Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Wang, X; Chen, P; Tansel, IN; Yenilmez, A

doi:10.1016/j.ijmachtools.2005.04.010

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Atıf İçin Kopyala

Wang X., Chen P., Tansel I., Yenilmez A.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, cilt.46, sa.1, ss.36-42, 2006 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 46 Sayı: 1
Basım Tarihi: 2006
Doi Numarası: 10.1016/j.ijmachtools.2005.04.010
Dergi Adı: INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.36-42
İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed. (c) 2005 Elsevier Ltd. All rights reserved.