As AI transformation expands in manufacturing, intelligent technologies are increasingly applied to CNC machine tools and machining processes. In multi-product, small-batch production environments, frequent product changes require flexible and autonomous process planning. This study proposes a standard data integration-based intelligent process planning system that automatically performs the entire process from 3D model input to NC code generation. To enable intelligent process planning, data across all stages—from feature recognition to machining execution—must be integrated into a unified flow and connected with AI-based decision-making. The proposed system uses an ISO 14649-based XML schema to sequentially link data generated by each module, ensuring standardized information flow. Based on this framework, rulebased feature recognition, constraint-based process planning, and machine learning-based cutting condition optimization are implemented. A prototype system was developed to validate the approach, automatically generating NC code for industrial parts and performing actual CNC machining. Experimental results confirmed the feasibility and validity of the proposed system. This study demonstrates that standardized data integration combined with AI technologies can enable autonomous, flexible, and efficient process planning for advanced manufacturing environments.

Cyber-Physical Production Systems (CPPS), which pursue the implementation of machine intelligence in manufacturing systems, receive much attention as an advanced technology in Smart Factories. CPPS significantly necessitates the selflearning capability because this capability enables manufacturing objects to foresee performance results during their process planning activities and thus to make data-driven autonomous and collaborative decisions. The present work designs and implements a self-learning factory mechanism, which performs predictive process planning for energy reduction in metal cutting industries based on a hybrid-learning approach. The hybrid-learning approach is designed to accommodate traditional machine-learning and transfer-learning, thereby providing the ability of predictive modeling in both data sufficient and insufficient environments. Those manufacturing objects are agentized under the paradigm of Holonic Manufacturing Systems to determine the best energy-efficient machine tool through their self-decisions and interactions without the intervention of humans’ decisions. For such purpose, this paper includes: the proposition of the hybrid-learning approach, the design of system architecture and operational procedure for the self-learning factory, and the implementation of a prototype system.