Tool tip position estimation with artificial intelligence techniques in the industry 4.0 era: An example of digital transformation in industry

Thanks to the advancement in the processing of sensor data collected from machines through digital transformation, great efforts have been made in recent years to predict machine tool breakage and wear that may cause production to stop before they occur. Inferences can be made from the data of cutting tools in production using artificial intelligence algorithms. The main motivation for this study is the tools used for machining. During the production of the tools, data is received from the machine with the help of sensors. Estimation of the locations where the location data will go on the three axes, which are among the selected discriminative features, was made using LSTM, RNN and ARIMA algorithms. These three algorithms were compared using their error rates. The LSTM algorithm has the lowest mean absolute error and root mean square error rates. According to the results obtained, extending equipment life, increasing equipment efficiency and preventing production disruption by predicting tool tip wear is possible with the hybrid approach of discriminative feature selection and deep learning-based prediction.