Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model
The quality of the processed products in CNC machining centers is a critical factor in manufacturing equipment. The anomaly detection and predictive maintenance functions are essential for improving efficiency and reducing time and costs. This study aims to strengthen service competitiveness by redu...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/7/1093 |
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| author | Jintak Choi Zuobin Xiong Kyungtae Kang |
| author_facet | Jintak Choi Zuobin Xiong Kyungtae Kang |
| author_sort | Jintak Choi |
| collection | DOAJ |
| description | The quality of the processed products in CNC machining centers is a critical factor in manufacturing equipment. The anomaly detection and predictive maintenance functions are essential for improving efficiency and reducing time and costs. This study aims to strengthen service competitiveness by reducing quality assurance costs and implementing AI-based predictive maintenance services, as well as establishing a predictive maintenance system for CNC manufacturing equipment. The proposed system integrates preventive maintenance, time-based maintenance, and condition-based maintenance strategies. Using continuous learning based on long short-term memory (LSTM), the system enables anomaly detection, failure prediction, cause analysis, root cause identification, remaining useful life (RUL) prediction, and optimal maintenance timing decisions. In addition, this study focuses on roller-cutting devices that are essential in packaging processes, such as food, pharmaceutical, and cosmetic production. When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments. |
| format | Article |
| id | doaj-art-4b9f456bdd344b5d9d73dd313f1bb913 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-4b9f456bdd344b5d9d73dd313f1bb9132025-08-20T03:06:24ZengMDPI AGMathematics2227-73902025-03-01137109310.3390/math13071093Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction ModelJintak Choi0Zuobin Xiong1Kyungtae Kang2Department of Applied Artificial Intelligence (Major in Bio Artificial Intelligence), Hanyang University, Ansan 15588, Republic of KoreaDepartment of Computer Science, University of Nevada, Las Vegas, NV 89154, USADepartment of Artificial Intelligence, Hanyang University, Ansan 15588, Republic of KoreaThe quality of the processed products in CNC machining centers is a critical factor in manufacturing equipment. The anomaly detection and predictive maintenance functions are essential for improving efficiency and reducing time and costs. This study aims to strengthen service competitiveness by reducing quality assurance costs and implementing AI-based predictive maintenance services, as well as establishing a predictive maintenance system for CNC manufacturing equipment. The proposed system integrates preventive maintenance, time-based maintenance, and condition-based maintenance strategies. Using continuous learning based on long short-term memory (LSTM), the system enables anomaly detection, failure prediction, cause analysis, root cause identification, remaining useful life (RUL) prediction, and optimal maintenance timing decisions. In addition, this study focuses on roller-cutting devices that are essential in packaging processes, such as food, pharmaceutical, and cosmetic production. When rolling pins are machining with CNC equipment, a sensor system is installed to collect acoustic data, analyze failure patterns, and apply RUL prediction algorithms. The AI-based predictive maintenance system developed ensures the reliability and operational efficiency of CNC equipment, while also laying the foundation for a smart factory monitoring platform, thus enhancing competitiveness in intelligent manufacturing environments.https://www.mdpi.com/2227-7390/13/7/1093CNC machining centersf-AnoGANLSTMlatent ODEVAECBM |
| spellingShingle | Jintak Choi Zuobin Xiong Kyungtae Kang Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model Mathematics CNC machining centers f-AnoGAN LSTM latent ODE VAE CBM |
| title | Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model |
| title_full | Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model |
| title_fullStr | Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model |
| title_full_unstemmed | Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model |
| title_short | Long Short-Term Memory-Based Computerized Numerical Control Machining Center Failure Prediction Model |
| title_sort | long short term memory based computerized numerical control machining center failure prediction model |
| topic | CNC machining centers f-AnoGAN LSTM latent ODE VAE CBM |
| url | https://www.mdpi.com/2227-7390/13/7/1093 |
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