State Monitoring of The Machining Process in Multi-Variety and Small-Batch Production Systems Based on Power Data
DOI:
https://doi.org/10.23055/ijietap.2025.32.2.10415Abstract
Multi-variety and small-batch production is prevalent in today's manufacturing industries, where identifying the operational state is crucial for achieving efficient and effective manufacturing. However, real-time and intrusive monitoring is challenging due to the nature of multi-variety and small-batch production compared to flowline production. In the context of machining systems, power data not only offers insights into energy consumption but also aids in controlling the production process. Taking into account the characteristics of multi-variety and small-batch manufacturing systems, along with the economic and technological viability of collecting power data, a novel state monitoring method based on power data for multi-variety and small-batch production is proposed. First, to bolster the sample size, power data from the machining process is decomposed using wavelet analysis to extract features across three distinct layers. Then a Dynamic Time Warping (DTW) based workpiece recognizer is established, which calculates the features distance between real-time power signal and predefined templates, thereby facilitating workpiece identification. Thereafter, Recurrence Quantification Analysis (RQA) is applied to the Cross Recurrence Plot (CRP) of the real-time power signals and their corresponding template workpiece powers. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then utilized to construct an anomaly detection model, which is fed by the outcomes of the RQA. The validity of this proposed methodology is confirmed through experimental validation. A case study demonstrates that the accuracy rates for workpiece recognition and anomaly detection are 98.40% and 98.8%, respectively. This method addresses the issue of limited sample size and provides an in-depth analysis of the input power in the machining system, making it suitable for state monitoring during the machining process within a multi-variety and small-batch production framework. It also has the potential to support dynamic state monitoring and energy optimization in practical machining system applications.
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