Modeling of abrasive flow rotary machining process by artificial neural network

Abrasive flow machining (AFM) is one of the non-traditional machining processes applicable to finishing, deburring, rounding of edges, and removing defective layers from workpiece surface. Abrasive material, used as a mixture of a polymer with abrasive material powder, has reciprocal motion on workpiece surface under pressure during the process. In the following study, a new method of AFM process called henceforth abrasive flow rotary machining (AFRM) will be proposed, in which by elimination of reciprocal motion of abrasive material and the mere use of its stirring and rotation of workpiece, the amount of used material would be optimized. Furthermore, AFRM is executable by simpler tools and machines. In order to investigate performance of the method, experimental tests were designed by the Taguchi method. Then, the tests were carried out and the influence of candidate effective parameters was determined and modeled by artificial neural network (ANN) method. To evaluate the ANN results, they were compared with reported results of AFM. An agreement between our ANN results on predictions of AFRM material removal value and surface roughness was observed with AFM data. The results showed through AFRM, in addition to saving of abrasive material, surface finish is achievable same as AFM’s.