Search

Usually, the laminated-composite-plates (LCPs) are strengthened by altering the constituents. The use of nano-particles as filler is a new approach in this regard. Here, flyash and graphene are used as fillers in epoxy-based woven E-glass fabric-reinforced LCPs. The LCPs are often subjected to moving mass/load in use and it becomes necessary to study their stability. Further, foundation support has an important role in structural stability. Here, the response of LCPs resting on elastic foundations to a moving load is studied. A fifth-order plate-theory based on Eringen’s non-local model for LCPS with filler is followed and validated with finite-element-analysis (FEA) and other literature. The effect of the intensity of moving mass, its position on LCP, speed, the material variant, foundation constant, and damping ratio on the dynamicity of LCPs is then reported. It is observed that masses moving on the LCP induce instability with frequency loss (FL) and increased dynamic amplitude ratio (DAR) in vibration. LCPs with the least FL and maximum DAR are more stable. Further, LCP with flyash (FLCP) is highly unstable with 18.5% FL for 10% moving load and GLCP (LCP with graphene) is the most stable (6% loss). Meanwhile, the DAR for GLCP is maximum (16.13%) at 140 m/s critical velocity of moving mass. Increasing the Pasternak co-efficient increases foundation stiffness and frequency whereas Winkler’s parameter has a negligible effect. The foundation without damping oscillates more critically (with a maximized DAR of 1.92) in comparison to the foundation with a damping ratio of 0.1 (DAR of 1.17).

ABSTRACTAt the early age of the joint replacements, natural metals like iron, gold and zinc were used. With the advancement in material science, new biomaterials (alloyed materials and high grade of polymeric composites) were developed. The significance of these biomaterials is higher load-carrying capacity, good wear resistance and compatibility to human body and tissues unlike the natural metals. In this study four different biomaterials (three sets of alloy metals and one set of polymeric composites) are considered. The current investigation is focused on the design and modelling of artificial femur head and bone using finite element analysis (FEA) under various loading conditions, walking posture. Different solid and shell type elements (8 node 183, 8 node 185, 20 node 186, concrete 65, quad 4 node 182, SOLSH 190) are implemented in finite element simulation. The simulation is optimised using convergence test. The possibility of use various elements in the meshed model are explored. Pools of materials are investigated under different walking posture and body weight. The relation of femur head geometry with maximum stress intensity and deflection are investigated. The viability of the proposed FE simulation model is performed. It is observed that variations of the results are within 2 %.

The presence of faults is common in engineering composite structures. Precise detection of faults prior to failure has been the focal point for researchers working in this domain. Detection of damage from the modal characteristics is an age-old promising technique besides the artificial intelligence techniques such as neural network (NN), Artificial neural network (ANN), recurrent neural network (RNN), and particle swarm optimization (PSO) methods. However, multiple detection techniques combined can give better results. In this regard, recurrent neural networks (RNN) and modified particle swarm optimization (mPSO) are combined to detect damages in a glass fiber reinforced polymer (GFRP) composite cantilever beam. The proposed hybrid model consists of two stages; indices of relative natural frequencies applied to locate crack parameters (position and severity) by recurrent neural network technique and optimization of the results (position and severity) by mPSO. The results illustrate this method can be implemented to predict multiple transverse cracks and their parameters in a composite beam, manifested in the change of natural frequencies where the error is limited to 0.97% only.