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- Title
Tool Wear Monitoring System Using Seq2Seq.
- Authors
Jeon, Wang-Su; Rhee, Sang-Yong
- Abstract
The advancement of smart factories has brought about small quantity batch production. In multi-variety production, both materials and processing methods change constantly, resulting in irregular changes in the progression of tool wear, which is often affected by processing methods. This leads to changes in the timing of tool replacement, and failure to correctly determine this timing may result in substantial damage and financial loss. In this study, we sought to address the issue of incorrect timing for tool replacement by using a Seq2Seq model to predict tool wear. We also trained LSTM and GRU models to compare performance by using R 2 , mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an R 2 of approximately 0.03~0.037 in step drill data, 0.540.57 in top metal data, and 0.16~0.45 in low metal data. Confirming that Seq2Seq exhibited the best performance, we established a real-time monitoring system to verify the prediction results obtained using the Seq2Seq model. It is anticipated that this monitoring system will help prevent accidents in advance.
- Subjects
MANUFACTURING processes; PRODUCTION quantity; LEAD time (Supply chain management)
- Publication
Machines, 2024, Vol 12, Issue 3, p169
- ISSN
2075-1702
- Publication type
Article
- DOI
10.3390/machines12030169