난삭재 절삭공정 시 인공지능 기반 실시간 공구 상태 판단을 위한 예측 시스템 설계 및 구현
Cutting processes are essential in manufacturing metal components, particularly in aerospace applications. For difficult-to-cut materials such as titanium alloys, tool condition significantly impacts machining quality, making real-time failure prediction critical. While many studies have applied art...
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| Published in | 한국생산제조학회지, 34(4) pp. 225 - 233 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | Korean |
| Published |
한국생산제조학회
01.08.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2508-5093 2508-5107 |
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| Summary: | Cutting processes are essential in manufacturing metal components, particularly in aerospace applications. For difficult-to-cut materials such as titanium alloys, tool condition significantly impacts machining quality, making real-time failure prediction critical. While many studies have applied artificial intelligence (AI) to analyze sensor data for predictive maintenance, real-time AI implementation during cutting remains limited. This study presents a real-time tool failure detection system capable of executing an AI model during machining. Titanium alloy was machined using an end mill, and an acceleration sensor was mounted on the machine tool for condition monitoring. Data acquisition, preprocessing, and transmission to the AI model were handled via LabVIEW. The AI model classified the tool condition into four states and was integrated with the monitoring system through TCP/IP communication for real-time prediction.
The system was validated through cutting experiments, demonstrating its effectiveness for real-time tool condition monitoring in high-performance machining environments. KCI Citation Count: 0 |
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| ISSN: | 2508-5093 2508-5107 |