Natural fiber-reinforced composites are recognized as better materials for structural components due to their inherent properties. However, milling these materials presents a number of problems, such as surface delamination and surface roughness (Ra), which appear during the machining process, associated with the characteristics of the material and the cutting parameters. In order to reduce these problems we present this study with the objective of evaluating the cutting parameters (cutting velocity and feed rate) and the influence of the fibers under delamination factor (Fd) and surface roughness (Ra). An experimental plan, based on Taguchi techniques and on the analysis of variance (ANOVA), was established considering milling with prefixed cutting parameters in Natural Fiber-Reinforced Plastic (NFRP) composite materials using cemented carbide end mill. The results of NFRP composite were compared with Glass Fiber-Reinforced Plastic (GFRP) composites. The objective was to establish a model using multiple regression analysis between cutting velocity and feed rate with the delamination factor (Fd) and surface roughness (Ra) of different fiberreinforced laminates.

In the present work, the damping behavior of multiwall carbon nanotubes/polymer nanocomposites has been studied by aligning the carbon nanotubes (CNTs) in the matrix using DC electric field during the curing of composite. Nanocomposite specimens have been fabricated for three different CNT loadings (0.1, 0.2, and 0.3 wt%) to prepare two types, namely randomly oriented and aligned. The alignment of nanocomposites has developed a strong anisotropy in the composite. Microstructural analysis and electrical conductivity tests are carried out to examine the CNTs alignment. It is evident from the obtained results that a significant improvement is achieved in electrical conductivity of aligned nanocomposites over randomly oriented one. Dynamic mechanical analysis and flexural vibration experiments have been performed to study the frequency dependent damping behavior of nanocomposites. In case of aligned nanocomposites, significant enhancement is noticed in the material damping for the tested frequency band width of 0-30 Hz; also the flexural tests have shown an improvement up to 37% in structural damping compared to randomly oriented. The influence of CNT coating on fiber in improving anisotropic nature of fiber has been studied experimentally and it is found that the coating of CNT reinforced resin has enhanced simultaneously the straining capability and anisotropic nature of the fiber, without compromising its mechanical properties. Further, the vibration damping behavior of the CNT coated fiber is evaluated using digital image correlation technique and it is noticed that CNT dispersion on the fiber improves its damping ability by 2.6%.

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In this work, a machine vision system has been utilized to determine the surface roughness of the milled surfaces. For checking the effectiveness of the machine vision based results, a wide range of surface roughness were generated on CNC milling centre using Design of Experiments (DoE) technique. Stylusbased parameters Ra and Rsm were acquired and compared with vision-based parameters (Ga, R1, R2, CV, contrast etc). Model equations have been developed, in terms of the machining parameters, image parameters and machining and image parameters using response surface methodology on the basis of experimental results. The experimental result indicates that the surface roughness could be estimated/predicted with a reasonable accuracy using machine vision

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