Ultra-precision CNC machine tools play a significant role in the machining of precision dies and molds, optics, consumer electronics, etc., Due to the nature of ultra-precision machining, a subtle change in process condition, machine anomalies, etc. may significantly influence the machining outcome. Hence, continuous monitoring of the equipment’s operation is required to better understand the variations associated with the process and the machine. The conventional monitoring platform requires comprehensive data analysis using multiple sensors, and controller data to detect, diagnose, and prognose machine and process conditions. This increases the cost and complexity of installing a monitoring platform. The energy consumption data contains valuable information that could be potentially used to identify machine and process variations. The information can also be used to develop potential energy-saving strategies in an effort towards Green Manufacturing. This paper proposes an intelligent energy monitoring method using a 1-dimensional convolutional neural network (1D-CNN) to effortlessly and accurately obtain the working status information of the machine with minimal retrofitting. The 1D-CNN uses the energy consumption data to determine the equipment’s operation status by identifying the working components and the feed rate of the moving axis. The hyper-parameters of the developed model were optimized to improve the prediction accuracy. The paper also compares different Deep Learning and Machine Learning algorithms to gauge their effective performance in this application. Finally, the model with the highest accuracy was validated on a 5-axis ultra-precision CNC machine tool. Results show that 1D-CNN performs better than multi-layer neural networks and machine learning algorithms in processing time-series datasets. The classification accuracy of 1D-CNN on the detection of operation status and feed rate of each axis can reach 95.7 and 91.4%, respectively. Further studies are currently in progress to improve prediction accuracy of the model, and to detect subtle changes in energy consumption which would enable identification of the machine and process anomalies.

The feasibility of using deep learning techniques in industrial cold forging for fault detection was investigated. In this work, vibration data were collected from an industrial setting to detect machine conditions resulting in defective products (faults). After collecting data from several commonly encountered faults, a Convolutional Neural Network classifier detected fault conditions with 99.02% accuracy and further classified each fault with 92.66% accuracy. A decision tree (DT) model was also used in an attempt to detect and classify faults using time domain features. The model was able to detect faults with 92.5% accuracy but was unable to classify them. In addition, DT feature importance analysis was performed to understand how various faults impacted the machine signal for future refinement of the proposed system. The results suggest that the proposed deep learning method has the potential to detect faults in cold forging, but future work is required to validate the method.