日本機械学会サイト

目次に戻る

2020/3 Vol.123

表紙の説明:1931 年に米国のブラットフォード社で製造されたベルト掛け段車式普通旋盤の主軸台の換え歯 車装置部分である。当時は、段車の 付いた主軸端に小歯車を装着し、1、2段減速し、その都度、換え歯車表 を見て歯車を掛け替えて、送り速度 やねじのピッチを換え作業した。

表紙写真 北原一宏
撮影地協力 日本工業大学 工業技術博物館

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

(2018年10月~2019年9月の期間で集計)*順位の変動は2018年4月~2019年2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

日本機械学会論文集B編 (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.

熱電対による表面温度測定の誤差解析

中村 元

日本機械学会論文集 (2018) DOI:10.1299/transjsme.18-00216
In surface temperature measurement by thermocouple, measurement error was quantitatively investigated when the temperature measuring junction was adhered to the surface and a part of the lead wire was placed on the isothermal surface. First, heat paths of a thermocouple were modeled by thermal-resistance-network. Unknown parameters related to the contact between the thermocouple and the surface were estimated based on the measured data. The error ratio calculated by the thermal-resistance-network model agreed well to the experimental data both quantitatively and qualitatively, which indicates the validity of this model. Then, an analytic solution was derived with only main heat paths of the thermal-resistance-network. As a result, it was confirmed that even with only main heat paths, a reasonable error-ratio corresponding to the measurement can be obtained. Using the analytic solution, the error-ratio was calculated against various parameters, such as type and diameter of metal wire, gap between the junction and the surface, thermal conductivity of adhesive, lead-length placed on the isothermal surface, and so forth. Based on the analytic results, the effective method was presented to reduce the measurement error.

4位

[NEW]

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

5位

[NEW]

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

6位

[NEW]

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

7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

8位

[↓]

外乱オブザーバ併用型モデル予測制御による2リンク・マニピュレータの制御

佐藤 俊之, 阿部 梨恵, 齋藤 直樹, 永瀬 純也, 嵯峨 宣彦
日本機械学会論文集 (2015) DOI:10.1299/transjsme.15-00084

9位

[NEW]

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

10位

[NEW]

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

キーワード: