Using catalog data mining in support of determining micro end-milling conditions

Hiroyuki Kodama, Koichi Okuda, Takuya Tsujimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


A growing number of requirements are being placed on micro fabrication using end mills with high operating degrees-of-freedom. In particular, when the minor diameter of the end mill is 1.0 mm or less, the handling of tools becomes difficult because of the influence of the characteristic size effect and bending of the cutting portion. Furthermore, it is hard for engineers to derive the cutting conditions that can serve as indexes in the early stage of end-milling with small minor diameters. To solve this problem, in this research, on a basis of workpiece material characteristics and tool shape parameters, a system that can make instantaneous decisions on the tool shape and cutting conditions to be used was built, and its usefulness was evaluated. This system was developed by applying data mining techniques together with non-hierarchical and hierarchical clustering methods on tool catalog data. The results of the analysis found that the characteristics of the workpiece material were significant in making decisions regarding the cutting conditions for the minor diameter of the end mill. Moreover, the cutting speed was found to have a significant effect on the tool shape parameters, whereas differences as to the processing method (side milling or slotting) did not have a significant effect.

Original languageEnglish
Title of host publicationInternational Symposium on Flexible Automation, ISFA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781509034673
Publication statusPublished - Dec 16 2016
Externally publishedYes
EventInternational Symposium on Flexible Automation, ISFA 2016 - Cleveland, United States
Duration: Aug 1 2016Aug 3 2016


OtherInternational Symposium on Flexible Automation, ISFA 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering


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