Pyramid LSTM Network for Tool Condition Monitoring

Abstract

Tool condition monitoring is important to guarantee the product quality and improve the productivity in high-performance computer numerical control (CNC) machining. Due to the harsh working conditions and non-stationary milling process, it is difficult to establish a monitoring model suitable for complex conditions. In this article, a pyramid long short-term memory (LSTM) auto-encoder is proposed to monitor tool wear. Unlike the classic stacked LSTM, the pyramid model is constructed based on the frequency spectrum of cutting signals. Each layer focuses on one periodic scale, and each unit focuses on one periodic fluctuation. The features are compressed layer by layer according to the frequency spectrum. The learned patterns are restricted by the spectrum-based structure, which simplifies the monitoring task and reduces the model complexity. The length of the cutting signal is also no longer limited by the memory capacity of LSTM. In the meantime, the efficiency of long-term signal processing is also greatly improved by reducing the number of units. In addition, the introduction of auto-encoder can further improve the accuracy of the model under complex working conditions through unsupervised learning. The good performance on high-speed milling experiments shows the accuracy of the model under unknown tools and milling parameters. Compared with the classic stacked LSTM, the pyramid LSTM auto-encoder has advantages in computational speed, stability, and prediction accuracy.