A Hybrid Neural Network for Prediction of Surface Residual Stress in MQL Face Turning
Record type:
paper
Creator:
XiaAleManYa
Publisher:
Tra
Host:
Surface integrity, such as surface roughness and residual stress, is an aspect of surface quality on machined parts. Residual stress in the machined surface and subsurface is affected by materials, machining conditions, and tool geometry. These residual stresses could affect the service qualify and component life significantly. Residual stress can be determined by empirical or numerical experiments for selected configurations, even if both are expensive procedures. This paper presents a hybrid neural network that is trained using Simulated Annealing (SA) and Levenberg-Marquardt Algorithm (LM) in order to predict the values of residual stresses in cutting and radial direction after the MQL face turning process accurately. To verify the performance of the proposed approach, the predicted results are compared with the results obtained by training an ANN using SA and LM separately. The results have shown that the hybrid neural network outperforms SA and LM in predicting machining induced surface integrity that is critical to determine the fatigue life of the components.
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