應用迴歸類神經預測銑削加工之表面粗糙度

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應用迴歸類神經預測銑削加工之表面粗糙度

Application of regression neuron in predicting surface roughness in end milling operations

張竹賢 , Masters　　Advisor：黃博滄　　

繁體中文

迴歸分析；類神經網路；預測；表面粗糙度；銑削加工； regression ； surface roughness ； predict ； end milling ； artificial neural network

Abstract │ Reference (50) │ Altmetrics

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Every industry hopes to reduce the cost, the waste and promote the productivity and efficiency to achieve the goal and vision. For achieving this goal, quality control is the most important role in manufacturing processing. Therefore, how to reach quality control efficiency must using some kinds of method to reduce the fail rate and promote the productivity, ex: Quality 7 Tools, 6 sigma and QCC etc. However, using this methods to achieve quality control, we usually through workpiece measurement.

In the manufacturing industry, the surface roughness is an important index to evaluate product quality. Surface roughness direct impacting surface wear resistance, fatigue strength, reliability and leak-proof quality, thermal conductivity and drive precision and so on, so the surface roughness is  an important index assessing the surface conditions, reflecting the quality, especially in the machinery processing has become an essential quality requirements.

In the past, has many of constructing a prediction decision-making system research. But how to promote the decision-making system precision is researchers hope. This research using combination of Regression and Neural Network to develop a regression neural network prediction model, the purpose is predicting the surface roughness efficiency, reducing the variation of data and promote system precision.

Reference ( 50 ) 〈TOP〉

[2] Chen, J.C., M.S. Lou, 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) (2000) 358–368.
連結：
[3] Huang, B.P., Chen, J.C., An in-process neural network-based surface roughness prediction system using a dynamometer in end milling operations, Int. J. Adv. Manuf. Technol. 21 (2003) 339–347.
連結：
[4] Ho ,W.H., Tsai, J.T., Lin, B.T., Chou, J.H., Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm, Expert Systems with Applications, 36 (2009) 3216–3222
連結：
[5] Karayel, D., Prediction and control of surface roughness in CNC lathe using artificial neural network. J. Mater. Process. Tech (2008).
連結：
[6] SubbaNarasimha, P. N., Arinze, B., & Anandarajan, M. The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: Exploration of some issues. Expert Systems with Applications, 19 (2000) 117–123.
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