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.