Article citationsMore>>

G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. Konig and R. Teti, “Tool Condition Monitoring (TCM)—The Status of Research and Industrial Application,” CIRP An-nals—Manufacturing Technology, Vol. 44, No. 2, 1995, pp. 541-567. doi:10.1016/S0007-8506(07)60503-4

has been cited by the following article:

• TITLE: Tool Wear Classification Using Fuzzy Logic for Machining of Al/SiC Composite Material

  AUTHORS: V. Kalaichelvi, R. Karthikeyan, D. Sivakumar, V. Srinivasan

  KEYWORDS: Tool Wear Classification; Current Signal; Regression Analysis; Fuzzy Classification

  JOURNAL NAME: Modeling and Numerical Simulation of Material Science, Vol.2 No.2, April 16, 2012

  ABSTRACT: Tool wear state classification has good potential to play a critical role in ensuring the dimensional accuracy of the work piece and prevention of damage to cutting tool in machining process. During machining process, tool wear is an important factor which contributes to the variation of spindle motor current, speed, feed and depth of cut. In the present work, online tool wear state detecting method with spindle motor current in turning operation for Al/SiC composite material is presented. By analyzing the effects of tool wear as well as the cutting parameters on the current signal, the models on the relationship between the current signals and the cutting parameters are established with partial design taken from experimental data and regression analysis. The fuzzy classification method is used to classify the tool wear states so as to facilitate defective tool replacement at the proper time.