✦ LIBER ✦

FUZZY CLUSTERING FOR AUTOMATED TOOL CONDITION MONITORING IN MACHINING

✍ Scribed by S. Li; M.A. Elbestawi

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 397 KB
Volume: 10
Category: Article
ISSN: 0888-3270
DOI: 10.1006/mssp.1996.0037

No coin nor oath required. For personal study only.

✦ Synopsis

A strategy for unsupervised classification for automated tool condition monitoring by using fuzzy neural networks is proposed. This approach is based on a newly developed classification algorithm by the authors, namely the multiple principal component fuzzy neural network for tool condition monitoring. This algorithm uses three major components of soft computation; fuzzy logic, neural network, and probability reasoning. The proposed network functioned successfully in clustering the data obtained from cutting tests performed within a reasonable range of cutting conditions. Experiments in turning were implemented to test the performance of the proposed algorithm. Several sensors were used for monitoring feature selection. The results showed approximately 80 to 94% success rates in these tests.

📜 SIMILAR VOLUMES

ORDER BISPECTRUM: A NEW TOOL FOR RECIPRO

ORDER BISPECTRUM: A NEW TOOL FOR RECIPROCATED MACHINE CONDITION MONITORING

✍ DUŠAN KOCUR; RADOSLAV STANKO 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 792 KB

Vibrations and sounds generated by reciprocated machines or by their parts strongly depend on the rotation speed of the main shaft of the tested reciprocating system. At the testing or at common performance of the reciprocated machines, their rotation speed is usually changing. With regard to this f

Possibilistic and fuzzy C-means clusteri

Possibilistic and fuzzy C-means clustering for process monitoring in an activated sludge waste-water treatment plant

✍ Pekka Teppola; Pentti Minkkinen 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 304 KB 👁 1 views

A partial least squares (PLS) regression is used to model and visualize the waste-water treatment process. The score values of PLS are submitted to both a fuzzy C-means (FCM) clustering and a possibilistic C-means (PCM) clustering. In this work, four concepts are presented. Firstly, a hidden path pr