研究者詳細 - 児玉　紘幸

論文 - 児玉　紘幸

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Data mining from endmill tool catalog information based on the use of a machine learning method

Akihito Asakura, Toshiki Hirogaki, Eiichi Aoyama, Hiroyuki Kodama

Proceedings of the ASME Design Engineering Technical Conference 9 2020年

掲載種別：研究論文（国際会議プロシーディングス）

In recent years, the needs associated with the development of new technologies in the manufacturing industry that utilize big data typified by the Internet-of-Things (IoT) and artificial intelligence (AI) have been increasing. Recent computer-aided manufacturing (CAM) systems have evolved so that unskilled technicians can create tool paths relatively easily with numerically controlled (NC) programs, but tool-cutting conditions used for machining cannot be automatically determined. Therefore, many unskilled technicians often set the cutting conditions based on the recommended conditions described in the tool catalog. However, given that the catalog contains large-scale data on machining technology, setting the proper conditions becomes a time-consuming and inefficient process. In this study, we aimed to construct a system to support unskilled technicians to determine the optimum machining conditions. To this end, we constructed a prediction model using a random forest machine learning method to predict the cutting conditions. It was confirmed that the prediction with the random forest method can be performed with high accuracy based on the cutting conditions recommended by the tool maker. Thus, the effectiveness of this method was verified.

DOI： 10.1115/DETC2020-22126

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An Effect of a Setting Depth of Cut in a Face Grinding Process with a Single Layer Electro Plated Diamond Grinding Wheel with Periphery R Shape 査読

Takanori FUJIWARA, Koudai HIRAMATSU, Takuro HASHIGUCHI, Takashi TSUJINO, Ryo KOMATSUBARA, Takashi ONISHI, Hiroyuki KODAMA, Kazuhito OHASHI

Proceedings of the 18th International Conference on Precision Engineering(ICPE2020) 2020年

Concentric Mutual Lapping to Improve Sliding Surface Function of SiC Ceramics 査読

Hiroyuki Kodama, Hayato Koyama, Tomoaki Ishii, Yusuke Tanimoto, Kazuhito Ohashi

Int. J. of Automation Technology 13 ( 6 ) 756 - 764 2019年11月

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

Hiroyuki Kodama, Itaru Uotani and Kazuhito Ohashi

Int. J. Abrasive Technology 9 ( 2 ) 89 - 98 2019年8月

最大情報係数MICを応用したラジアスエンドミルのカタログマイニング査読

佐久間太志, 廣垣俊樹, 青山栄一, 久保健吾, 児玉紘幸

精密工学会誌 85 ( 3 ) 260 - 266 2019年3月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：公益社団法人精密工学会

<p>A novel datamining method has been needed because the IoT (Internet of things) is spread over various kinds of industrial fields. We therefore propose to apply a data mining method to tool catalog data-base to improve the manufacturing technologies with machine tools because it is considered to include a useful information derived from tool manufacturing technology as a big-data. In the present report, we look at MIC (Maximal Information Coefficient) as a novel processing method to search for a new knowledge in tool data data-base, and construct a hierarchical clustering method based on MIC as a data mining method. Comparing a predicting equation derived from the conventional catalog mining method based on a traditional statistics with one based on MIC processing method, we investigate a function of MIC in data mining for end milling conditions. As a result, it can be seen that a constructed method (MIC data mining method) makes it feasible efficiently to find out essential variables in the radius end mill database because a derived practical formula has less interactions than conventional one with keeping the same prediction accuracy.</p>

DOI： 10.2493/jjspe.85.260

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機械学習を導入した工具カタログのデータマイニングプロセスの提案査読

佐久間太志, 朝倉彬仁, 山田航太郎, 廣垣俊樹, 青山栄一, 児玉紘幸

日本機械学会論文集 85 ( 877 ) 19 - 00215-19-00215 2019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

<p>We attempt to construct a novel technology development utilizing big data such as Deep Learning in the manufacturing industry. Especially, we look at the data mining method and the tool catalog as a useful big data base which is updated by tool makers because it is easy for CAD/CAM engineers and machine tool operators to obtain it in the manufacturing fields. In the present report, we proposed the visualization and consideration of cutting condition determination process based on a decision tree method which is one type of statistical analysis method for radius-endmill data base. We also developed a cutting condition prediction system with a random forest which is a type of machine learning method applying a decision tree. Moreover, we performed a case study in endmilling under deriving cutting conditions by the proposed method, which is an unknown and expanded cutting condition based on tool catalog data base. As a result, it is demonstrated that the support based on machine learning is found to be effective to select a cutting condition including an unknown cutting condition in tool catalog data base.</p>

