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Metal forming processes, characterized by high energy consumption, are widely used in modern manufacturing. In this context, methods for monitoring the operational state and cycle-dependent metrics of manufactured parts are essential for implementing energy optimization strategies. Such strategies require moving away from time-aggregated energy assessments, which fail to capture part-level variability, toward analyses at the granularity of individual parts. This article introduces a non-intrusive methodology to enable the identification, in real time, of the part under production and to estimate cycle time and energy consumption per part. The method relies on electrical measurements taken at the switchboards. The RMS current and power values are the inputs to a machine-learning (ML) approach that identifies the part in production. To this end, the time-domain and time–frequency-domain features extracted from the signals are employed to train a Support Vector Machine (SVM) classifier that achieves a test accuracy of 99.9%. Next, the approach estimates cycle time and energy per cycle in real time. Approximately 58,000 production cycles, corresponding to several part types, were characterized. The proposed approach demonstrates that part-level identification and per-cycle energy estimation can be achieved in real time using only electrical measurements in an industrial process.
Gonzalez et al. (Tue,) studied this question.