Modeling the Surface Roughness and Tool Wear for Turning Of GFRP Composites Using Design of Experiments

In recent years, the utilization of GFRP composite materials in many different engineering fields has undergone a tremendous increase. Accordingly, the need for accurate machining of composites has increased enormously. However, the knowledge acquired in machining of ductile metals is not suitable for composites. To make an effective use of composites, it is essential that a high degree of confidence be achieved in predicting the process state variables. This work has been taken up with a view to bring out the benefits in developing a model for surface roughness and tool wear prediction in terms of speed, feed, depth of cut and work piece (fiber orientation angle) in composite material. Fiber orientation angle is the variable, which is specially applied to the fiber reinforced composite material. Experiments were conducted as per full factorial experimental design. Regression analysis is used to develop the model. Sum of squares are calculated using Yates method. Analysis of variance is used to study the effect of process parameters on machining process. The developed models can be used to predict the surface roughness and tool wear for turning of GFRP composites within the ranges of variable studied. This procedure eliminates the need for repeated experiments, time and material consumption by the use of conventional procedure.