Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks

Zhu, Jianjian1,2; Su, Zhongqing1,2; Wang, Qingqing1,3; Yu, Yinghong4; Wen, Jinshan4; Han, Zhibin5

2023-11-01

10.1088/1361-665X/acfcf8

SMART MATERIALS AND STRUCTURES   影响因子和分区

0964-1726

1361-665X

Continuous and accurate monitoring of the degree of curing (DoC) is essential for ensuring the structural integrity of fabricated composites during service. Although machine learning (ML) has shown effectiveness in DoC monitoring, its generalization and extendibility are limited when applied to other curing-related scenarios not included in the previous learning process. To break through this bottleneck, we propose a novel DoC monitoring approach that utilizes transfer learning (TL)-boosted convolutional neural networks alongside Gramian angular field-based imaging processing. The effectiveness of the proposed approach is validated through experiments on metal/polymeric composite co-bonded structures and carbon fiber reinforced polymers using raw sensor data separately collected through the electromechanical impedance and fiber Bragg grating (FBG) measurements. Four indicators, accuracy, precision, recall, and F1-score are introduced to evaluate the performance of generalization and extendibility of the proposed approach. The indicator scores of the proposed approach exceed 0.9900 and outperform other conventional ML algorithms on the FBG dataset of the target domain, demonstrating the effectiveness of the proposed approach in reusing the pre-trained base model on the composite curing monitoring issues.

curing monitoring polymeric composite machine learning transfer learning convolutional neural networks

SCI ; EI

英语

其他

Dr Jianjian Zhu acknowledges the project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 52205171). Professor Zhongqing Su acknowledges the support from the Hong Kong Research Grants Council via General[52205171] ; Young Scientists Fund of the National Natural Science Foundation of China["15202820","15204419"]

Instruments & Instrumentation ; Materials Science

Instruments & Instrumentation ; Materials Science, Multidisciplinary

WOS:001079308500001

IOP Publishing Ltd

20234414988290

Carbon fiber reinforced plastics ; Convolution ; Convolutional neural networks ; Curing ; Machine learning ; Process control ; Process monitoring

Information Theory and Signal Processing:716.1 ; Artificial Intelligence:723.4 ; Chemical Reactions:802.2 ; Polymer Products:817.1 ; Production Engineering:913.1

Web of Science

1.Hong Kong Polytech Univ, Dept Mech Engn, Hong Kong, Peoples R China
2.Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen, Peoples R China
3.Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg, Shenzhen, Peoples R China
4.Xiamen Univ, Sch Aerosp Engn, Xiamen, Peoples R China
5.Chinese Univ Hong Kong, Hong Kong, Peoples R China

Zhu, Jianjian,Su, Zhongqing,Wang, Qingqing,et al. Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks[J]. SMART MATERIALS AND STRUCTURES,2023,32(11).

Zhu, Jianjian,Su, Zhongqing,Wang, Qingqing,Yu, Yinghong,Wen, Jinshan,&Han, Zhibin.(2023).Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks.SMART MATERIALS AND STRUCTURES,32(11).

Zhu, Jianjian,et al."Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks".SMART MATERIALS AND STRUCTURES 32.11(2023).