3 research outputs found

    Simulation and experimental investigations on tribological characteristics of kenaf/thermoset composites

    Get PDF
    Over the current decade, the use of natural fibres as an alternative to synthetic fibres such as glass and carbon has been growing due to the environmental and economic advantages of natural fibres. In this study, the mechanical and tribological performance of epoxy composites based on kenaf fibres was evaluated. The interfacial adhesion between the kenaf fibres and the epoxy matrix was sudied and the effect of NaOH treatment was considered. The tensile and flexural properties of the untreated and treated kenaf fibre reinforced epoxy (KFRE) were determined, and their fracture behaviour was examined using scanning electron microscopy (SEM). For the tribological experiments, the adhesive wear and frictional experiments were performed considering three different orientations of the fibres with respect to the sliding of the counterface. Different operating parameters were considered, such as applied loads (5–200 N), sliding distances (0–5 km) and sliding velocity (0–3.5m/s) under dry/wet contact conditions. The prediction of the frictional performance of the composites was modelled using artificial neural networks (ANN) considering different configurations. Furthermore, the effects of sand particle size, applied load and kenaf fibre orientation on the three-body abrasion (3B-A) wear behaviour of epoxy composites subjected to high stress were investigated. ABAQUS software was used to develop the 3B-A model aiming to assist in understanding the damage features on the composite surfaces, considering different particle angles, pressures, and fibre orientations

    An artificial neural network for predicting the friction coefficient of deposited Cr1−xAlxC films

    No full text
    [[abstract]]This paper applies a generalized regression neural network (GRNN) for predicting the friction coefficient of deposited Cr1−xAlxC films on high-speed steel substrates via direct current magnetron sputtering systems. The Cr1−xAlxC films exhibited some excellent characteristics, such as low friction coefficient, high hardness, and large contact angle. In this study, a GRNN model is applied for predicting the friction coefficient of Cr1−xAlxC films on high-speed steel substrates instead of complex practical experiments. The results exhibit good prediction accuracy of friction coefficient since about ±0.97% average errors and show the feasibility of the prediction model. Compared to the conventional back propagation model, the GRNN model is more suitable to predict the friction coefficient of Cr1−xAlxC films

    An artificial neural network for predicting the friction coefficient of deposited Cr1-xAlxC films

    No full text
    [[abstract]]This paper applies a generalized regression neural network (GRNN) for predicting the friction coefficient of deposited Cr1?xAlxC films on high-speed steel substrates via direct current magnetron sputtering systems. The Cr1?xAlxC films exhibited some excellent characteristics, such as low friction coefficient, high hardness, and large contact angle. In this study, a GRNN model is applied for predicting the friction coefficient of Cr1?xAlxC films on high-speed steel substrates instead of complex practical experiments. The results exhibit good prediction accuracy of friction coefficient since about ±0.97% average errors and show the feasibility of the prediction model. Compared to the conventional back propagation model, the GRNN model is more suitable to predict the friction coefficient of Cr1?xAlxC films
    corecore