平面銑削之灰色即時可調式學習表面粗糙度預測系統開發

張黃傑

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平面銑削之灰色即時可調式學習表面粗糙度預測系統開發

The Development of a Gray In-process Adaptive Learning Surface Roughness Prediction System in Milling Operations

張黃傑 , Masters　　Advisor：黃博滄　　

繁體中文

表面粗糙度；灰色關聯分析；灰色預測； Surface roughness ； Grey relational analysis ； Grey prediction

Abstract │ Reference (66) │ Times Cited (1) │ Altmetrics

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With competitions between enterprises, it is importance and necessary to control costs and quality of products. Therefore, the development of prediction system is tending to become a significant role in last decades. The models of the system including model simulation, statistical prediction, regression analysis and various soft computing have been generally used in the prediction system. its accuracy is also increasing gradually. However, the major component of building the models is a large number of data. Under the circumstance, the prediction system is difficult to be implemented under the small batch and new producing lines situations. The purpose of this study is to develop a real-time adaptive learning prediction system, which can be utilized in a small sample, such as production shortfall in the early stages of producing. Moreover, this model can be adjusted in accordance with different specifications of operation conditions.

This research is to develop a gray prediction system associated with gray relational analysis, which is use select the input factors of the system. Furthermore, a gray prediction theory would work as framework to build a real-time adaptive learning prediction system with a small number of data. The gray prediction implemented the result of accumulated generating operation, the calculation of gray values, effect data and establishes information anticipation with a small-scale model through the trend of characteristics of. This study applied the model to predict the surface roughness in milling operations. The correlation between cutting forces and surface roughness has been analyzed. With proper cutting force as input factors, a gray real time adaptive learning surface roughness prediction system in milling operations is built. The system does not need any training data in advance to construct the prediction model. It would only use limited data from trial production or set up to create the real-time prediction model for each production batch.

To evaluate reliability and accuracy of the system, the model would be compared with a traditional neural network prediction model of a large sample. The result of comparison between these two models shows no significant difference. The time and cost of collecting and data and training model are significantly reduced.

Reference ( 66 ) 〈TOP〉

Ak, R., Li, Y., Vitelli, V., Zio, E., López Droguett, E., & Magno Couto Jacinto, C. (2013). NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment. Expert Systems with Applications, 40(4), 1205-1212.
連結：
Altintas, Y., & Yellowley, I. (1989). In-Process Detection of Tool Failure in Milling Using Cutting Force Models. Journal of Manufacturing Science and Engineering, 111(2), 149-157.
連結：
Bouacha, K., Yallese, M. A., Mabrouki, T., & Rigal, J. F. (2010). Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. International Journal of Refractory Metals and Hard Materials, 28(3), 349-361.
連結：
Bouzakis, K. D., Aichouh, P., & Efstathiou, K. (2003). Determination of the chip geometry, cutting force and roughness in free form surfaces finishing milling, with ball end tools. International Journal of Machine Tools and Manufacture, 43(5), 499-514.
連結：
Cakir, M. C., & Isik, Y. (2005). Detecting tool breakage in turning aisi 1050 steel using coated and uncoated cutting tools. Journal of Materials Processing Technology, 159(2), 191-198.
連結：

Times Cited (1) 〈TOP〉

林明慶(2016)。建構田口殘差修正GM(1,1)灰模型之微鑽孔刀具壽命預測系統。中原大學工業與系統工程研究所學位論文。2016。1-89。

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