研究者詳細 - 児玉　紘幸

論文 - 児玉　紘幸

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Utility Evaluation Regarding Technical Mastering Process on End-milling Condition Decision Support System Using Data Mining 査読

J. Sakamoto, T. Hirogaki, E. Aoyama, K. Ogawa, H. Kodama

Proc. of the 15th International Conference on Advances in Materials & Processing Technology 2012年

Decision methodology of end-milling conditions using data-mining 査読

Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa, Hiroyuki Kodama, Tasuku Kitamura

Precision Engineering 35 ( 2 ) 197 - 203 2011年4月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：ELSEVIER SCIENCE INC

The purpose of the present study is to apply data-mining methods to support the decision of reasonable cutting conditions. Although an enormous amount of information is listed in a catalog, it is not possible to know all of it. Seen from the viewpoint of the user, this enormous amount of information becomes a hindrance. For example, even if an expert worker does not look at a catalog, in end-mill processing, he can decide the appropriate processing condition efficiently from experience; however, this type of situation creates difficult problems for an unskilled worker or a skilled worker with little experience. The recommended cutting condition for every type of material is listed in a catalog together with the appropriate tool, but it takes much time and labor to search and examine the catalog to find the right tool, and this process is inefficient. The main subject of our research was to support the processing condition of the end-mill for each precision tool efficiently based on end-mill clusters. Our research applied the techniques of data mining, in particular, non-hierarchy clustering and hierarchy clustering, to catalog data. With these techniques, we applied multiple regression analysis and reached the following main conclusions. As a first step, we paid attention to the shape element of catalog data. In addition to using conventional mining processes, we grouped end-mills from the viewpoint of tool shape, which meant the ratio of dimensions, visually by applying the K-means method. We applied variable cluster analysis next to each cluster and extracted an predictor variable to represent each cluster, and we performed multiple regression analysis and derived a cutting condition decision formula. The cutting condition decision formula provided high accuracy. The accuracy was higher than the results achieved through mining of all data. A more highly precise processing condition decision formula was derived by doing mining again, excluding the peculiar data clusters such as small diameter end-mill. We understood what was effective for cutting condition decision to be factors related to blade length and the ratio of the full length, factors which have not been singled out through background knowledge or expert knowledge, but were noticed as an effect of catalog mining. © 2010 Elsevier Inc. All rights reserved.

DOI： 10.1016/j.precisioneng.2010.11.005

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Effects on proposed end-milling condition decision-support system using data mining on saving power consumption

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa, Junichi Sakamoto

Proceedings of the 6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011 2011年

掲載種別：研究論文（国際会議プロシーディングス）

Choosing cutting tools and conditions depends on expert engineers' knowledge and experience, and often a lengthy process of trial and error is required before they obtain appropriate end-milling conditions. We have previously proposed data mining methods to make decisions about end-milling conditions on the basis of catalog data. We cut hardened die steel JIS SKD61 under three kinds of end-milling conditions: catalog conditions, mined conditions, expert engineer conditions. We used LCA to quantitatively evaluate the environmental impact resulting from these conditions. Results showed that the mined condition is environmentally superior to the catalog conditions.

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データマイニングを用いたエンドミル加工条件決定支援:工具カタログ推奨条件データ群からの知識抽出

児玉紘幸, 廣垣俊樹, 青山栄一, 小川圭二

精密工学会学術講演会講演論文集 2011 255 - 256 2011年

記述言語：日本語出版者・発行元：一般社団法人日本機械学会

本研究では，非熟練技能者を対象に，切削条件や使用工具形状の選定を支援できるシステムを提案するために，工具カタログデータに，非階層・階層型クラスタリング手法を適用した．また，これらのデータマイニングプロセスにおいて新知識の発掘を目的とする．その結果，非熟練技能者にとって，初期の目安となる切削条件の導出ができた．さらにマイニング効果として，工具カタログメーカごとの推奨切削条件設定の特徴が抽出できた．

