A Review on Energy Efficiency Improvements in CNC Machining

Abstract

Today, the manufacturing industry faces significant pressure to become more sustainable while maintaining competitiveness. In the spirit of the green manufacturing philosophy, numerous efforts have been undertaken to replace or reduce waste materials that burden the environment, and to promote environmentally friendly raw materials. Minimizing energy consumption in CNC machining is also crucial, since it offers significant potential to reduce environmental impact. This review focuses on recent developments in energy consumption monitoring and modelling, energy-efficient machining strategies, energy-saving solutions in CNC machine tools and controllers, and optimized tool path planning algorithms. The first step in implementing energy optimization is modelling the energy consumption of machine tools. Traditionally, the energy demand for cutting has been described using formulas that rely on the material removal rate and specific cutting energy. However, in recent years, data-driven techniques have become more popular, necessitating the development of data monitoring and acquisition techniques. After modelling energy consumption, optimizing cutting parameters is the most straightforward way to increase energy efficiency. However, the complex effect of machining parameters and the quality requirements pose significant challenges. As a result, soft computing methods such as heuristic algorithms and machine learning techniques have become essential and remain the subject of extensive research today. Energy-saving functions of modern CNC machine tools, such as standby mode, auto-shutdown features, and regenerative drives, also play an important role in reducing overall energy consumption. AI-supported tool path generation algorithms have significant potential to improve energy efficiency and sustainability, but this potential is currently underutilized. Future research will presumably focus on intelligent machining technologies using adaptive control and real-time monitoring with predictive optimization methods. These advancements are expected to reduce the environmental impact of CNC machining while enhancing its overall sustainability and productivity.