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Tensile Properties Estimation Method Using Convolutional LSTM Model

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2018, 23(11), pp.43-49
  • DOI : 10.9708/jksci.2018.23.11.043
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 30, 2018
  • Accepted : November 23, 2018
  • Published : November 30, 2018

Choi Hyeon-Joon 1 Kang, Dong-Joong 1

1부산대학교

Accredited

ABSTRACT

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

Citation status

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This paper was written with support from the National Research Foundation of Korea.