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Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(11), pp.43-52
  • DOI : 10.9708/jksci.2023.28.11.043
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 19, 2023
  • Accepted : November 20, 2023
  • Published : November 30, 2023

Jaehyun Park 1 Yonghun Jang 1 Bok-Dong Lee 1 Myung-Sub Lee 2

1(주)니어네트웍스
2영남이공대학교

Accredited

ABSTRACT

Rubber produced by rubber companies is subjected to quality suitability inspection through rheometer test, followed by secondary processing for automobile parts. However, rheometer test is being conducted by humans and has the disadvantage of being very dependent on experts. In order to solve this problem, this paper proposes a deep learning-based rheometer quality inspection system. The proposed system combines LSTM(Long Short-Term Memory) and CNN(Convolutional Neural Network) to take advantage of temporal and spatial characteristics from the rheometer. Next, combination materials of each rubber was used as an auxiliary input to enable quality conformity inspection of various rubber products in one model. The proposed method examined its performance with 30,000 validation datasets. As a result, an F1-score of 0.9940 was achieved on average, and its excellence was proved.

Citation status

* References for papers published after 2022 are currently being built.

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