How Machining State Is Driving Energy Performances in AI-Guided Industries?

Machining state, particularly tool wear and CNC (Computer Numerical Control) operational phases, significantly influences energy consumption in AI-guided manufacturing systems. This study maps the literature linking machining energy behavior, tool wear and predictive analytics to make clear how energy profiles can support maintenance decisions. Authors conducted a bibliometric analysis of 483 Scopus-indexed publications, finally selecting 32 core studies revealing how energy signals can act as non-intrusive indicators of tool condition and process dynamics. Accordingly, machining parameters and wear progression can be used, jointly, to shape energy cycles, while machine-learning models can enable reliable prediction of tool wear and remaining useful life from energy data. Prediction-oriented approaches demonstrate that energy-based monitoring has the potential to support anticipatory tool replacement, energy efficiency, and tool-life extension without additional sensors. Energy-driven predictive maintenance is emerging as a relevant mechanism for managing energy performance in AI-driven machining environments.