Tool Wear Monitoring System Using Seq2Seq
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 cha...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/3/169 |
| _version_ | 1797240212153171968 |
|---|---|
| author | Wang-Su Jeon Sang-Yong Rhee |
| author_facet | Wang-Su Jeon Sang-Yong Rhee |
| author_sort | Wang-Su Jeon |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-04-24T18:03:50Z |
| format | Article |
| id | doaj.art-61ce4a0fc99e4f02aa2225407f76c0b5 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-04-24T18:03:50Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-61ce4a0fc99e4f02aa2225407f76c0b52024-03-27T13:51:49ZengMDPI AGMachines2075-17022024-03-0112316910.3390/machines12030169Tool Wear Monitoring System Using Seq2SeqWang-Su Jeon0Sang-Yong Rhee1Department of Computer Engineering, University of Kyungnam, Changwon 51767, Republic of KoreaDepartment of Computer Engineering, University of Kyungnam, Changwon 51767, Republic of KoreaThe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>, mean absolute error (MAE), and mean squared error (MSE). The Seq2Seq model outperformed LSTM and GRU with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2075-1702/12/3/169sequence to sequencetool wearLSTMGRUmonitoring system |
| spellingShingle | Wang-Su Jeon Sang-Yong Rhee Tool Wear Monitoring System Using Seq2Seq Machines sequence to sequence tool wear LSTM GRU monitoring system |
| title | Tool Wear Monitoring System Using Seq2Seq |
| title_full | Tool Wear Monitoring System Using Seq2Seq |
| title_fullStr | Tool Wear Monitoring System Using Seq2Seq |
| title_full_unstemmed | Tool Wear Monitoring System Using Seq2Seq |
| title_short | Tool Wear Monitoring System Using Seq2Seq |
| title_sort | tool wear monitoring system using seq2seq |
| topic | sequence to sequence tool wear LSTM GRU monitoring system |
| url | https://www.mdpi.com/2075-1702/12/3/169 |
| work_keys_str_mv | AT wangsujeon toolwearmonitoringsystemusingseq2seq AT sangyongrhee toolwearmonitoringsystemusingseq2seq |