建構以銑削尺寸為依據之灰色刀具壽命預警系統

江謝宇烜

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建構以銑削尺寸為依據之灰色刀具壽命預警系統

Construction of Grey Tool Life Alerting System Based on Milling Dimension

江謝宇烜 , Masters　　Advisor：黃博滄　　

繁體中文

加工尺寸；灰色理論；刀具壽命；拋棄式刀片；移動全距管制圖； Milling dimension ； Grey theory ； Tool life ； Insert ； MR chart

Abstract │ Reference (45) │ Altmetrics

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As the market trend changes these years, small volume, large variety and customization substitute mass production in mechanical manufacturing process. We have to reach customers’ request quickly because of sharp product cycle declination. Consequently, many researchers have proposed models and methods, such as prediction, simulate, and soft computing before production to build system. However,   there is a disadvantage above the methods. That is, we must use large amounts of data to build models, which is unfavorable to productions nowadays. It’s inconvenient that we even need to change parameter because of different manufacturing conditions. This research will build a tool life predict system through grey theory which is aimed at milling dimension. We hope that we can get predictions of tools by less data used in the system. Besides, the system won’t be influenced by manufacturing parameter and materials.
This research build a grey tool life prediction system, using the Grey Generating Operation in grey theory. A method which emphasizes the input data trend and uses its small sample feature as the fundamental structure for building a real time prediction system. Grey prediction is a small-scale prediction model built upon accumulated generating sequence and the forecasting characteristics among data. When data input grey prediction system be used Accumulated Generating Operation (AGO). It is used on CNC milling process in this research in which a tool’s operating life is predicted based on a small amount of dimension data.
To prove the proposed method is both accurate and reliable, setting same parameters and other a parameter. Input the less data is going to predict in grey prediction system, taking determine tool's life and investigate its accuracy to verify the feasibility of the prediction system proposed in this research.	
In the actual value and predicted value error to do accurate analysis. Predictive accuracy were higher than 90% in experiment. So this grey tool life prediction system is up to par. Correct predict value can alert earlier than actual value. To avoid there have been bad for a while yet alert in milling process.

Reference ( 45 ) 〈TOP〉

Ahilan, C., Kumanan, S., Sivakumaran, N., & Edwin Raja Dhas, J. (2013). Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Applied Soft Computing. 13(3). 1543-1551.
連結：
Caballero-Ruiz, A., Ruiz-Huerta, L., Baidyk, T., & Kussul, E. (2007). Geometrical error analysis of a CNC micro-machine tool. Mechatronics. 17(4-5). 231-243.
連結：
D’Addona, D. M., & Teti, R. (2013). Image Data Processing via Neural Networks for Tool Wear Prediction. Procedia CIRP. 12. 252-257.
連結：
Deng, J.-L. (1982). Control problems of grey systems. Systems & Control Letters. 1(5). 288-294.
連結：
Ghosh, N., Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., & Chattopadhyay, A. B. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing. 21(1). 466-479.
連結：

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