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.

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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