Classification of autoclave temperature via deep learning

Wan Ju Lin; Jian Wen Chen; Che Lun Hung; Ching Hsien Hsu

doi:10.1109/IUCC/DSCI/SmartCNS.2019.00136

Classification of autoclave temperature via deep learning

Wan Ju Lin, Jian Wen Chen, Che Lun Hung, Ching Hsien Hsu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	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
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	653-656
Number of pages	4
ISBN (Electronic)	9781728152097
DOIs	https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00136
State	Published - 10 2019
Externally published	Yes
Event	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 - Shenyang, China Duration: 21 10 2019 → 23 10 2019

Publication series

Name	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

Conference

Conference	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
Country/Territory	China
City	Shenyang
Period	21/10/19 → 23/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Autoclave Forming
Composite Material
Convolution Neural Network
Deep Learning
Long Short Term Memory

Access to Document

10.1109/IUCC/DSCI/SmartCNS.2019.00136

Cite this

Lin, W. J., Chen, J. W., Hung, C. L., & Hsu, C. H. (2019). Classification of autoclave temperature via deep learning. In 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 (pp. 653-656). Article 8982577 (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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00136

Lin, Wan Ju ; Chen, Jian Wen ; Hung, Che Lun et al. / Classification of autoclave temperature via deep learning. 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. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 653-656 (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).

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title = "Classification of autoclave temperature via deep learning",

abstract = "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.",

keywords = "Autoclave Forming, Composite Material, Convolution Neural Network, Deep Learning, Long Short Term Memory",

author = "Lin, {Wan Ju} and Chen, {Jian Wen} and Hung, {Che Lun} and Hsu, {Ching Hsien}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 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 ; Conference date: 21-10-2019 Through 23-10-2019",

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Lin, WJ, Chen, JW, Hung, CL & Hsu, CH 2019, Classification of autoclave temperature via deep learning. in 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., 8982577, 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, Institute of Electrical and Electronics Engineers Inc., pp. 653-656, 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, Shenyang, China, 21/10/19. https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00136

Classification of autoclave temperature via deep learning. / Lin, Wan Ju; Chen, Jian Wen; Hung, Che Lun et al.
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. Institute of Electrical and Electronics Engineers Inc., 2019. p. 653-656 8982577 (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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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.

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KW - Composite Material

KW - Convolution Neural Network

KW - Deep Learning

KW - Long Short Term Memory

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Lin WJ, Chen JW, Hung CL, Hsu CH. Classification of autoclave temperature via deep learning. In 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. Institute of Electrical and Electronics Engineers Inc. 2019. p. 653-656. 8982577. (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). doi: 10.1109/IUCC/DSCI/SmartCNS.2019.00136

Classification of autoclave temperature via deep learning

Abstract

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Bibliographical note

Keywords

Access to Document

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