Rapidly and accurately determining the resin and volatile content of CF/PPBESK thermoplastic prepreg by NIR spectroscopy

被引:15
作者
Hao, Haoyue [1 ,2 ,3 ]
Cheng, Shan [1 ,2 ,3 ]
Ren, Zifei [1 ,2 ]
Zhang, Liyan [1 ,2 ]
Wang, Bing [1 ,2 ,3 ]
Li, Nan [1 ,2 ]
Bao, Qingguang [1 ,2 ]
Feng, Jingyao [1 ,2 ]
Hu, Fangyuan [1 ,2 ,3 ]
Liu, Cheng [1 ,2 ]
Zhang, Shouhai [1 ,2 ]
Jian, Xigao [1 ,2 ,3 ]
机构
[1] Dalian Univ Technol, Frontier Sci Ctr Smart Mat, Sch Chem Engn, State Key Lab Fine Chem, Dalian 116024, Peoples R China
[2] Technol Innovat Ctr High Performance Resin Mat, Dalian 116024, Liaoning, Peoples R China
[3] Dalian Univ Technol, Sch Mat Sci & Engn, Dalian 116024, Peoples R China
基金
美国国家科学基金会;
关键词
Prepreg; Analytical modelling; Process monitoring; NEAR-INFRARED SPECTROSCOPY; VARIABLE SELECTION METHOD; QUANTITATIVE-ANALYSIS; FIBER PREPREG; QUALITY; MODELS;
D O I
10.1016/j.compositesa.2023.107517
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, near infrared (NIR) spectroscopy was used to predict the resin content and volatile content of CF/ PPBESK prepreg. Mathematical models between resin and volatile content and spectra were established by PLS, respectively. To improve the accuracy of the models, 8 algorithms were used to preprocess the NIR spectra of the prepreg. The best preprocessing methods were SNV for resin content and SNV + 1stDER for volatile content. Combined with the CARS wavelength screening algorithm separately, the PLS mathematical models of resin and volatile content were established, respectively. The maximum absolute errors in predicting the resin content and volatile content of 14 randomly collected samples were 0.998 % and 0.203 %, respectively. The entire detection process was completed within 60 s, which was 180 times faster than the traditional analysis method. Therefore, the NIR spectroscopy can achieve accurate, rapid, nondestructive and synchronized measurement of resin content and volatile content.
引用
收藏
页数:10
相关论文
共 44 条
[1]   Variable Selection Method in the NIR Quantitative Analysis Model of Total Saponins in Red Ginseng Extract [J].
An Si-yu ;
Zhang Lei ;
Shang Xian-zhao ;
Yue Hong-shui ;
Liu Wen-yuan ;
Ju Ai-chun .
SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (01) :206-209
[2]   Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis [J].
Assi, Sulaf ;
Arafat, Basel ;
Lawson-Wood, Kathryn ;
Robertson, Ian .
APPLIED SPECTROSCOPY, 2021, 75 (04) :434-444
[3]   Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data [J].
Balabin, Roman M. ;
Smirnov, Sergey V. .
ANALYTICA CHIMICA ACTA, 2011, 692 (1-2) :63-72
[4]   Classification tools in chemistry. Part 1: linear models. PLS-DA [J].
Ballabio, Davide ;
Consonni, Viviana .
ANALYTICAL METHODS, 2013, 5 (16) :3790-3798
[5]   Using mixture design approach for modeling and optimizing tribology properties of PPESK composites [J].
Bao, Qingguang ;
Li, Nan ;
Feng, Jingyao ;
Yan, Shebin ;
Ma, Gaozhang ;
Wang, Bing ;
Cheng, Shan ;
Hao, Haoyue ;
Liu, Cheng ;
Zhang, Shouhai ;
Chen, Yousi ;
Jian, Xigao .
COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING, 2022, 163
[6]   A local pre-processing method for near-infrared spectra, combined with spectral segmentation and standard normal variate transformation [J].
Bi, Yiming ;
Yuan, Kailong ;
Xiao, Weiqiang ;
Wu, Jizhong ;
Shi, Chunyun ;
Xia, Jun ;
Chu, Guohai ;
Zhang, Guangxin ;
Zhou, Guojun .
ANALYTICA CHIMICA ACTA, 2016, 909 :30-40
[7]   Pull-out of copolymer in situ-formed during reactive blending: effect of the copolymer architecture [J].
Charoensirisomboon, P ;
Inoue, T ;
Weber, M .
POLYMER, 2000, 41 (18) :6907-6912
[8]   Preparation of CF-GO-SiO2 multi-scale reinforcements based on electrostatic interaction: A non-destructive and simple method to construct hybrid interface layers of carbon fiber reinforced thermoplastic resin composites [J].
Cheng, Shan ;
Li, Nan ;
Wang, Bing ;
Hu, Fangyuan ;
Zong, Lishuai ;
Hao, Haoyue ;
Bao, Qingguang ;
Liu, Cheng ;
Chen, Yousi ;
Jian, Xigao .
COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING, 2022, 160
[9]   Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement [J].
Durduran, Turgut ;
Yodh, Arjun G. .
NEUROIMAGE, 2014, 85 :51-63
[10]   Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique [J].
Ferguson, Dougal ;
Henderson, Alex ;
McInnes, Elizabeth F. ;
Lind, Rob ;
Wildenhain, Jan ;
Gardner, Peter .
ANALYST, 2022, 147 (16) :3709-3722