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

Authors: Hiroyuki Kodama; Itaru Uotani; Kazuhito Ohashi

Addresses: Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan ' Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan ' Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan

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.

DOI: 10.1504/IJAT.2019.101399

International Journal of Abrasive Technology, 2019 Vol.9 No.2, pp.89 - 98

Received: 23 Apr 2018
Accepted: 23 Oct 2018

Published online: 31 Jul 2019 *

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