Optimization of Machining Parameters for Minimizing Surface Roughness in Turning GFRP Composite Using ANN and PSO Methodology

Abstract

The influence of surface roughness in determining the quality of finished products in any industrial application has an enormous impact on gaining competitive edge and establishing superiority. Thus, recognizing and understanding the factors influencing the resulted surface roughness are the crucial issues helping to achieve the desired goal in any competitive industrial environment. Fact is that machining process parameters are major factors affecting the outcome. This research is focused on determining the optimum machining parameters (cutting speed, feed rate, depth of cut) which result in minimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To understand the effects of machining parameters on surface roughness and to determine relationship between them; Particle Swarm Optimization (PSO) has been employed. A multiple regression equation is used as objective function to determine the optimum values of inputs (cutting speed, feed, and depth of cut) using PSO formula and it yields an optimum value of surface roughness of 0.6252 µm. Artificial Neural Network (ANN) has also been implemented to predict various level of surface roughness for different machining parameters. To predict the surface roughness (Ra), standard multilayer feed-forward back-propagation hierarchical neural network has been applied and the findings provide an overall value of coefficient of determination of 0.88881. These investigations of turning operation provide optimal process parameters for any desired value of surface roughness which result in gaining a competitive edge over others in any industrial application.