Development of the grinding wheel decision support system using data mining method

Research output: Contribution to conferencePaper

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
Publication statusPublished - Nov 13 2017
Event9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017 - Hiroshima City, Japan
Duration: Nov 13 2017Nov 17 2017

Other

Other9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017
CountryJapan
CityHiroshima City
Period11/13/1711/17/17

Fingerprint

Grinding wheels
Decision support systems
Data mining
Abrasives
Decision trees
Surface properties
Visualization
Engineers

Keywords

  • Data mining
  • Decision tree
  • Grinding wheel

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Kodama, H., Okuda, K., & Oohashi, K. (2017). Development of the grinding wheel decision support system using data mining method. Paper presented at 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017, Hiroshima City, Japan.

Development of the grinding wheel decision support system using data mining method. / Kodama, Hiroyuki; Okuda, Koichi; Oohashi, Kazuhito.

2017. Paper presented at 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017, Hiroshima City, Japan.

Research output: Contribution to conferencePaper

Kodama, H, Okuda, K & Oohashi, K 2017, 'Development of the grinding wheel decision support system using data mining method' Paper presented at 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017, Hiroshima City, Japan, 11/13/17 - 11/17/17, .
Kodama H, Okuda K, Oohashi K. Development of the grinding wheel decision support system using data mining method. 2017. Paper presented at 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017, Hiroshima City, Japan.
Kodama, Hiroyuki ; Okuda, Koichi ; Oohashi, Kazuhito. / Development of the grinding wheel decision support system using data mining method. Paper presented at 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017, Hiroshima City, Japan.
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