運用主成份田口類神經模組建立端銑表面粗糙度可調式控制系統

賴柏辰

doi:10.6840/CYCU.2010.00454

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運用主成份田口類神經模組建立端銑表面粗糙度可調式控制系統

A PCA-Taguchi-Neural Adaptive Control of surface finishing in end milling operations

賴柏辰 , Masters　　Advisor：黃博滄　　

繁體中文 DOI： 10.6840/CYCU.2010.00454

表面粗糙度；田口方法；類神經網路；端銑；主成份分析法； the principal component analysis ； surface roughness ； end-milling ； Neural Network ； Taguchi

Abstract │ Reference (32) │ Times Cited (1) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Surface roughness is an important quality index of machined parts. It usually needs additional production costs or to reduce productivity to result in a good surface roughness. To improve this problem, many researches suggested that predict the surface roughness through input parameters before processing; nevertheless, when its prediction does not meet quality specification, it still brings problems such as defective products or stop work. Therefore, this study proposed a real-time and two-way adjustable concept of quality measurement systems for surface roughness. The study used PCA-Taguchi-Neural Model to establish a control system of surface roughness in end milling operations, which can adjust parameters during machining processes, makes the surface roughness control in the range of quality specification, and reduces the cost of time of measurement and adjustment.

In the past, the research, which considered only the setting of processing parameters, is not enough. To be more accurate, other factors such as division of prediction and requirement, and the real surface roughness should also be considered, and therefore dynamometer was used in this uncontrolled processing system to increase the precision of surface roughness control. From the research, an empirical approach was applied to discover the proper cutting force signals, the average resultant peak force in XY plane (Fap) and the absolute average force in the Z direction (Faz). These two forces were employed to represent the uncontrollable cutting tool conditions for surface roughness control.  
    
   In a PCA-Taguchi-Neural Model that established a control system of surface roughness in end milling operations, the major decision-making system is neural networks. Neural networks have been widely used in prediction, diagnosis and adjustment. Therefore, this study used neural network as a decision-making system. Furthermore, to enhance the robustness of the module, the study used the experimental data of end milling operations, through PCA to convert the training source of the neural network, and the training process combined with Taguchi method to optimize the training parameters of the neural network. Four different models were used to verify the performance of this study, the results showed a significant effect that applied the principal component analysis in NN with spindle speed as one of input parameters, provided a better result than others.

Reference ( 32 ) 〈TOP〉

[2] Huang, B. P., Chen, J. C. (2003). An in-process neural network-based surface
連結：
roughness prediction (INN-SRP) system using a dynamometer in end milling operations, Int. J. Adv. Manuf. Technol., 21, 339–347.
連結：
[3] Singh, S.K., Srinivasan, K.,Chakraborty, D.(2004). Acoustic characterization and prediction of surface roughness, Journal of Materials Processing Technology, 152, 127-130.
連結：
[4] Chen, J. C., Lou, M. S. (2000). Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations, Int. J. Comput. Integr. Manuf., 13 (4), 358–368.
連結：
[5] Benardos, P. G., Vosniakos, G. C. (2002), Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments, Robotics and Computer Integrated Manufacturing, 18, 343-354.
連結：

Times Cited (1) 〈TOP〉

古孟晨(2011)。發展可調式加工參數於端銑表面粗糙度品質控制系統之研究。中原大學工業與系統工程研究所學位論文。2011。1-75。

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