Paper Title:
Aid of End-Milling Condition Decision Using Data Mining from Tool Catalog Data for Rough Processing
  Abstract

The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing).

  Info
Periodical
Chapter
Chapter 2: Turning, Milling and Drilling
Edited by
Taghi Tawakoli
Pages
345-350
DOI
10.4028/www.scientific.net/AMR.325.345
Citation
H. Kodama, T. Hirogaki, E. Aoyama, K. Ogawa, "Aid of End-Milling Condition Decision Using Data Mining from Tool Catalog Data for Rough Processing", Advanced Materials Research, Vol. 325, pp. 345-350, 2011
Online since
August 2011
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Price
$32.00
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