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Classification of rubber vulcanizing accelerators based on particle swarm optimization extreme learning machine and terahertz spectra
Abstract
In rubber tire production, three popular types of rubber vulcanizing accelerators exist that are similar in appearance (i.e., 2-Mercaptobenzothiazole, 4,4′-dithiodimorpholine, and tetramethyl thiuram monosulfide). Because the rubber vulcanizing accelerator has a great influence on the vulcanized rubber characteristics, it is necessary to classify and identify the three popular types of rubber vulcanizing accelerators to avoid using the wrong accelerator during tire production and to ensure the tire quality. The THz spectra of the accelerator samples were measured using a terahertz time-domain spectral system (THz-TDS) in a frequency range of 0.3–1.6 THz. An extreme learning machine (ELM) model was constructed to classify the three popular types of rubber vulcanizing accelerators via terahertz absorption spectra. To improve the classification accuracy of the model, a particle swarm optimization ELM model was constructed possessing a higher classification accuracy than the ELM model in the classification and identification of rubber vulcanizing accelerators.
Keywords
terahertz spectrum,
rubber vulcanization accelerator,
particle swarm optimization,
extreme learning machine,
classification.
Supporting agencies: This work was supported by the National Natural Science Foundation of China (NSFC) (grant No. 61841502), the Guangxi Natural Science Foundation (grant No. 2018GXNSFAA281341) and the Foundation from Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (grant No. YQ19102). We thank Sara Maccagnano-Zacher, PhD, from Liwen Bianji (Edanz) (https://www.liwenbianji.cn) for editing the language of a draft of this manuscript.
About the Authors
X. Yin
School of Electronic Engineering and Automation at Guilin University of Electronic Technology;
Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
Russian Federation
Russian Federation
Guangxi Guilin 541004
W. He
School of Electronic Engineering and Automation at Guilin University of Electronic Technology;
Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
Russian Federation
Russian Federation
Guangxi Guilin 541004
L. Wang
School of Electronic Engineering and Automation at Guilin University of Electronic Technology;
Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
Russian Federation
Russian Federation
Guangxi Guilin 541004
W. Mo
School of Electronic Engineering and Automation at Guilin University of Electronic Technology;
Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
Russian Federation
Russian Federation
Guangxi Guilin 541004
A. Li
National Rubber and Rubber Products Quality Supervision and Inspection Center (Guangxi)
Russian Federation
Russian Federation
Guilin 541004
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Review
For citations:
Yin X., He W., Wang L., Mo W., Li A. Classification of rubber vulcanizing accelerators based on particle swarm optimization extreme learning machine and terahertz spectra. Zhurnal Prikladnoii Spektroskopii. 2021;88(6):980.
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