Cutting Tool Wear Estimation Using a Genetic Algorithm Based Long Short-Term Memory Neural Network

On-line cutting tool wear monitoring plays a critical role in industry automation and has the potential to significantly increase productivity and improve product quality. In this study, we employed the long short-term memory neural network as the decision model of the tool condition monitoring system to predict the amount of cutting tool wear. Compared with the traditional recurrent neural networks, the long short-term memory (LSTM) network can capture the long-term dependencies within a time series. To further decrease the training error and enhance the prediction performance of the network, a genetic algorithm (GA) is applied to find the initial values of the networks that minimize the objective (training error). The proposed methodology is applied on a publicly available milling data set. Comparisons of the prediction performance between the Elman network and the LSTM with and without using GA optimization proves that the GA based LSTM shows an enhanced prediction performance on this data set.