State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model

Ultra-precision machine tools are the foundation for ultra-precision manufacturing. In the era of Industry 4.0, monitoring the machine tool’s working condition is critical to control the machining quality. In a conventional setting, numerous sensors are retrofitted to the machine to monitor its condition effectively. This process could potentially increase the cost of the widespread application of Industry 4.0 technologies. In contrast to the method of retrofitting the machine tool, in this work, we propose an intelligent monitoring system that utilizes the equipment’s power consumption data to assess and determine the equipment states. The work also discusses the development of a G-code interpreter application used to develop an equipment working status matrix. The G-code interpreter application can generate the training data and extract features for the Deep Learning/Machine learning models. The feature extraction process can also be customized by providing template functions to the application. A densely connected convolutional neural network with multiple outputs was then developed to identify the machine state and predict the feedrate simultaneously. The model was able to identify the working component of the machine with an accuracy of $$\sim $$ ∼ 94% and was able to predict the feed rate with a standard deviation ( $$\sigma $$ σ ) of 21.900 from the energy consumption data. The overarching goal of the research work is to predict energy consumed, and augment anomaly detection for an ultra-precision CNC machine tool. The work presented here involves the identification of the equipment state and prediction of the equipment feedrate, and it will serve as a precursor.