A Hybrid Information System for Clean Production Management in CNC Milling Using Open Machining Data

This study addresses the integration of sustainability-oriented analytics and decision support in CNC milling through a hybrid information system combining structured data management, sustainability KPIs, rule-based expert reasoning, and machine learning models. The proposed framework is evaluated using the publicly available NASA Ames Milling Tool Wear Dataset. Sustainability indicators related to operational energy demand, tool degradation, and vibration/acoustic-emission response are computed from machining parameters and sensor-derived features. Random Forest and Support Vector Machine models are used for tool wear classification. The expert system applies deterministic rules to identify operational risks, which are combined with machine learning predictions through a hierarchical decision-fusion strategy. Under case-wise cross-validation, the Random Forest achieved a mean classification accuracy of 73.8%, while the Support Vector Machine achieved 68.8%. The expert system most frequently identified elevated vibration-index and acoustic-emission conditions, while critical clean-production risks occurred rarely. Overall, the results demonstrate the feasibility of integrating expert knowledge, sustainability KPIs, and data-driven models into a hybrid information system for decision support in CNC milling environments. The proposed framework provides a foundation for future research on hybrid information systems supporting sustainable manufacturing and intelligent decision making.