Decision support system for principal factors of grinding wheel using data mining methodology

Research output: Contribution to journalArticle

Abstract

The recommended grinding conditions are described in five factors of the three main elements in the grinding wheel catalogue dataset. Although the setting of the five factors of the three elements of a grinding wheel is an important parameter that affects the surface quality and grinding efficiency, it is difficult to determine the optimal combination of workpiece materials and grinding conditions. 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. As a result, a visualisation process was proposed in correspondence to the action of the grinding wheel elements and their factors to the material characteristics of the workpiece material. Patterns to support selection of grinding wheels by visualising the surface grinding wheel selection decision tendency from more amount of data was produced, based on data mixed with Japan Industrial Standards (JIS) and maker's catalogue data.

Original languageEnglish
Pages (from-to)89-98
Number of pages10
JournalInternational Journal of Abrasive Technology
Volume9
Issue number2
DOIs
Publication statusPublished - Jan 1 2019

Fingerprint

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

Keywords

  • Data mining
  • Decision tree
  • Grinding wheel
  • Surface grinding

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

@article{759d3e2323c04ffc89d566f96d7113ba,
title = "Decision support system for principal factors of grinding wheel using data mining methodology",
abstract = "The recommended grinding conditions are described in five factors of the three main elements in the grinding wheel catalogue dataset. Although the setting of the five factors of the three elements of a grinding wheel is an important parameter that affects the surface quality and grinding efficiency, it is difficult to determine the optimal combination of workpiece materials and grinding conditions. 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. As a result, a visualisation process was proposed in correspondence to the action of the grinding wheel elements and their factors to the material characteristics of the workpiece material. Patterns to support selection of grinding wheels by visualising the surface grinding wheel selection decision tendency from more amount of data was produced, based on data mixed with Japan Industrial Standards (JIS) and maker's catalogue data.",
keywords = "Data mining, Decision tree, Grinding wheel, Surface grinding",
author = "Hiroyuki Kodama and Itaru Uotani and Kazuhito Oohashi",
year = "2019",
month = "1",
day = "1",
doi = "10.1504/IJAT.2019.101399",
language = "English",
volume = "9",
pages = "89--98",
journal = "International Journal of Abrasive Technology",
issn = "1752-2641",
publisher = "Inderscience Enterprises Ltd",
number = "2",

}

TY - JOUR

T1 - Decision support system for principal factors of grinding wheel using data mining methodology

AU - Kodama, Hiroyuki

AU - Uotani, Itaru

AU - Oohashi, Kazuhito

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The recommended grinding conditions are described in five factors of the three main elements in the grinding wheel catalogue dataset. Although the setting of the five factors of the three elements of a grinding wheel is an important parameter that affects the surface quality and grinding efficiency, it is difficult to determine the optimal combination of workpiece materials and grinding conditions. 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. As a result, a visualisation process was proposed in correspondence to the action of the grinding wheel elements and their factors to the material characteristics of the workpiece material. Patterns to support selection of grinding wheels by visualising the surface grinding wheel selection decision tendency from more amount of data was produced, based on data mixed with Japan Industrial Standards (JIS) and maker's catalogue data.

AB - The recommended grinding conditions are described in five factors of the three main elements in the grinding wheel catalogue dataset. Although the setting of the five factors of the three elements of a grinding wheel is an important parameter that affects the surface quality and grinding efficiency, it is difficult to determine the optimal combination of workpiece materials and grinding conditions. 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. As a result, a visualisation process was proposed in correspondence to the action of the grinding wheel elements and their factors to the material characteristics of the workpiece material. Patterns to support selection of grinding wheels by visualising the surface grinding wheel selection decision tendency from more amount of data was produced, based on data mixed with Japan Industrial Standards (JIS) and maker's catalogue data.

KW - Data mining

KW - Decision tree

KW - Grinding wheel

KW - Surface grinding

UR - http://www.scopus.com/inward/record.url?scp=85070497082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85070497082&partnerID=8YFLogxK

U2 - 10.1504/IJAT.2019.101399

DO - 10.1504/IJAT.2019.101399

M3 - Article

VL - 9

SP - 89

EP - 98

JO - International Journal of Abrasive Technology

JF - International Journal of Abrasive Technology

SN - 1752-2641

IS - 2

ER -