Surface roughness parameter prediction and evaluation are important factors in determining the satisfactory performance of machined surfaces in many fields. The recent trend towards the measurement and evaluation of surface roughness has led to renewed interest in the use of newly developed non-contact sensors. In the present work, an attempt has been made to measure the surface roughness parameter of different machined surfaces using a high sensitivity capacitive sensor. A capacitive response model is proposed to predict theoretical average capacitive surface roughness and compare it with the capacitive sensor measurement results. The measurements were carried out for 18 specimens using the proposed capacitive-sensor-based non-contact measurement setup. The results show that surface roughness values measured using a sensor well agree with the model output. For ground and milled surfaces, the correlation coefficients obtained are high, while for the surfaces generated by shaping, the correlation coefficient is low. It is observed that the sensor can effectively assess the fine and moderate rough-machined surfaces compared to rough surfaces generated by a shaping process. Furthermore, a linear regression model is proposed to predict the surface roughness from the measured average capacitive roughness. It can be further used in on-machine measurement, on-line monitoring and control of surface roughness in the machine tool environment.

This research work addresses the influence of graphene and basalt filler on mechanical properties and free vibration behavior of banana/sisal hybrid composite. Banana/sisal hybrid composites were prepared with three weight percentage (wt.%), 6 wt.% of graphene, and basalt filler by a compression molding process. The improvement in tensile strength of 24.8% and 30% was noticed for the basalt (6 wt.%) and graphene (6 wt.%) filler addition, respectively. Comparing with basalt addition, graphene addition provides an 1.5 times improvement in flexural strength. The tensile fractography was also carried out and studied the interfacial bonding of the composite. From the morphology, it was observed that there was a good interfacial adhesion between the fiber and the matrix which enhance the mechanical property of the hybrid composite. The free vibrational behavior of the hybrid composite has also been analyzed. The modal analysis shows the enhanced natural frequencies and modal damping for the addition of 6 wt.% of graphene filler in the hybrid composite.

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Natural-fibre reinforced composite material is an emerging material that has great potential to be used in various industrial aspects and applications. The cotton-viscose-reinforced composite is prepared using a compression moulding process. In addition to it, analysis of its mechanical properties was also carried out, such as tensile strength, flexural strength, impact strength and hardness. An attempt was made to process the prepared composite material using abrasive water jet machining (AWJM) under different process parameters (water pressure, nozzle transfer speed and abrasive flow rate) levels to determine the better suitable process conditions to achieve the better surface finish and optimize the machining process. The significance of the optimization process was ensured using the results of the analysis of variance. Morphological analyses of the machined surface were performed using a scanning electron microscope. The surface roughness of 8.28 µm was found to be the optimized process parameter. Optimum process parameters in AWJM are used to improve the surface quality.

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Wide spread applications of composite materials have been significantly growing in aerospace, naval, space, and automotive industries. Drilling of such materials is a challenging task because of differential machining properties and checking the quality of hole is significantly a great attention. In this paper, experimental investigation on prediction of hole quality characteristics of aluminum matrix composite (AMC225xe) during drilling process. The influence of process parameters such as speed, feed rate and coolant flow rate on the surface finish and circularity were investigated during the experimentation. The experiments were conducted according to the Taguchi’s L9 array design using process parameters. The quality of the hole characteristics were measured using roughness tester and CMM. Regression analysis has been carried out for prediction of hole quality characteristics from the experimentation. It is observed that the predicted results are good correlation with measured values. Also, the results indicate that the feed rate is the most influencing parameter for drilling of AMC225xe.