社会インフラ・生活を支える革新的かつ高信頼なプロダクト群（高速鉄道車両、原子力プラント、産業機械、家電、検査装置、医用機器、電子顕微鏡など）を創出し、高信頼な運用・保守や循環再利用を実現するための、機械工学とデジタルテクノロジーを高度に融合させた研究開発
デジタルエンジニアリング、設計工学、知識マネジメント、構造強度信頼性、流体力学、振動/耐震/車両ダイナミクス、音響工学、故障リスクマネジメント
Publishing Academic Papers:
Yamaguchi Koki, Masataka Hidai, Kenjiro Goda, Kazuo Kamekawa, Takao Watanabe, Tadamasa Kaneyasu, and Shinji Kinoshita, "Damped Vibration Analysis for Vertical Ride Comfort of Railway Vehicle with Offset Primary Suspension Element Positions.", Vehicle System Dynamics, November, 1-15, 2025
Abstract: This paper reveals an important effect of the primary damper offset on the modal damping and relating responses in railway vehicle dynamics considering carbody flexural motion. These damped vibration characteristics are derived via simple vehicle dynamics simulations with which the influence of the primary suspension offset with respect to the axle centre on the excitation of the bending modes of a railway vehicle carbody in the 0-20 Hz frequency range is studied. The derived results revealed that increasing the axle damper offset from the axle centre in the longitudinal direction caused the axle box to rotate around the axle centre, which led to the modal damping ratio of the system significantly decreasing. Consequently, the mechanism whereby the position of the primary suspension influences the modal damping ratio and relating frequency responses of the vehicle travelling at a constant speed can be theoretically explained.
Tatsuya Hasebe, Erika Katayama, Katsumura Yoshiteru, "Deep CAD Shape Recognition for Carbon Footprint Estimation at the Design Stage", Procedia CIRP, Volume 122, 2024
Abstract: Estimating the carbon footprint of products at the early stage of design is crucial for streamlining the engineering process of sustainable products. However, the carbon footprint estimation of the products requires material and manufacturing information that is typically not available at the early design stage. In this study, a novel method is proposed for carbon footprint estimation, which can evaluate the carbon footprint through the shape recognition of computer aided design (CAD) models based on the graph deep learning. The learning model utilizes the boundary representation of CAD models for deeper understandings of the CAD model shape. The proposed method trains the deep learning model on the existing CAD models to recognize important sub-shapes and attributes, including materials and manufacturing information, such as welded parts, which are the essential data for the carbon footprint estimation. The proposed method enables the designers to estimate the carbon footprint without the laborious condition setting, which facilitates concurrent monitoring and improvement of the carbon footprint. The method is applied to actual assembly models and demonstrated that the material and welded parts, which are attributes required for emission prediction, can be recognized with an accuracy of more than 80%.
Takanori Aono, Masatoshi Kanamaru, Hiroshi Ikeda, "Fabricating Process of Thin-Strain Sensor by Utilizing Wafer-Level-Packaging Techniques", IEEJ Transactions on Sensors and Micromachines, Volume 144, 2024
Abstract: This research has developed a fabricating process of thin-strain sensor by utilizing wafer-level-packaging (WLP) techniques. The thickness of sensor makes thinner, its performance is able to highly increase. However, the thinner sensor was fragile, and so it was difficult to handle in post processes. Thus, a thin sensor with lid by utilizing WLP techniques, which is tough to break even when handled, is proposed in this research. More than 250-mm-deep grooves were fabricated around the lid by deep reactive ion etching. After the lid substrate was bonded on the sensor substrate with a resin, the sensor and lid substrates were respectively polished to 50 µm and 200 µm thickness. The lids were released along the grooves, and the 50-mm-thick strain sensors were able to be fabricated by utilizing WLP techniques. This sensor was used as a diaphragm to measure pressure. The sensors were assembled on a stainless steel housing without breakage. The performance of developed sensor was almost showed with a conventional pressure sensor.
2024年度 日本計算工学会 庄子メダル
2024年度 日本機械学会奨励賞（技術）「グラフ深層学習 3DCAD 形状認識による環境負荷評価技術の開発」
2024年度 日本機械学会奨励賞（技術）「解析高精度化による衝撃吸収性能に優れた通勤鉄道車両の開発」
生成AIを活用した製品設計知識の構造化技術
https://www.hitachihyoron.com/jp/archive/2020s/2025/01/15/index.html#toc-21
鉄道車両の安全と乗り心地を支える台車ダイナミクス技術
https://www.hitachihyoron.com/jp/archive/2020s/2025/01/17/index.html#toc-11
原子力プラントの信頼性向上に向けた溶接品質可視化技術と耐震解析技術
https://www.hitachihyoron.com/jp/archive/2020s/2024/01/17/index.html#sec05
3DCADデータを用いた製品環境負荷の予測技術
https://www.hitachihyoron.com/jp/archive/2020s/2024/01/18/index.html#sec13
