低熱伝導材料加工のためのニューラルネットワークを用いた工具温度管理

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Recently titanium and nickel alloys have become eminent for making aeronautic and astronautic parts. Since both titanium and nickel alloys have very low thermal conductivity, the cutting tool suffers from most of the heat generated during cutting process and becomes severely damaged. Therefore, there is a requirement for a durable tool with excellent control of cutting condition. In this research, the control of tool temperature using the neural network for machining material with low thermal conductivity were developed and evaluated. The neural network between the cutting conditions and the temperatures of tool was firstly made by the teaching data. Then mathematical model using algebra was made by using the neural network. This mathematical model is very convenient tool with simple and economical. Before cutting process, NC program was initially read and the relationship between the optimum cutting speed and cutting time was calculated by using the neural network. Therefore cutting speed was controlled to reduce the maximum temperature on the edge of tool. The temperature on the edge of the tool can be kept at desired value and thus, the tool life will also be kept longer. The optimum cutting speed was used during the cutting. These were evaluated by turning experiments using assayed materials of Ti6Al4V. It is concluded from the results that; (1) Temperature change of tool was controlled by using the neural network during cutting, (2) Even work piece with very low thermal conductivity, the tool life was kept longer by this control method.

低熱伝導材料加工のためのニューラルネットワークを用いた工具温度管理

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