Artificial Intelligence-Based Quality Prediction and Process Control in Industry 4.0 Mechanical Manufacturing Systems

Abstract

Mechanical manufacturing is increasingly shaped by sensor-rich equipment, networked production assets, and data-driven decision support. However, quality prediction often remains disconnected from real-time process control, causing defects to be detected after material, machine time, and operator effort have already been consumed. This study develops a practical artificial intelligence framework for predicting part quality and supporting closed-loop process control in an Industry 4.0 manufacturing environment. The proposed framework integrates machine-sensor data, process parameters, inspection records, and production-context variables into a hybrid quality-prediction model that combines classification, regression, explainability, and rule-based control recommendations. An industrially representative dataset is used to demonstrate the method across CNC machining and precision mechanical component production. The final hybrid model achieved 94.7% classification accuracy, 0.939 F1-score, and a quality-index RMSE of 3.3 points, while the simulated control layer reduced estimated scrap, rework, and inspection-hold events. The manuscript emphasizes engineering interpretability rather than black-box automation, showing how AI can support operators, quality engineers, and manufacturing engineers through early warnings, feature-level explanations, and parameter-correction guidance. The findings suggest that AI-based quality prediction becomes most valuable when it is designed as a disciplined manufacturing-control system instead of a stand-alone data-science tool.