnoue, 4) Tomofumi Itoh, 5) Hidenori Morita, 6) Dirk Hoffmann, 7) Peter Mas
Affiliation(J)1) トヨタ自動車, 2) トヨタ自動車, 3) トヨタ自動車, 4) トヨタ自動車, 5) トヨタ自動車, 6) Siemens Industry Software S.A.S. Simcenter Engineering Services Div., 7) Siemens Industry Software S.A.S. Simcenter Engineering Services Div.
Abstract(J)自立切り替え式液封マウントは振幅・周波数に依存する非線形な特性である為,モデルベース開発の時間軸計算に必要な等価力学系への置換えは困難である.そこで,マウントのばね・減衰の要求特性から機械学習によるモデル化手法を構築した.更に,その予測精度を検証し,1D-CAEに組み込んだ際の計算安定性を確認した.

Translation

Abstract(E)We present a modeling method for self switchable hydromount from the specification of spring and damping by using machine learning. This is because this component has a non-linear characteristic depending on amplitude and frequency, so it is difficult to substitute to the equivalent dynamics system which is generally performed in the time series calculations at the model-based development. We confirmed the model’s prediction accuracy and also inspected the calculation stability when it is incorporated in 1D-CAE.

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