Investigation of principal factor decision support system using data mining methodology for surface grinding wheel

Research output: Contribution to journalArticle

Abstract

The five factors (abrasive grain, grain size, grade, structure and bonding material) of the three main elements (abrasive grain, bonding material and pore) of a grinding wheel are important parameters affecting surface quality and grinding efficiency, however it is difficult to determine an optimal combination of grinding conditions for workpiece material. In previous research, we constructed a support system for effectively selecting an appropriate grinding wheel using decision tree technique. We also proposed a visualisation process to show how grinding wheel elements and factors correspond to the materials characteristics of the workpiece material. In this research, to evaluate the usefulness of prepared visualisation maps and their effectiveness in deciding grinding wheel elements, we performed comparison experiments applying the surface grinding technique to JIS SUS310S material using PA abrasive grain as recommended by the grain-type visualisation map and WA and GC abrasive grains for comparison purposes. We found that visualisation maps enable quick selection of a grinding wheel even for the grinding of difficult-to-cut materials for which grinding wheel selection is usually difficult.

Original languageEnglish
Pages (from-to)303-318
Number of pages16
JournalInternational Journal of Abrasive Technology
Volume9
Issue number4
DOIs
Publication statusPublished - 2019

Keywords

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

ASJC Scopus subject areas

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

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