한국산업융합학회 논문집 (Journal of the Korean Society of Industry Convergence)

  • 제27권1호
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  • Pages.151-160
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  • 2024
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  • 1226-833X(pISSN)
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  • 2765-5415(eISSN)
한국산업융합학회 (The Korean Society of Industry Convergence)
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유한요소해석과 순환신경망을 활용한 하중 예측

Load Prediction using Finite Element Analysis and Recurrent Neural Network

  • Jung-Ho Kang (Dept. of Mechanical Engineering, Dong A University)
  • 투고 : 2024.01.10
  • 심사 : 2024.01.23
  • 발행 : 2024.02.28
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초록

Artificial Neural Networks that enabled Artificial Intelligence are being used in many fields. However, the application to mechanical structures has several problems and research is incomplete. One of the problems is that it is difficult to secure a large amount of data necessary for learning Artificial Neural Networks. In particular, it is important to detect and recognize external forces and forces for safety working and accident prevention of mechanical structures. This study examined the possibility by applying the Current Neural Network of Artificial Neural Networks to detect and recognize the load on the machine. Tens of thousands of data are required for general learning of Recurrent Neural Networks, and to secure large amounts of data, this paper derives load data from ANSYS structural analysis results and applies a stacked auto-encoder technique to secure the amount of data that can be learned. The usefulness of Stacked Auto-Encoder data was examined by comparing Stacked Auto-Encoder data and ANSYS data. In addition, in order to improve the accuracy of detection and recognition of load data with a Recurrent Neural Network, the optimal conditions are proposed by investigating the effects of related functions.

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