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@article{ART003017286},
author={Jaehyun Park and Yonghun Jang and Bok-Dong Lee and Myung-Sub Lee},
title={Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={11},
pages={43-52},
doi={10.9708/jksci.2023.28.11.043}

TY - JOUR
AU - Jaehyun Park
AU - Yonghun Jang
AU - Bok-Dong Lee
AU - Myung-Sub Lee
TI - Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 11
PB - The Korean Society Of Computer And Information
SP - 43
EP - 52
SN - 1598-849X
AB - 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.
KW - Deep Learning;Smart Factory;LSTM CNN;Rheometer
DO - 10.9708/jksci.2023.28.11.043
ER -

Jaehyun Park, Yonghun Jang, Bok-Dong Lee and Myung-Sub Lee. (2023). Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics. Journal of The Korea Society of Computer and Information, 28(11), 43-52.

Jaehyun Park, Yonghun Jang, Bok-Dong Lee and Myung-Sub Lee. 2023, "Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics", Journal of The Korea Society of Computer and Information, vol.28, no.11 pp.43-52. Available from: doi:10.9708/jksci.2023.28.11.043

Jaehyun Park, Yonghun Jang, Bok-Dong Lee, Myung-Sub Lee "Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics" Journal of The Korea Society of Computer and Information 28.11 pp.43-52 (2023) : 43.

Jaehyun Park, Yonghun Jang, Bok-Dong Lee, Myung-Sub Lee. Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics. 2023; 28(11), 43-52. Available from: doi:10.9708/jksci.2023.28.11.043

Jaehyun Park, Yonghun Jang, Bok-Dong Lee and Myung-Sub Lee. "Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics" Journal of The Korea Society of Computer and Information 28, no.11 (2023) : 43-52.doi: 10.9708/jksci.2023.28.11.043

Jaehyun Park; Yonghun Jang; Bok-Dong Lee; Myung-Sub Lee. Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics. Journal of The Korea Society of Computer and Information, 28(11), 43-52. doi: 10.9708/jksci.2023.28.11.043

Jaehyun Park; Yonghun Jang; Bok-Dong Lee; Myung-Sub Lee. Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics. Journal of The Korea Society of Computer and Information. 2023; 28(11) 43-52. doi: 10.9708/jksci.2023.28.11.043

Jaehyun Park, Yonghun Jang, Bok-Dong Lee, Myung-Sub Lee. Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics. 2023; 28(11), 43-52. Available from: doi:10.9708/jksci.2023.28.11.043

Jaehyun Park, Yonghun Jang, Bok-Dong Lee and Myung-Sub Lee. "Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics" Journal of The Korea Society of Computer and Information 28, no.11 (2023) : 43-52.doi: 10.9708/jksci.2023.28.11.043