Mechanical tests, for example, tensile and hardness tests, are usually used to evaluate the properties of rubber materials. In this work, mechanical properties of selected rubber materials, that is, natural rubber (NR), styrene butadiene rubber (SBR), nitrile butadiene rubber (NBR), and ethylene propylene diene monomer (EPDM), were evaluated using a near infrared (NIR) spectroscopy technique. Here, NR/NBR and NR/EPDM blends were first prepared. All of the samples were then scanned using a FT-NIR spectrometer and fitted with an integration sphere working in a diffused reflectance mode. The spectra were correlated with hardness and tensile properties. Partial least square (PLS) calibration models were built from the spectra datasets with preprocessing techniques, that is, smoothing and second derivative. This indicated that reasonably accurate models, that is, with a coefficient of determination [R2] of the validation greater than 0.9, could be achieved for the hardness and tensile properties of rubber materials. This study demonstrated that FT-NIR analysis can be applied to determine hardness and tensile values in rubbers and rubber blends effectively.
Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky-Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky-Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r 2 values were greater than 0.9, the bias values were among AE0.5, and the RMSEP values were lower than 5.
The objective of this research was to reduce an offensive odour by using perlite as an odour-adsorbing filler. Perlite was mixed with highly odorous STR20 and RSS5. Perlite content was varied at 0, 10, 20 and 30 phr. The ability to reduce odour by sensory method and GC-MS were examined. Cure characteristics and mechanical properties, such as tensile strength, hardness and abrasion resistance, were also determined. From the sensory method, the odour reduction efficiency increased with increasing perlite content.With an addition of perlite, the reduction of some components from an offensive odour, such as thiourea and benzothiazole, was also observed using GC-MS technique. Furthermore, the addition of perlite showed an increase in torque difference, a slight change in scorch time and a decrease in cure time, an increase in hardness and abrasion resistance but a decrease in tensile strength. Perlite was shown to be comparable to clay with respect to mechanical properties and potentially be used as low-cost and odour-adsorbing filler.
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