Prediction model for surface layer microhardness of processed TC17 via high energy shot peening
(School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China)
Abstract: The bulk TC17 was subjected to the high energy shot peening (HESP) at the air pressures ranging from 0.35 to 0.55 MPa and processing durations ranging from 15 to 60 min. The microhardness (HV0.02) from topmost surface to matrix of the HESP processed TC17 was measured, which generally decreases with the increase of depth from topmost surface to matrix and presents different variation with air pressure and processing duration at different depths. A fuzzy neural network (FNN) model was established to predict the surface layer microhardness of the HESP processed TC17, where the maximum and average difference between the measured and the predicted microhardness were respectively 8.5% and 3.2%. Applying the FNN model, the effects of the air pressure and processing duration on the microhardness at different depths were analyzed, revealing the significant interaction between the refined layer shelling and the continuous grain refinement.
Key words: TC17; high energy shot peening; microhardness; fuzzy neural network; model