earticle

영어

In machining operations, the extents of important effect of the process parameters like speed, feed, and depth of cut are different for different responses. This paper investigates the effect of process parameters in turning of AA6061 T6 on conventional lathe. The problem appeared owing to selection of parameters increases the deficiency of turning process. Modeling can facilitate the acquisition of a better understanding of such complex process, save the machining time and make the process economic. Thus, the present work clearly defines the development of an artificial neural network (ANN) model for predicting the material removal rate. This study presents a new method to prediction the material removal rate (MRR) on a lathe turning Process. Firstly, Process parameters namely, Spindle speed, depth of cut and feed rate are designed using the Box behnken (DOE) was employed as the experimental strategy. The result shows that the ANN model can predict the material removal rate effectively. This approach helps in economic lathe machining.