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2020/7 Vol.123

表紙の説明:「これは、1930年代にドイツのファウター社で製造されたホブ盤のテーブルとワークアーバ部分である。穴加工済みの工作物を、テーブルの中心になるようにワークアーバに取付けて固定し、工具(ホブカッタ)と工作物の相対運動により歯形形状を創成しながら加工して歯車を作る。」
[日本工業大学工業技術博物館]

表紙写真 北原 一宏

バックナンバー

論文アクセスランキング

日本機械学会学術誌(和文) 年間アクセス数トップ10

(2019年1月~2019年12月の期間で集計) *順位の変動は2018年10月~2019年9月の期間との比較


ディーゼル機関におけるエタノールおよびブタノール混合軽油の燃焼比較

山本 昌平, 坂口 大作, 植木 弘信, 石田 正弘

ディーゼル機関におけるエタノールおよびブタノール混合軽油の燃焼比較(2013) DOI:10.1299/kikaib.79.380
In order to realize a premixed compression ignition (PCI) engine by utilizing bio-alcohol, combustion characteristics of bio-alcohol blended with gas oil were compared between ethanol and n-butanol in a diesel engine. The effects of the ethanol blend ratio and the butanol blend ratio on ignition delay, premixed combustion, diffusion combustion, fuel consumption and exhaust emissions such as smoke density, nitrogen oxide (NOx) and so on were investigated experimentally. It is found that ethanol almost burns out together with low evaporation temperature composition of gas oil in the premixed combustion period and the heat release in the diffusion combustion is based on mainly high evaporation temperature composition of gas oil, then, soot is formed in the diffusion combustion of gas oil. On the other hand, a part of butanol burns in the diffusion combustion, and the combustion of butanol in the diffusion stage is not the cause of soot formation. Butanol is more useful in diesel engine compared with ethanol because butanol can be blended with gas oil without surface-active agent, and fuel consumption and smoke are almost equal in both blend fuels if the alcohol blend ratio is the same.

機械学習を用いたしゅう動面状態監視システムに関する研究

橋本 優花, 本田 知己, 持田 裕介, 杉山 和彦, 中村 由美子, 高東 智佳子

日本機械学会論文集 (2018) DOI:10.1299/transjsme.18-00275
Machine equipment usually comprises many mechanical elements that can fail because of functional deterioration and friction. For tribo-elements like plane bearings, it is extremely important to diagnose the abnormal conditions and prevent such parts from breakdown caused by wear. However, diagnosing tribo-elements requires expensive diagnostic equipment and expertise. This study aims to propose a cost- and time- effective system that detect the signs of breakdown during equipment operation by using machine learning to identify abnormalities. We conducted wear tests in contaminated oil and used multiple sensors to collect data regarding the friction force, the electrical contact resistance, the acoustic emission (AE) signal, and vibration. An appropriate learning sample was selected using k-fold cross-validation. The electrical contact resistance was found to contribute relatively little to the detection of abnormalities, whereas the friction coefficient contributed greatly. Furthermore, the AE signal and the vibration detected local changes on the sliding surface. Consequently, we found that machine learning can judge whether monitoring data are normal or abnormal.

樹脂材料の強度低下予測(温度と湿度による加速評価)

澤田 祐子, 越前谷 大介

日本機械学会論文集 (2019) DOI:10.1299/transjsme.18-00341
A large quantity of engineering plastics are used in electric industrial products. It is necessary to estimate the life of the product made of engineering plastics because of its shorter life and lower strength than some of ceramics or metals. New test device was developed for accelerated stress test. It can make the unsaturated environment above 100 oC under atmospheric pressure using superheated water vapor. Then it becomes possible to set wide range condition of temperature and humidity. Polybutylene terephthalate (PBT) is one of the hydrolyzed engineering plastics. Test pieces of PBT were submitted to accelerated stress test under some conditions of elevated temperature and humidity. Then they were provided to three point bending test. The time required for strength degradation has a good relation with water vapor pressure at each temperature. Then it became clear that the time required for strength degradation on each water vapor pressure was accelerated with temperature using the Arrhenius type equation.

4位

[↑]

多量の含油排水からの油分回収およびBDF製造

近藤 千尋, 佐野 広季, 一宮 暢希, 山根 浩二, 河﨑 澄
日本機械学会論文集 (2019) DOI:10.1299/transjsme.18-00340

5位

[↑]

格子ボルツマン法による自転車競技の集団走行の大規模LES空力解析

長谷川 雄太, 青木 尊之, 小林 宏充, 白﨑 啓太
日本機械学会論文集 (2019) DOI:10.1299/transjsme.18-00441

6位

[↑]

Ni基超合金Alloy718合金の疲労強度に及ぼす表面加工層の影響(第2報:結晶塑性有限要素法を用いた残留応力解放の検討)

蓮沼 将太, 波田野 明日可, 泉 聡志, 酒井 信介
日本機械学会論文集 (2017) DOI:10.1299/transjsme.16-00264

7位

[↑]

温度と湿度による樹脂材料の強度低下の予測

澤田 祐子, 越前谷 大介
日本機械学会論文集 (2019) DOI:10.1299/transjsme.18-00505

8位

[NEW]

電磁比例弁内のスプールに作用するクーロン摩擦力に起因した不安定振動の解析と安定化させるための設計法

山藤 勝彦, 山本 建, 澤田 賢治
日本機械学会論文集 (2017) DOI:10.1299/transjsme.16-00553

9位

[NEW]

離散化モデルを用いたHCCIエンジンの制御シミュレーション

林 卓哉, 山崎 由大, 金子 成彦, 疋田 孝幸, 水野 沙織, 藤井 拓磨
日本機械学会論文集 (2018) DOI:10.1299/transjsme.17-00325

10位

[↓]

トポロジー最適化と積層造形を活用したラティス構造の創出手法

西津 卓史, 谷次 智弥, 竹澤 晃弘, 米倉 一男, 渡邊 修, 北村 充
日本機械学会論文集 (2017) DOI:10.1299/transjsme.16-00581

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