Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

doi:10.1007/s40436-023-00455-z

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (1): 167-176.doi: 10.1007/s40436-023-00455-z

• • 上一篇

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

Qiang-Qiang Liu¹, Shu-Ting Liu¹, Ying-Guang Li¹, Xu Liu², Xiao-Zhong Hao¹

1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China;
2. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, People's Republic of China

收稿日期:2022-10-28 修回日期:2023-03-05 发布日期:2024-03-14
通讯作者: Ying-Guang Li,E-mail:liyingguang@nuaa.edu.cn E-mail:liyingguang@nuaa.edu.cn
作者简介:Qiang-Qiang Liu received a B.S. degree in Aircraft Manufacturing Engineering from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2020. He is now studying for his M.S. degree at the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics. His current research interest is self-resistive heating and curing monitoring of carbon fiber reinforced polymer;
Shu-Ting Liu received his B.S. degree in mechanical engineering and Ph.D. degrees in Aeronautics and Astronautics Manufacturing Engineering from the Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2015 and 2022, respectively. He has been an assistant professor at NUAA. His research interest is advanced curing technology of composite part;
Ying-Guang Li is a professor at the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. He is the associate editor of the Journal of Manufacturing Systems and IMechE Proceedings Part B, and Deputy Director of the Editorial Board of the Chinese Journal of Mechanical Engineering. He directed more than 10 major projects funded by the National Natural Science Foundation of China and aerospace industry. He was awarded the second prize in the 2016 Chinese National Award for Technological Invention. Prof Li was elected as Cheung Kong Scholar Chair Professor in 2017 and awarded the National Science Fund for Distinguished Young Scholars of China in 2019. His research interests include intelligent manufacturing, NC machining, microwave curing principles and technologies for aerospace composites;
Xu Liu received his B.S. degree in mechanical engineering and Ph.D. degree in Aeronautics and Astronautics Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2009 and 2015, respectively. He has been an associate professor at the Nanjing Tech University, Nanjing, China. His research interest is data-driven intelligent manufacturing;
Xiao-Zhong Hao received his Ph.D. degree in Manufacturing Engineering of Aeronautics and Astronautics from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2018. He is a professor at the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics. His research interests include intelligent CNC machining and advanced manufacturing of composite materials.
基金资助:
This work is supported by the Major Program of National Natural Science Foundation of China (Grant No. 52090052) and General Program of National Natural Science Foundation of China (Grant No. 51875288), the authors sincerely appreciate the continuous support provided by their industrial collaborators.

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

Qiang-Qiang Liu¹, Shu-Ting Liu¹, Ying-Guang Li¹, Xu Liu², Xiao-Zhong Hao¹

1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People's Republic of China;
2. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, People's Republic of China

Received:2022-10-28 Revised:2023-03-05 Published:2024-03-14
Contact: Ying-Guang Li,E-mail:liyingguang@nuaa.edu.cn E-mail:liyingguang@nuaa.edu.cn
Supported by:
This work is supported by the Major Program of National Natural Science Foundation of China (Grant No. 52090052) and General Program of National Natural Science Foundation of China (Grant No. 51875288), the authors sincerely appreciate the continuous support provided by their industrial collaborators.

摘要/Abstract

摘要： Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts. Traditional embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the part. In this paper, a non-contact, full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is proposed. Using the proposed method, an average temperature monitoring accuracy of 97% is achieved in various heating patterns. In addition, this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part. This method was experimentally validated during the self-resistance electric heating process, in which the monitoring accuracy reached 93.1%. This method can potentially be applied to automated manufacturing and process control in the composites industry.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00455-z

关键词: Online monitoring, Curing temperature field, Deep learning (DL), In-situ heating

Abstract: Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts. Traditional embedded sensor-based technologies have difficulty monitoring the full temperature field or have to introduce heterogeneous items that could have an undesired impact on the part. In this paper, a non-contact, full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using surface temperature measurements of auxiliary materials is proposed. Using the proposed method, an average temperature monitoring accuracy of 97% is achieved in various heating patterns. In addition, this method also demonstrates satisfying feasibility when a stronger thermal barrier covers the part. This method was experimentally validated during the self-resistance electric heating process, in which the monitoring accuracy reached 93.1%. This method can potentially be applied to automated manufacturing and process control in the composites industry.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00455-z

Key words: Online monitoring, Curing temperature field, Deep learning (DL), In-situ heating

Qiang-Qiang Liu, Shu-Ting Liu, Ying-Guang Li, Xu Liu, Xiao-Zhong Hao. Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning[J]. Advances in Manufacturing, 2024, 12(1): 167-176.

