Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes

Energy consumption is a critical factor that nega-tively impacts the environment. Sustainable production is essen-tial for addressing the climate crisis, as low-emission manufacturing can both reduce costs and minimize environmental impact. Energy-efficient CNC machine tools significantly contribute to achieving ambitious environmental objectives. In recent years, numerous studies have focused on low-energy consumption production, analyzing factors that contribute to sustainable manufacturing. When using the analytical or empirical model, factors and corrections might be omitted. With advancements in machine learning and the increasing availability of large datasets, models are being developed to predict energy consumption with high accuracy. However, these models often overlook the importance of features that contribute to a transparent prediction process and their influence on the results. In our paper, a LSTM model is initially utilized to predict the energy consumption of CNC machines. Following this, a method is devised to rank the features based on their predictive power, considering temporal variations. We show that some of the features ranked in the top positions agree with independent literature findings, while others are new and demand further investigation.