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 language | English |
|---|---|
| Publication status | Published - Nov 13 2017 |
| Event | 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017 - Hiroshima City, Japan Duration: Nov 13 2017 → Nov 17 2017 |
Other
| Other | 9th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2017 |
|---|---|
| Country | Japan |
| City | Hiroshima City |
| Period | 11/13/17 → 11/17/17 |
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Keywords
- Data mining
- Decision tree
- Grinding wheel
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
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 conference › Paper
}
TY - CONF
T1 - Development of the grinding wheel decision support system using data mining method
AU - Kodama, Hiroyuki
AU - Okuda, Koichi
AU - Oohashi, Kazuhito
PY - 2017/11/13
Y1 - 2017/11/13
N2 - 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.
AB - 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.
KW - Data mining
KW - Decision tree
KW - Grinding wheel
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