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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The present investigation focused on abrasive waterjet cutting (AWJC) of natural fibre reinforced nano clay filled polyester composites with the objectives of maximizing material removal rate ( MRR) and minimizing the kerf taper ( KT) and surface roughness ( Ra). The influence of nano clay addition, traverse speed (TS), jet pressure (JP) and stand-off distance (SOD) on the AWJC characteristics of fabricated composite laminates are investigated. The natural fibre reinforced composite (NFRC) laminates are fabricated through hand lay-up technique through varying the wt% of nano clay fillers (0, 1 and 2). The AWJC experiments are planned and rigorous experiments were performed by adopting box-behnken design approach. The relative consequence of process variables on response features and quadratic regression models were assessed through analysis of variance (ANOVA). Further, multiple response optimization is carried out using statistical desirability technique to enhance the cut quality characteristics. The optimal AWJC parameters such as JP of 316.24 MPa, SOD of 2 mm and TS of 304.24 mm/min with 1.15 wt% of nano clay addition are determined. Microstructure of cut surface is examined to ascertain the morphological behaviour of AWJC surfaces with different processing conditions.

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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