Application of Neural Networks to Flank Wear Prediction

On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research

“…Influences of cutting conditions are actually eliminated in this way (but only partly, see e.g. [107]). However, an increase of the cutting forces caused by tool wear can (partly) be eliminated as well.…”

Predictive modeling of deposition rate in electro-deposition of copper–tin using regression and artificial neural network

“…Cathode efficiency and deposition rate prediction in electro-deposition of bronze and prediction of tin content in the deposits were carried out using regression and ANN [8,9]. Numerous reports are available on the development of mathematical models relating process variables and bead geometry for the selection and control of the procedural variables [10][11][12] and prediction of tool life [13][14][15][16][17][18]. Models based on neural networks in predicting accurately both surface roughness and tool flank wear in finish dry hard turning have been developed [19] and the neural network models have been compared with the mathematical regression models.…”

Neural Network Multi Layer Perceptron Modeling For Surface Quality Prediction in Laser Machining

“…Five input features were applied to the backpropagating neural network to predict a wear state of light, medium or heavy wear (Wilkinson et al, 1999). Neural network capability in developing a reliable method to predict flank wear during a turning process with the input numeric of tool geometry, depth of cut, cutting speed, federate, workpiece properties, cutting fluid (Lee et al, 1996). The cutting force model for self-propelled rotary tool (SPRT) cutting force prediction using artificial neural networks (ANN) has been described in detail.…”