A Computer Algorithm for Flank and Crater Wear Estimation in CNC Turning Operations

In order to increase the productivity of turning processes, several attempts have been made in the recent past for tool wear estimation and classification in turning operations. The tool flank and crater wear can be predicted by a number of models including statistical, pattern recognition, quantitative and neural network models. In this paper, a computer algorithm of new quantitative models for flank and crater wear estimation is presented. First, a quantitative model based on a correlation between increases in feed and radial forces and the average width of flank wear is developed. Then another model which relates acoustic emission (AErms) in the turning operation with the flank and crater wear developed on the tool is presented. The flank wear estimated by the first model is then employed in the second model to predict the crater wear on the tool insert. The influence of flank and crater wear on AErms generated during the turning operation has also been investigated. Additionally, chip-flow direction and tool–chip rake face interfacing area are also examined. The experimental results indicate that the computer program developed, based on the algorithm mentioned above, has a high accuracy for estimation of tool flank wear

On-Line Tool Wear Estimation in CNC Turning Operations using Fuzzy Neural Network Model

In recent past, several neural network models which employ cutting forces and AErms or their derivatives for estimation as well as classification of flank wear have been developed. However, a significant variation in mean cutting forces and AErms at the start of cutting operation for similar new tools can result in estimation and classification error. In order to deal with this problem, a new on-line fuzzy neural network (FNN) model is presented in this paper. This model has four parts. The first part of the model is developed to classify tool wear by using fuzzy logic. The second part of this model is designed for normalizing the inputs for the next part. The third part consisting of modified least-square backpropagation neural network is built to estimate flank and crater wear. The development of forth part was done in order to adjust the results of the third part. Several basic and derived parameters including forces, AErms, skew and kurtosis of force bands, as well as the total energy of forces were employed as inputs in order to enhance the accuracy of tool wear prediction. The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time

The Total Energy and the Total Entropy of Force Signals - New Parameters for Monitoring Oblique Turning Operations

In unmanned CNC turning operations, the accuracy of tool wear predictions is very important for accurate tool replacement policies and avoiding unnecessary tool insert changes. This paper introduces two new parameters, namely the total energy and the total entropy of force signals, for tool condition monitoring. The correlation between the new parameters, tool wear and a wide range of cutting conditions is examined. The experimental results show that the energy of force signal can be reliably used to monitor tool flank and crater wear over a wide range of cutting conditions. However, the total entropy of forces does not appear to be sensitive to feed rate, rake angle and tool wear. The experimental results also indicate that crater wear causes an increase in the effective rake angle resulting in lower total energy of forces. For some particular shapes of worn tool, however, the crater wear results in a decreased rake angle which increases the total energy of forces. The influence of crater wear on forces and the root mean square of acoustic emission (AErms) signals is also observed in this research