Neural Evaluation of Friction and Flow Stress Adaptive to Ring Geometry

Interfacial friction and material flow stress can be evaluated through the use of calibration curves in ring compression testing. In this study the neural network approach has been extended to their evaluation adaptive to ring geometries of wider range. The ring geometries covered were in the range of 6:3:0.5 to 6:3:2 (OD: ID: T₀), which are the most commonly used values. Data for training the networks were acquired in the same way as in the development of the calibration curves. A serial scheme for the evaluation was found to be effective when multilayered BP (backpropagation) networks were employed. Network construction, network training including the selection of learning parameters, and implementation of the trained network are also detailed in this paper. Predictions for different ring geometries and friction factors were conducted and satisfactory results were obtained with prediction error of about 5%, at maximum, for both friction and flow stress.