Enhancing clustering algorithm with initial centroids in tool wear region recognition
Autonomous manufacturing allows the system to distinguish between a mild, normal and total failure in tool condition. K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. Nevertheless, the algorithm is sensitive to the initial centroids giving...
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| 主要な著者: | , , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
SpringerOpen
2020
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| オンライン･アクセス: | http://eprints.utem.edu.my/id/eprint/26681/2/FULL%20PAPER_NA%20KASIM%20-%20IJPEM%20LAST%20REVISED.PDF http://eprints.utem.edu.my/id/eprint/26681/ https://link.springer.com/article/10.1007/s12541-020-00450-5 https://doi.org/10.1007/s12541-020-00450-5 |
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