Decision support system for grinding wheel selection using data-mining

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

1 Citation (Scopus)

Abstract

In the grinding wheel catalog data-set, the recommended grinding conditions are shown in reference to five factors (abrasive grain, grain size, grade, structure, and bonding material) of the three main elements (abrasive grain, bonding material, and pore). Since systematic arrangement is not made, grinding conditions (cutting speed, table feed, depth of cut) have to be decided on the basis of an experienced engineer's information or experience. Moreover, although the setting of the five factors of the three elements of a grinding wheel is important parameter that affects the surface quality and grinding efficiency, it is difficult to determine the optimal combination of workpiece materials and grinding conditions. In this research, a support system for effectively deciding the desired grinding wheel was built by using a decision tree technique, which is one of the data-mining techniques. This system extracts a significant tendency of grinding wheel conditions from catalog data. As a result, a visualization process was proposed in correspondence to the action of the grinding wheel elements and their factors to the material characteristics of the workpiece material.

Original languageEnglish
Title of host publicationEuropean Society for Precision Engineering and Nanotechnology, Conference Proceedings - 18th International Conference and Exhibition, EUSPEN 2018
EditorsO. Riemer, Enrico Savio, D. Billington, R. K. Leach, D. Phillips
Publishereuspen
Pages213-214
Number of pages2
ISBN (Print)9780995775121
Publication statusPublished - Jan 1 2018
Event18th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2018 - Venice, Italy
Duration: Jun 4 2018Jun 8 2018

Other

Other18th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2018
CountryItaly
CityVenice
Period6/4/186/8/18

Keywords

  • Cylindrical grinding
  • Data-mining
  • Decision tree
  • Grinding wheel

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Instrumentation
  • Environmental Engineering
  • Mechanical Engineering
  • Materials Science(all)

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  • Cite this

    Kodama, H., Oohashi, K., & Uotani, I. (2018). Decision support system for grinding wheel selection using data-mining. In O. Riemer, E. Savio, D. Billington, R. K. Leach, & D. Phillips (Eds.), European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 18th International Conference and Exhibition, EUSPEN 2018 (pp. 213-214). euspen.