SmartCNC AI: An Industry 4.0 Machine Digital Twin Framework for CNC Condition Monitoring and AI-Based Predictive Maintenance

Author

Aryan Bhole, Suraj Gond, Deepak Naik, Prof. Satish Kuchiwale


Abstract

 Computer numerical control (CNC) machines are central to modern manufacturing, and unplanned downtime, tool breakage, and spindle or bearing failure are costly. Industry 4.0 technologies allow machine condition to be sensed, transmitted, and analysed continuously. A stream of sensor readings, or an alarm triggered by a threshold, is not by itself a machine digital twin, and a prototype that has not been validated must never be used to make safety-critical decisions. This paper describes SmartCNC AI, a proposed framework based on the Industrial Internet of Things (IIoT) that connects timestamped vibration, current, temperature, and machine-state observations to a structured virtual machine state, keeps recent history together with data-quality metadata, and supports review of anomalies and maintenance recommendations through transparent baselines and candidate machine-learning methods. The planned prototype pairs an ESP32-class edge controller with a MEMS accelerometer, a spindle-current sensor, and a temperature sensor, while further channels such as acoustic sensing and controller data via MTConnect or OPC UA remain optional and subject to verification. The study plan keeps offline analysis of public datasets distinct from later hardware testing. The candidate datasets, the NASA milling tool-wear dataset and the AI4I 2020 predictive maintenance dataset, carry labels, machines, and operating conditions that do not represent every real shop floor. The paper sets out an architecture, a data schema, an evaluation protocol, implementation milestones, and safety and data-governance safeguards. At this stage no physical prototype, trained model, or experimental result is claimed. The intended contribution is a reproducible evaluation framework for testing whether a machine-specific, context-aware baseline offers value beyond fixed-threshold condition monitoring on a clearly defined task.


Keywords

Industry 4.0, Industrial IoT, CNC Machine, Predictive Maintenance, Condition Monitoring, Anomaly Detection, Digital Twin, Tool Wear



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References


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