DOI： 10.1299/transjsme.19-00215

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Investigation on Concentric Mutual Lapping Characteristic for Improvement in Sliding Surface Function of SiC Ceramics 査読

Yusuke TANIMOTO, Hayato KOYAMA, Hiroyuki KODAMA, Kazuhito OHASHI

Proceedings of the 22nd International Symposium on Advances in Abrasive Technology(ISAAT2019) 2019年

Principal Factor Decision Support System Using Data-Mining Methodology for Surface Grinding Wheel 査読

Hiroyuki KODAMA, Itaru UOTANI, Takao MENDORI, Kazuhito OHASHI

Proceedings of the 22nd International Symposium on Advances in Abrasive Technology(ISAAT2019) 2019年

Influence on Grinding Force Distribution in Feed Rate Variations of Cemented Carbide with Vertical Face Grinding 査読

Takashi TSUJINO, Takanori FUJIWARA, Hiroyuki KODAMA, Takashi ONISHI, Shinya TSUKAMOTO, Kazuhito OHASHI

Proceedings of the 22nd International Symposium on Advances in Abrasive Technology(ISAAT2019) 2019年

Influence of Grinding Temperature on Surface Quality of CFRP 査読

Shingo OKAZAKI, Yifan JIANG, Hiroyuki KODAMA, Hiroyuki YODEN, Kazuhito OHASHI

Proceedings of the 22nd International Symposium on Advances in Abrasive Technology(ISAAT2019) 2019年

Study on concentric mutual lapping for improvement in sliding surface function of SiC ceramics 査読

Yusuke Tanimoto, Hayato Koyama, Hiroyuki Kodama, Kazuhito Ohashi

Proceedings of the 21st International Symposium on Advances in Abrasive Technology (ISAAT2018) 2018年10月

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）

© ISAAT 2018 - 21st International Symposium on Advances in Abrasive Technology. All rights reserved. In this study, the ring shaped SiC ceramics applied to sliding components are selected as specimens. In the surface of SiC ceramics, many scratches penetrating the inner edge to outer one are generated in random direction by conventional lapping. The scratches affect the surface functions in prevention of fluid exuding from the outer side to the inner side of SiC ring and have low friction force on the finished surface as a sliding material. In this study, the concentric mutual lapping is proposed to remove the scratches by conventional lapping. Then, the surface topography is evaluated quantitatively by the white light interferometer and proposed the vectorial and quantitative analysis of surface profile. As a result, the concentric mutual lapping can remove scratches quickly by the conventional lapping. And then circumferential scratches along a lapping direction were generated. In addition, we analyzed effect of surface topography on the surface function as a sliding material by sliding tests. From the investigation, it turned out that the concentric mutual lapping suppressed the difference in surface functions depending on the sliding direction.

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Decision support system for grinding wheel selection using data-mining 査読

Hiroyuki Kodama, Kazuhito Ohashi, Itaru Uotani

Proceedings of euspen’s 18th International Conference & Exhibition 213 - 214 2018年6月

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）

© 2018 CURRAN-CONFERENCE. All rights reserved. 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.

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Aiding of Micro End-Milling Condition Decision Using Data-Mining from Tool Catalog Data 査読

Hiroyuki Kodama, Koichi Okuda, Kazuhiro Tanaka

Int. J. of Automation Technology 12 ( 2 ) 238 - 245 2018年3月

Influence of Grinding Atmosphere on Grinding Characteristics of CFRP 査読

S. Okazaki, Y. Jiang, H.Kodama, H. Yoden, K. Ohashi

Proceedings of the 2018 International Conference on Machining, Materials and Mechanical Technologies(IC3MT2018) 105 - 109 2018年

Micro Abrasive Jet Patterning of Sloped Micro Herringbone Grooves for Journal Bearings 査読

Shota NAKAMAE, Masashi HARADA, Hiroyuki KODAMA and Kazuhito OHASHI