Finding Predictive Features for Energy Consumption of CNC Machines

"With rising energy costs and a growing emphasis on sustainable and efficient production, predicting the energy consumption of CNC machines has become increasingly important. Accurate predictions can lead to significant energy savings, better planning, more informed decision-making, and alignment with smart manufacturing and Industry 4.0 initiatives. Extensive research has been conducted in this area, utilizing both physical and analytical models, as well as expert knowledge from experiments. More recently, machine learning models have also been employed using a wide range of input features. In this paper, we examine the energy consumption of CNC machines by analyzing various features explored in different studies. We propose a method that ranks these features based on their predictive power, then groups the rankings to highlight a small subset of the most predictive features.
Furthermore, we assess the stability of the predictive power of these features over time, allowing us to not only rank them by their predictive strength but also evaluate their long-term stability. Our findings indicate that only a few features are highly predictive, and their predictive power remains consistent over time."