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Modern manufacturing is challenged by customized, high-variety and low-volume job orders, dynamic ambient working conditions in shop floors, and stricter requirements on sustainability. Data-driven approach for manufacturing planning, control, and management will be a new research trend to tackle the challenge. In this research, based on in-process monitoring on energy consumption of machining processes and data analytics technologies, an innovative Cyber-physical system (CPS) and Big Data-enabled scheduling optimization system for sustainable computer numerical control (CNC) machining has been developed. This system is augmented with intelligent mechanisms for enhancing adaptability to condition dynamics in machining shop floors. The system consists of scheduling and re-scheduling functions. For scheduling, an artificial neural networks (ANNs)-based algorithm has been designed …