DOI： 10.11522/pscjspe.2011S.0.255.0

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Improvement accuracy of cutting condition decision formula using catalog mining

Hiroaki Fukasawa, Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

Proceedings of the 6th International Conference on Leading Edge Manufacturing in 21st Century, LEM 2011 2011年

掲載種別：研究論文（国際会議プロシーディングス）

The aim of our research was to extract new knowledge by applying data mining techniques to machine tool maker catalogs. We cut a SKD61 under three types of cutting conditions: those recommended in tool maker catalogs, those derived from data mining, and those recommended by veteran engineers. Conditions derived from data mining were found to be more stable than those recommended in tool maker catalogs. We fed back based on the catalog mining process. We found improvement accuracy of cutting conditions by calculating the corrective coefficient. As a result, these cutting conditions decision formulas were found to be highly accurate.

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Aid of end-milling condition decision using data mining from tool catalog data for rough processing 査読

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

Advanced Materials Research 325 345 - 350 2011年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：TRANS TECH PUBLICATIONS LTD

The uses of data mining methods to support workers decide on reasonable cutting conditions has been investigated in this work. The aim of our research is to find new knowledge by applying data mining techniques to a tool catalog. Hierarchical and non-hierarchical clustering of catalog data as well as multiple regression analysis was used. The K-means method was used and on the shape presented in the catalog data and grouped end mills from the viewpoint of the tool's shape, which here means the ratio of dimensions has been focused. The numbers of variables were decreased using hierarchical cluster analysis. In addition, an expression for calculating the better cutting conditions was found and the calculated values were compared with the catalog values. There were three cutting conditions: conditions recommended in the catalog, conditions derived by data mining, and proven cutting conditions for die machining (rough processing). © (2011) Trans Tech Publications, Switzerland.

DOI： 10.4028/www.scientific.net/AMR.325.345

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Cutting Condition Decision Support System Using Data Mining －Application of Life Cycle Assessment on Estimation of Cutting Conditions－査読

J. Sakamoto, H. Kodama, T. Hirogaki, E. Aoyama, K. Ogawa

Proc. of the 7th International Symposium on Environmentally Conscious Design and Inverse Manufacturing 640 - 644 2011年

Cutting condition decision methodology based on data-mining of tool catalog data 査読

Hiroyuki Kodama, Toshiki Hirogaki, Eiichi Aoyama, Keiji Ogawa

ASME 2010 International Manufacturing Science and Engineering Conference, MSEC 2010 2 491 - 499 2010年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：AMER SOC MECHANICAL ENGINEERS

Data-mining methods were used to support decisions about reasonable cutting conditions. The aim of our research was to extract new knowledge by applying data-mining techniques to a tool catalog. We used both hierarchical and non-hierarchical clustering of catalog data and also used applied multiple regression analysis. We focused on the shape element of catalog data and we visually grouped end mills from the viewpoint of tool shape, which here meant the ratio of dimensions, by using the k-means method. We then decreased the number of variables by using hierarchical cluster analysis. We also found an expression for calculating the best cutting conditions, and we compared the calculated values with the catalog values. We did 15 minutes of cutting work using three kinds of cutting conditions: conditions recommended in the catalog, conditions derived by data-mining, and proven cutting conditions for die machining (rough processing). © 2010 by ASME.

DOI： 10.1115/MSEC2010-34199

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Decision Methodology of Cutting Condition Based on Data-mining of Tool Catalog Data 査読

H. Kodama, T. Hirogaki, E. Aoyama, K. Ogawa

Proc. of ASME2010 International Manufacturing Science and Engineering Conference 1 - 9 2010年

Effect on Decision Methodology of End-milling Conditions Using Data-mining 査読

H. Kodama, T. Hirogaki, E. Aoyama, K. Ogawa, T. Kitamura

Proc. of ISFA 2010 International Symposium on Flexible Automation 1 - 8 2010年

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