Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

Abstract

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

“…This allows processes to be monitored from part one, meaning that it is no longer necessary to determine reference signals beforehand by recording them. For this purpose, the proposed framework is tested on the approach introduced in [33]. In the presented hybrid model, an ML model maps previously simulated kinematic quantities such as axis-specific velocities, accelerations, and process forces onto the current signals of the X, Y, and Z-axes and the main spindle (SP) of a milling machine with a target frequency of 50 Hz.…”

Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability

“…Ströbel et al [7] developed a model to predict the energy consumption during milling for the individual axes. The machine behaviour is first predicted based on the NC code.…”

“…The input variables of the network are the acceleration a i and velocity v i for all four axes, the calculated forces in all three spatial directions, and the spindle force. Furthermore, the material removal rate is relevant [7].…”

“…The measurement data used in this thesis were recorded in [7]. For this purpose, milling tests were carried out on a CMX 600 V milling machine from the manufacturer DMG Mori.…”

“…Additional extensions also address a cloud environment to monitor production and optimally distribute jobs, intending to enhance the determination of milling parameters for energy efficiency [6]. The positioning of the workpiece regarding energy consumption has also become a focus of current research with the target to develop an optimization model for workpiece positioning to minimize axis-specific energy consumption [7]. However, these approaches often lack a holistic perspective, failing to consider the inter-dependencies between various machining parameters and their cumulative impact on energy consumption and environmental sustainability.…”