Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram. Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, Tamil Nadu, India.
Computer Numerical Control (CNC) machines play a pivotal role in modern precision manufacturing, where real-time monitoring is essential to prevent catastrophic failures and minimize downtime. This study proposes a Fuzzy Logic-Enhanced Expert System (FLEES) for real-time anomaly detection in CNC machines, leveraging linguistic rule inference fused with physical constraints and data-driven optimization. The system processes 14 distinct sensory inputs, including spindle vibration, cutting torque, thermal gradients, and acoustic emissions, gathered from 120 hours of high-frequency CNC machine operation under varying load conditions. Fuzzification maps raw sensor signals to 42 linguistic variables using Gaussian and trapezoidal membership functions. A total of 96 fuzzy rules were formulated based on expert knowledge and refined via Particle Swarm Optimization (PSO) guided by energy consistency and classification loss minimization. Experiments conducted on a benchmark CNC dataset show that FLEES achieves 96.7% anomaly classification accuracy, with 95.3% sensitivity and 97.9% specificity, outperforming existing methods, including SVM (91.2%) and LSTM (94.1%). Moreover, the system maintains a real-time response under 180 milliseconds per inference cycle. These results confirm that integrating fuzzy reasoning with physics-informed optimization enhances reliability and interpretability for real-time fault diagnostics in smart manufacturing.
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The authors confirm contribution to the paper as follows:
Conceptualization: Atulya Gupta, Josphin Mary J, Nandhini I, Thivya Lakshmi R T, Santhi M V B T and Pari R;
Writing- Original Draft Preparation: Atulya Gupta, Josphin Mary J, Nandhini I, Thivya Lakshmi R T, Santhi M V B T and Pari R;
Visualization: Atulya Gupta, Josphin Mary J and Nandhini I;
Investigation: Thivya Lakshmi R T, Santhi M V B T and Pari R;
Supervision: Atulya Gupta, Josphin Mary J and Nandhini I;
Validation: Thivya Lakshmi R T, Santhi M V B T and Pari R;
Writing- Reviewing and Editing: Atulya Gupta, Josphin Mary J, Nandhini I, Thivya Lakshmi R T, Santhi M V B T and Pari R; All authors reviewed the results and approved the final version of the manuscript.
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Atulya Gupta
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad, Uttar Pradesh, India.
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Cite this article
Atulya Gupta, Josphin Mary J, Nandhini I, Thivya Lakshmi R T, Santhi M V B T and Pari R, “Fuzzy Logic-Enhanced Expert System for Real Time Anomaly Detection in CNC Machines”, Journal of Machine and Computing, vol.5, no.4, pp. 2615-2624, October 2025, doi: 10.53759/7669/jmc202505201.