参考文献

1 Kim M, Sung DH, Kong K et al (2016) Characterization of resistive heating and thermoelectric behavior of discontinuous carbon fiber-epoxy composites. Compos B-Eng 90:37–44
2 Joseph C, Viney C (2000) Electrical resistance curing of carbon-fibre/epoxy composites. Compos Sci Technol 60:315–319
3 Xia T, Zeng D, Li Z et al (2018) Electrically conductive GNP/epoxy composites for out-of-autoclave thermoset curing through Joule heating. Compos Sci Technol 164:304–312
4 Liu S, Li Y, Shen Y et al (2019) Mechanical performance of carbon fiber/epoxy composites cured by self-resistance electric heating method. Int J Adv Manuf Tech 103:3479–3493
5 Zhou J, Li Y, Zhu Z et al (2022) Microwave heating and curing of metal-like CFRP laminates through ultrathin and flexible resonance structures. Compos Sci Technol 218:109200. https://doi.org/10.1016/j.compscitech.2021.109200
6 Naik TP, Singh I, Sharma AK (2022) Processing of polymer matrix composites using microwave energy: a review. Compos Part A-Appl S 156:106870. https://doi.org/10.1016/j.compositesa.2022.106870
7 Li N, Li Y, Jelonnek J et al (2017) A new process control method for microwave curing of carbon fibre reinforced composites in aerospace applications. Compos B- Eng 122:61–70
8 Chen J, Wang Y, Liu F et al (2020) Laser-induced graphene paper heaters with multimodally patternable electrothermal performance for low-energy manufacturing of composites. ACS Appl Mater Inter 12:23284–23297
9 Tu R, Liu T, Steinke K et al (2022) Laser induced graphene-based out-of-autoclave curing of fiberglass reinforced polymer matrix composites. Compos Sci Technol 226:109529. https://doi.org/10.1016/j.compscitech.2022.109529
10 Shen Y, Lu Y, Liu S et al (2022) Temperature distribution analysis of carbon fiber reinforced polymer composites during self-resistance electric heating process. J Reinf Plast Comp 41(19/20):805–821
11 Zobeiry N, Park J, Poursartip A (2019) An infrared thermography-based method for the evaluation of the thermal response of tooling for composites manufacturing. J Compos Mater 53:1277–1290
12 Dolkun D, Wang H, Wang H et al (2020) Influence of large framed mold placement in autoclave on heating performance. Appl Compos Mater 27:811–837
13 Zhou J, Li Y, Li N et al (2018) A multi-pattern compensation method to ensure even temperature in composite materials during microwave curing process. Compos Part A-Appl S 107:10–20
14 Shen Y, Lu Y, Liu S et al (2022) Self-resistance electric heating of shaped CFRP laminates: temperature distribution optimization and validation. Int J Adv Manuf Technol 121:1755–1768
15 Zhang B, Li Y, Liu S et al (2021) Layered self-resistance electric heating to cure thick carbon fiber reinforced epoxy laminates. Polym Compos 42:2469–2483
16 Eriksen A, Osinski D, Hjelme DR (2014) Evaluation of thermal imaging system and thermal radiation detector for real-time condition monitoring of high power frequency converters. Adv Manuf 2:88–94
17 Huang XK, Tian XY, Zhong Q et al (2020) Real-time process control of powder bed fusion by monitoring dynamic temperature field. Adv Manuf 8:380–391
18 Li F, Yu ZH, Li H et al (2022) Real-time monitoring of raster temperature distribution and width anomalies in fused filament fabrication process. Adv Manuf 10:571–582
19 Konstantopoulos S, Tonejc M, Maier A et al (2015) Exploiting temperature measurements for cure monitoring of FRP composites—applications with thermocouples and infrared thermography. J Reinf Plast Comp 34:1015–1026
20 Ito Y, Minakuchi S, Mizutani T et al (2012) Cure monitoring of carbon–epoxy composites by optical fiber-based distributed strain–temperature sensing system. Adv Compos Mater 21:259–271
21 Hübner M, Lang W (2017) Online monitoring of composites with a miniaturized flexible combined dielectric and temperature sensor. In: Multidisciplinary digital publishing institute proceedings, Paris, France, 2017, 1, p 627. https://doi.org/10.3390/proceedings1040627
22 Ramakrishnan M, Rajan G, Semenova Y et al (2016) Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials. Sensors 16:99. https://doi.org/10.3390/s16010099
23 Bagavathiappan S, Lahiri BB, Saravanan T et al (2013) Infrared thermography for condition monitoring—a review. Infrared Phys Techn 60:35–55
24 Nash C, Karve P, Adams D et al (2020) Real-time cure monitoring of fiber-reinforced polymer composites using infrared thermography and recursive Bayesian filtering. Compos B-Eng 198:108241. https://doi.org/10.1016/j.compositesb.2020.108241
25 Zobeiry N, Humfeld KD (2021) A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Eng Appl Artif Intel 101:104232. https://doi.org/10.1016/j.engappai.2021.104232
26 Humfeld KD, Gu D, Butler GA et al (2021) A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control. Compos B-Eng 223:109150. https://doi.org/10.1016/j.compositesb.2021.109150
27 Wang M, Hu W, Jiang Y et al (2021) Internal temperature prediction of ternary polymer lithium-ion battery pack based on CNN and virtual thermal sensor technology. Int J Energy Res 45:13681–13691
28 Ma H, Hu X, Zhang Y et al (2020) A combined data-driven and physics-driven method for steady heat conduction prediction using deep convolutional neural networks. arXiv preprint arXiv:2005.08119. https://doi.org/10.48550/arXiv.2005.08119
29 Amini NS, Haghighat E, Campbell T et al (2021) Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture. Comput Method Appl M 384:113959. https://doi.org/10.1016/j.cma.2021.113959
30 Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 2015, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594
31 Alvarez-Ramirez J, Rodriguez E, Carlos Echeverría J (2005) Detrending fluctuation analysis based on moving average filtering. Physica A 354:199–219
32 Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data-Ger 6:60. https://doi.org/10.1186/s40537-019-0197-0

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

Non-contact and full-field online monitoring of curing temperature during the in-situ heating process based on deep learning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价