This study investigates the mechanical, thermal, and chemical properties of basalt/woven glass fiber reinforced polymer (BGRP) hybrid polyester composites. The Fourier transform infrared spectroscopy (FTIR) was used to explore the chemical aspect, whereas the dynamic mechanical analysis (DMA) and thermomechanical analysis (TMA) were performed to determine the mechanical and thermal properties. The dynamic mechanical properties were evaluated in terms of the storage modulus, loss modulus, and damping factor. The FTIR results showed that incorporating single and hybrid fibers in the matrix did not change the chemical properties. The DMA findings revealed that the B7.5/G22.5 composite with 7.5 wt% of basalt fiber (B) and 22.5 wt% of glass fiber (G) exhibited the highest elastic and viscous properties, as it exhibited the higher storage modulus (8.04 × 109 MPa) and loss modulus (1.32 × 109 MPa) compared to the other samples. All the reinforced composites had better damping behavior than the neat matrix, but no further enhancement was obtained upon hybridization. The analysis also revealed that the B22.5/G7.5 composite with 22.5 wt% of basalt fiber and 7.5 wt% of glass fiber had the highest Tg at 70.80 °C, and increased by 15 °C compared to the neat matrix. TMA data suggested that the reinforced composites had relatively low dimensional stabilities than the neat matrix, particularly between 50 to 80 °C. Overall, the hybridization of basalt and glass fibers in unsaturated polyester formed composites with higher mechanical and thermal properties than single reinforced composites.
Natural and synthetic fibres have emerged in high demand due to their excellent properties. Natural fibres have good mechanical properties and are less expensive, making them a viable substitute for synthetic fibers. Owing to certain drawbacks such as their inconsistent quality and hydrophilic nature, researchers focused on incorporating these two fibres as an alternative to improve the limitations of the single fibre. This review focused on the interply hybridisation of natural and synthetic fibres into composites. Natural fibres and their classifications are discussed. The physical and mechanical properties of these hybrid composites have also been included. A full discussion of the mechanical properties of natural/synthetic fibre hybrid composites such as tensile, flexural, impact, and perforation resistance, as well as their failure modes, is highlighted. Furthermore, the applications and future directions of hybrid composites have been described in details.
The study of prediction has drawn great interest in a wide range of field. T-Method which was developed specifically for prediction of the multidimensional case using historical data to develop its baseline model proved that making a prediction is possible even with limited sample size. The element of the signal to noise ratio (SNR) adopted into the T-Method strengthens its robustness. Orthogonal array (OA) in T-Method was used as features selection optimization in improving the analysis speed, cost and computer burden during the analysis. However, the limitation of OA in dealing with higher dimensionality and complex combination factors restraint the optimization accuracy. Artificial Bee Colony (ABC) was adopted in this study to overcome this limitation. The result of this study shows that T-method +ABC provide the best error% accuracy with only 2.45% and 2.53% (3 optimized features out of 15) compared to T-Method +OA which 2.81% and 2.67% and T-Method +Spearman Correlation as 3.16% and 3.06%. The power consumption prediction case study is a good example for cases that deal with high correlation coefficient (R2) baseline model (>0.8). If the R2 is lower than 0.8, further enhancement needs to be done to ensure a low risk of high error% prediction.
To categorize the different patterns of connecting rod based on the extent to which the product is remanufacturable is very challenging because of the existence of various models and wide tolerances. Sometimes it cannot be done due to the improper pattern recognition system. Mahalanobis-Taguchi System (MTS) is a diagnostic method employing Mahalanobis Distance (MD) for recognizing different patterns in multivariate data. The aim of this work is to apply T method-3, a sub-method of MTS, to the big-end diameter of connecting rod to distinguish between two distinct ranges within the remanufacturability process spectrum. Furthermore, the method also categorizes various patterns of connecting rod based on their MD from unit space with graphical illustration. The case study is performed in an automotive industry as well as in a contract remanufacturing environment in Malaysia. The outcome of this work is expected to be the enhancement of robustness in the remanufacturing system on pattern recognition applicable to the company under study. It is expected that the company will experience time and energy savings and improved work quality. The resulting systematic analysis is expected to enable fast decision-making. Finally, this study is expected to invoke among researchers a sense of seriousness in their approach towards various case studies involving the upgrading of the remanufacturing process that will bring Malaysias remanufacturing capability on par with that of other developed countries.
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