Optimization of cutting parameters in machining of UD-GFRP with PCD tool using NSGA II

Abstract

Fiber glass reinforced plastic was developed in 21st century. GFRP has drastically change the engineering materials and used in various fields like aircrafts, marines and many more, but their machining is quite different from conventional metals. For make any engineering material useable its machining plays a vital role. Since GFRP materials are different in nature its machining is also quite different and to get a perfect finished material all the cutting parameters should be selected carefully as bad parameter prediction leads to poor quality of finished material. Machining (cutting) parameters are essential to attain good quality in the cutting (machining) process. The current research focuses on the process of optimized machining parameters in machining of unidirectional glass fiber reinforced plastic (UD-GFRP) composites. The machining parameters selected for experimental work, were accomplished using Taguchis L18 orthogonal array technique. The parameters selected are rake angle of tool, nose radius of tool, rate of tool feed, speed of cutting, environment used for cutting (dry, wet and cooled) and depth of cut. Since all the parameters can not be used for optimization as only significant parameters need to be optimized. Selection of significant parameters ia also a tedious job for the researcher. This also make the optimization slower if insignificant parameters are selected along with significant ones. Hence out of these six parameters only the significant one are selected to optimize and literature surveys shows that significant parameters vary with the objective-function were selected. Here we selected two objectives, first is to increase the rate of material removal by the tool and second is to decrease the roughness of the targeted surface. So based on these objectives three parameters are selected as significant i.e. rate of feed the tool, speed of cutting and depth of cut. Since here we have two objectives so optimization algorithm with one objective may not serve our purpose, hence we need an optimization algorithm with more then one objective. Hence the Non-dominated Sorting Genetic Algorithm II (NSGA II) is suitable to fulfill our requirements.