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    • br Experimental procedures br General characterizations br R

    2018-11-12


    Experimental procedures
    General characterizations
    Results
    Discussion The silane surface treatment of glass fibre by aqueous solution method results improved ILSS in layered composite. The FTIR spectra results confirmed the presence of NH2 functional group on fibre which improves the bonding between fibre and resin. Scheme 1&2 show the reaction between silane & fibre and silane treated e-glass fibre & epoxy respectively. The amine functional group on fibre surface reacted with epoxide group thus formed covalent bonding [21]. The reacted fibre transferred load from matrix and reduces stress concentration on matrix. Similarly good bonding between fibre and epoxy improved delamination resistance of laminates in comparison with other surface treated fibres. Treating the fibres by 69 9 (H2SO4) and base (NaOH) the outer surface of fibre get leached and also the surface is not chemically altered with any functional groups. Hence this leads to uneven reduction of fibre thickness and no improvement in adhesion behavior [5,22]. When fibre diameter reduced it affects fibre\'s total surface area. When these fibres were subjected to external shear, the load bearing capacity of fibre become poor hence layers get delaminated. Whereas in silane treatment the silane covers the fibre as a cap and no leaching of fibre was taken place. After silane treatment fibres were chemically active with no reduction on surface area. From Fig. 4 it is understood that under loading, the untreated fibres were pulled out form matrix surface which indicates poor bonding of fibre. Whereas in silane treated fibre epoxy composites the fracture indicates predominant fibre breakage which indicates improved adhesion of fibres with matrix. Thus silane treatment on e-glass fibre improved bonding behavior of laminates than other types of surface treatments [23]. The optical microscopic images of drilled hole surfaces of maximum fibre loaded (40vol%, 5 ply) composite shows, delamination characters in untreated and acid treated fibre-epoxy composites. It is observed that while drilling process carried out at high speed (1400 rpm) the untreated fibre and acid treated fibre-epoxy system got delaminated on the edge of holes due to inability to withstand the shear force developed [24]. The force concentration on drill tool tip was quite larger because of low contact area of drill tool on work piece. This phenomenon created unbalanced forces on top surface of work piece and created top layer delamination. Whereas in silane treated fibre-epoxy system, excellent dimensional stability was achieved even at high drilling speeds. This is because of excellent adhesion of fibre with epoxy matrix. The unbalanced forces in drilling process could not pull the fibres outside. When pulling force was applied on fibres they tear out and maintain the edges with smooth. The inner hole optical microscopy images reveals that inner surfaces are fairly smooth during the penetration of the tool through thickness, the cutting forces are balanced and tend to be uniform. Hence delaminations in such places are minimum.
    Conclusions
    Introduction ‘Path planning and control’ of an autonomous mobile robot in an unknown dynamic environment is one of the most challenging jobs. Fuzzy logic is a mimic of human behavior, which easily handles the system uncertainty. One of the most cited methods in the field of the mobile robot is the fuzzy logic. Soft computing techniques such as fuzzy logic [1], neural network [2], neuro-fuzzy [4] and nature-inspired algorithms (Genetic Algorithm [8], Particle Swarm Optimization [12,13], Ant Colony Algorithm [10,11], Simulated Annealing Algorithm [14,15], Bacterial Foraging Optimization [5]) are widely used for mobile robot navigation. However, each method (algorithm) has its strengths and weaknesses. The motion control problem of an autonomous wheeled mobile robot has been widely investigated in past two decades. Abadi and Khooban [1] have introduced Mamdani-type fuzzy logic controller integrated with random inertia weight Particle Swarm Optimization (RNW-PSO) for optimal path tracking of wheeled mobile robots (WMRs). Algabri et al. [2] have combined the fuzzy logic with other soft computing techniques such as Genetic Algorithm (GA), Neural Networks (NN), and Particle Swarm Optimization (PSO) for optimizing the membership function parameters of the fuzzy controller to improve the navigation performance of the mobile robot. A comparative study between two soft computing approaches, namely genetic-fuzzy and genetic-neural and the conventional potential field method have been designed and developed by Hui and Pratihar [3] for an adaptive navigation planning of a car-like mobile robot moving in the presence of some dynamic obstacles. Pothal and Parhi [4] have proposed the sensor based Adaptive Neuro Fuzzy Inference System (ANFIS) controller for navigation of single and multiple mobile robots in the highly cluttered environment.