Control of tool temperature using neural network for machining materials with low thermal conductivity

• Autorzy: Soe Y. H. , Tanabe I. , Iyama T. , Abe Y.
• Języki publikacji: EN

Abstrakty

EN

Recently titanium and nickel alloys have become pre-eminent for aeronautic and astronautic parts. Since these cutting and becomes severely demaged. It is important to control cutting tool temperature. In this paper,the control system of tool tip temperature using inverse analysis of neural network for machining these materials was developed and evaluated. The neural network between cutting conditions and tool temperature was firstly created by a set of teaching data. Then, a mathematical model using algebra was developed. Cutting speed was selected as parameter to be controlled in reducing tool temperature. The relationship between the optimum cutting speed and cutting time was calculated with the inverse analysis of neural network by pre-reading of NC program before cutting. The tool temperature can be maintained at the desired value. The developed system is evaluated by the expaeriments using the turning process and workpiece of Ti6Al4V. From the results, it is concluded that; (1) Tool tip temperature can be controlled by using the proposed inverse analysis of the neural network, (2) CThe cutting tool life can be maintained by this method, for cutting materials with low thermal conductivity.

Słowa kluczowe

EN

titanium alloy; nickel alloy; neural network; tool; tool temperature

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2010
• Tom: Vol. 10, No. 3
• Strony: 78--89
• Opis fizyczny: Bibliogr. 10 poz., tab., rys.

Twórcy

autor

Soe Y. H.

• Nagaoka University of Technology, Dept. of Information Science and Control Engineering

autor

Tanabe I.

• Nagaoka University of Technology, Dept. of Mechanical Engineering

autor

Iyama T.

• Nagaoka University of Technology, Educational affairs section, Mechanics and Machinery Group

autor

Abe Y.

• Nagaoka University of Technology, Dept. of Mechanical Engineering

Bibliografia

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• [6] TANABE I., IKEDA S., URANO K., 2003, Esimation of Optimum Temperature for Cooling Oil on a Spindle Using Inverse Analysis of Neural Network (Effect of Relearning), Transactions of the Japan Society of Mechanical Engineer, Series C, 69/679, 819-824.
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