Enhancing clustering algorithm with initial centroids in tool wear region recognition

• Main Authors: Kasim, Nur Adilla, Nuawi, Mohd Zaki, Abdul Ghani, Jaharah, Ngatiman, Nor Azazi, Che Haron, Che Hassan, Muhammad Rizal
• Format: Article
• Language: English
• Published: SpringerOpen 2020
• Online Access: 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

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...