Amina Dinari, Tarek Benameur and Fuad Khoshnaw
The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis…
Abstract
Purpose
The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.
Design/methodology/approach
This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.
Findings
This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.
Originality/value
This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.
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Hanen Ghanmi, Adel Ghith and Tarek Benameur
In this study, the response surface methodology is used to predict the mechanical properties of yarn, their unevenness and hairiness by using the high-volume instrument (HVI…
Abstract
In this study, the response surface methodology is used to predict the mechanical properties of yarn, their unevenness and hairiness by using the high-volume instrument (HVI) properties of raw cotton and the parameters of the spinning process. Therefore, five different blends of cotton are processed and spun into ring yarns (Nm13, Nm19, Nm 21, Nm31 and Nm37). Each count is spun at five twist levels (450, 500, 650, 750 and 850 trs/m).
The models that are developed by using response surface regression with many iterations on a Minitab16 statistical software predict very well the different yarn properties since the R2 values obtained are very important. In addition, these models show that metric number and twist have the highest effect on the four studied parameters
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Hanen Ghanmi, Adel Ghith and Tarek Benameur
The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a…
Abstract
Purpose
The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn.
Design/methodology/approach
The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error.
Findings
The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy.
Originality/value
Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.