On the System for Setting the Cutting Conditions Based on the Evaluation of Cutting State (2nd Report)
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- Takuma Masanori
- 正会員 福井大学工学部(現,関西大学工学部)
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- Shibasaka Toshiroh
- 正会員 福井大学工学部
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- Kawai Masaki
- 学生会員 福井大学大学院(現,三菱電機(株))
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- Teshima Toshio
- 正会員 福井大学工学部
Bibliographic Information
- Other Title
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- 加工状態の評価に基づく切削条件設定システムに関する研究(第2報)
- 加工状態の評価に基づく切削条件設定システムに関する研究-2-ニューラルネットワークによる探索の効率化
- カコウ ジョウタイ ノ ヒョウカ ニ モトズク セッサク ジョウケン セッテイ
- Improvement of Searching Efficiency Using Neural Network
- ニューラルネットワークによる探索の効率化
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Abstract
The skilled machinist can continuously adjust operations in terms of machining efficiency, machinability and cost. This adjustment is done by means of cutting conditions. In the previous paper, the system was proposed to simulate the machinist's thinking process which set the appropriate cutting conditions based on evaluation of cutting state. The process was constructed by fuzzy integral and hill-climbing. But it had become a serious problem that the try counts on searching of the appropriate cutting conditions using the previous system were nearly four times as many as the skilled machinist's. In this parer, it proposed that the skilled machinist's know-how for improvement of searching efficiency is systematized by neural network. This system which is constructed by introducing the neural network into the previous system is superior to the previous system. By using this system, the cutting conditions which can get the high evaluation is obtained, and the try counts was half as many as the counts using the previous system. The accuracy of the system's output and the improvement of searching efficiency were examined by the experiments under various cutting conditions in turning, and the validity of this system was confirmed.
Journal
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- Journal of the Japan Society for Precision Engineering
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Journal of the Japan Society for Precision Engineering 61 (2), 218-222, 1995
The Japan Society for Precision Engineering
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Details 詳細情報について
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- CRID
- 1390282679742727552
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- NII Article ID
- 110001367512
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- NII Book ID
- AN1003250X
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- ISSN
- 1882675X
- 09120289
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- NDL BIB ID
- 3597478
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed