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北山, 哲士 ; 荒川, 雅生 ; 山崎, 光悦 ; Kitayama, Satoshi ; Arakawa, Masao ; Yamazaki, Koetsu
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金沢大学理工研究域機械工学系<br />This paper proposes a new method which is called as Adaptive Range Particle Swarm Optimization (ARPS
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O), based on Adaptive Range Genetic Algorithm. That is, the search domain is determined by using the mean and standard deviation of each design variable. At the initial search stage it is preferable to explore the design domain widely, and is also preferable to explore the small design domain as the search goes on. To achieve this objective, new parameter which determines the search domain is introduced. Through numerical examples, the effectiveness and validity of proposed method are examined.<br />本講演では,連続型多峰性関数の大域的最適解を求めるために開発されたParticle Swarm Optimizationをさらに発展させ,設計変数の平均と標準偏差を用いて探索領域を適宜変更させる領域適応型Particle Swarm Optimizationを提案する.本論文で提案する方法の有効性を数値計算例を通じて検討する.<br />出版者照会後に全文公開
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山崎, 光悦
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酒井, 忍 ; 山崎, 光悦 ; 鈴木, 武之 ; 中野, 正貴 ; 浅田, 浩治
概要:
Crankshaft miller is a machine tool used to mill crankshafts for engines. The miller causes a great amount of heat durin
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g the cutting process, which combines heavy and intermittent cutting. In this paper, the approximation equation of thermal displacement which applied a method to estimate thermal deformation on the basis of a few machine temperatures and the finite element analysis is proposed. The thermal characteristics and the mechanisms of heat transformation in the crankshaft miller are made clear by numerical simulations using the finite element models of the miller and the experiments. The simulations tried a steady and transient state analyses of heat transformation to consider the heat transfer coefficient in the cutting process. From the results of the analyses, the temperature distribution and thermal deformation on each part of the miller were found. The results of analyses are compared with the experimental values using the actual miller. The point of efficient measuring temperature and the calorific values in order to revise each coefficient value in the approximation equation were examined. Additionally, the effectiveness of proposed approximation equation was shown. © 2011 The Japan Society of Mechanical Engineers.
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山崎, 光悦 ; 坂本, 二郎 ; 酒井, 忍
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山崎, 光悦 ; 近田, 康夫 ; 上田, 隆司 ; 米山, 猛
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山崎, 光悦 ; 山田, 良穂 ; 上野, 久儀 ; 米山, 猛 ; 岩田, 佳雄 ; 多田, 幸生
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山崎, 光悦 ; 山田, 実 ; 並木, 則和 ; 近田, 康夫 ; 高本, 與志夫
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山田, 実 ; 山崎, 光悦 ; 岡島, 厚
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小貫, 涼介 ; 北山, 哲士 ; 山崎, 光悦 ; 荒川, 雅生
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This paper proposes a new method called Adaptive Range Differential Evolution (ARDE). The basic idea can be obtained fro
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m the Adaptive Range Particle Swarm Optimization (ARPSO). In the ARDE, the active search domain range consists of the mean and the standard deviation of each design variable. Also the best position is included in this search range. Therefore, the active search domain range is newly defined by utilizing the mean and the standard deviation of each design variable, and the best position. The detailed procedure of the new active search domain range is described in this paper. To keep the best position for the new active search domain range will lead to the wide search range. Of course, the newly active search domain range will shrink through the search iteration. As the result, a highly accurate global minimum can be found. The effectiveness and validity of the ARDE are examined through typical benchmark problems. It could be found through benchmark problems that the ARDE can find a global minimum with the small number of function evaluations in comparison with basic Differential Evolution. ©2013 The Japan Society of Mechanical Engineers.
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北山, 哲士 ; 山崎, 光悦
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金沢大学工学部
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