While data-driven models have proven effective in automating machining defects prediction / recognition and tool wear prediction, they are not yet directly deployed to support real-time decision-making and process optimization/control -an important step toward smart manufacturing in industry 4.0. To achieve this, a "smart/self-adaptive" machining system that allows in situ optimization/decision-making based on real time process monitoring data is needed.