TY - GEN
T1 - Classification of autoclave temperature via deep learning
AU - Lin, Wan Ju
AU - Chen, Jian Wen
AU - Hung, Che Lun
AU - Hsu, Ching Hsien
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In the composite material processing, autoclave forming is a commonly-used approach by the action of heat and pressure at the same time. The temperature distribution could greatly affect the quality of composite material. However, high temperature and cacuum leakage could result in poor quality of composite products. It is important to discover the reasons that caused undesirable composite product during the processing. In recent years, deep learning technique has achieved great success in improving manufacturing processing. In this paper, we applied CNN and long short term memory (LSTM) models for analysis the processing temperature types of the composite materials. In this study, we have made a comparative analysis of two different classification algorithms with 8 categories autoclave. The results show that CNN model was able to correctly recognize eight types of the autoclave in 83.33%, and 72.22% accuracy of LSTM model. With this intelligence models, which make it possible to perform in the autoclave forming processing to trace out the types of composite processing temperature.
AB - In the composite material processing, autoclave forming is a commonly-used approach by the action of heat and pressure at the same time. The temperature distribution could greatly affect the quality of composite material. However, high temperature and cacuum leakage could result in poor quality of composite products. It is important to discover the reasons that caused undesirable composite product during the processing. In recent years, deep learning technique has achieved great success in improving manufacturing processing. In this paper, we applied CNN and long short term memory (LSTM) models for analysis the processing temperature types of the composite materials. In this study, we have made a comparative analysis of two different classification algorithms with 8 categories autoclave. The results show that CNN model was able to correctly recognize eight types of the autoclave in 83.33%, and 72.22% accuracy of LSTM model. With this intelligence models, which make it possible to perform in the autoclave forming processing to trace out the types of composite processing temperature.
KW - Autoclave Forming
KW - Composite Material
KW - Convolution Neural Network
KW - Deep Learning
KW - Long Short Term Memory
UR - http://www.scopus.com/inward/record.url?scp=85081106573&partnerID=8YFLogxK
U2 - 10.1109/IUCC/DSCI/SmartCNS.2019.00136
DO - 10.1109/IUCC/DSCI/SmartCNS.2019.00136
M3 - Conference contribution
AN - SCOPUS:85081106573
T3 - Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019
SP - 653
EP - 656
BT - Proceedings - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019
Y2 - 21 October 2019 through 23 October 2019
ER -