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Real-time state monitoring of the machining process in multi-variety and small-batch production system based on transfer learning
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DOI：10.1080/0951192X.2026.2640914.png)
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Abstract
En 中文
Multi-variety and small-batch production is widely used in modern manufacturing. Monitoring the machining process in such systems is crucial for improving efficiency and reducing costs. However, most existing monitoring methods rely on intrusive sensors which often require equipment modifications and increase costs. Additionally, many methods can only determine the machining state after all operations on a machine are completed, making real-time monitoring difficult. The small sample size characteristic also limits the effectiveness of conventional machine learning approaches.Therefore, a real-time machining state monitoring method based on transfer learning and power signal is proposed. The input power of the machine tool is used as the original data, whose acquisition is non-intrusive. The power signal is segmented using the Bayesian online change point detection algorithm and converted into recurrence plots for state monitoring model. A transfer learning–based model is then developed to identify machining steps and processes using only a small amount of training data. Power signal features are extracted using frequency domain analysis and recurrence quantification analysis. Finally, machining anomalies are detected using a Z-score variant algorithm. Case studies show the proposed method achieves a monitoring accuracy of 97.1%, enabling effective real-time state monitoring in multi-variety and small-batch production systems.
Keywords:
State monitoring
anomaly detection
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