Abstract
In the milling process, a rotating cutting tool is used to cut the raw material into the desired shape. Since tool breakage adversely affects productivity, real-time tool breakage detection is required. In this study, a tool breakage monitoring system using AE signals and a deep learning model was investigated. First, LSTM-Autoencoder was constructed and trained using the AE signal, cutting speed, spindle speed, and depth of cut as input data. In order to distinguish between tool normality and anomalies, the largest value among the normal cutting data set was determined as the threshold to determine if the tool was broken. As the result of the experiment, we obtained the accuracy of 82.1% during normal cutting, but the accuracy was significantly reduced to 63.1% and 63.6% at the time of entry/exit. This is because the AE value that occurs during normal entry/exit is so large that it is mistaken for breakage. To overcome this problem, a combined model that uses both LSTM-Autoencoder and Gaussian Mixture Model was developed. First LSTM-Autoencoder was used to determine the breakage, and then Gaussian Mixture Model was used to determine the authenticity of the breakage. As a result of the experiment using the developed model, 52 out of 57 cuttings including entry/exit cutting were detected as failures, showing a high reliability of 91.2%, proving the superiority of the combined model.
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This work was supported by the 2018 Research Fund of the University of Seoul.
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Nam, J.S., Kwon, W.T. A Study on Tool Breakage Detection During Milling Process Using LSTM-Autoencoder and Gaussian Mixture Model. Int. J. Precis. Eng. Manuf. 23, 667–675 (2022). https://doi.org/10.1007/s12541-022-00647-w
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DOI: https://doi.org/10.1007/s12541-022-00647-w