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Diagnosis and Prediction of Typical Faults of Computer Numerical Control Machine Tools Based on Genetic Algorithm

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DOI: 10.25236/icmmct.2024.026


Yutao Lin

Corresponding Author

Yutao Lin


In this piece of writing, we introduce a distinctive approach for diagnosing and anticipating malfunctions in CNC machine tools, leveraging the Genetic Algorithm (GA). Our objective is threefold: enhancing the precision of CNC machine tools' fault diagnosis and prediction, minimizing maintenance expenses and machine downtime, and boosting overall production efficiency. To accomplish this, we employ a model of optimization technique rooted in GA and subject it to rigorous testing. The experimental results underscore the model's commendable performance, exhibiting low false alarm and misdetection rates, alongside swift diagnosis times. These findings collectively attest to the model's reliability and effectiveness in real-world settings. These results prove that the optimization method of this model is effective. It has high practical value and popularization potential, so as to provide strong support for its wide application in the actual production environment.


Genetic algorithm; Computer numerical control machine; Fault diagnosis