Journal ArticleDOI
Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments
TLDR
In this article, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling using the Taguchi design of experiments (DoE) method.Abstract:
In this paper, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling The data used for the training and checking of the networks’ performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force Using feedforward artificial neural networks (ANNs) trained with the Levenberg–Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5×3×1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 186% and to be consistent throughout the entire range of valuesread more
Citations
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Journal ArticleDOI
A new hybrid artificial neural networks and fuzzy regression model for time series forecasting
TL;DR: Based on the basic concepts of ANNs and fuzzy regression models, a new hybrid method is proposed that yields more accurate results with incomplete data sets and the empirical results of financial market forecasting indicate that the proposed model can be an effective way of improving forecasting accuracy.
Journal ArticleDOI
Study on prediction of surface quality in machining process
TL;DR: In this paper, the authors reviewed the methodologies and practice that are being employed for the prediction of surface profile and roughness, each approach with its advantages and disadvantages is summarized.
Journal ArticleDOI
A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction
Shiwei Yu,Kejun Zhu,Fengqin Diao +2 more
TL;DR: The results of application on oil reservoir prediction show that the proposed model with comparatively simple structure can meet the precision request and enhance the generalization ability.
Journal ArticleDOI
Design of experiments and focused grid search for neural network parameter optimization
Fabricio J. Pontes,G.F. Amorim,Pedro Paulo Balestrassi,Anderson Paulo de Paiva,João Roberto Ferreira +4 more
TL;DR: The proposed tuning method leads to significant reduction of roughness prediction errors in machining operations in comparison to techniques currently used, and constitutes an effective option for the systematic design models based on ANN for prediction of surface roughness, filling the gap reported in the literature.
Journal ArticleDOI
Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method
TL;DR: In this paper, the effect of milling parameters on surface roughness and cutting force using an uncoated carbide insert was investigated using an ANN and regression analysis with a mean squared error equal to 2.25% and 6.66% respectively.
References
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Book
Taguchi techniques for quality engineering
TL;DR: Taguchi as discussed by the authors presented Taguchi Techniques for Quality Engineering (TQE), a technique for quality engineering in the field of high-level geometry. Technometrics: Vol. 31, No. 2, pp. 253-255.
Journal ArticleDOI
A Mechanistic Model for the Prediction of the Force System in Face Milling Operations
Journal ArticleDOI
An in-process surface recognition system based on neural networks in end milling cutting operations
TL;DR: In this paper, an in-process surface recognition system was developed to predict the surface roughness of machined parts in the end milling process to assure product quality and increase production rate by predicting the surface finish parameters in real time.
Journal ArticleDOI
On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion
Riadh Azouzi,Michel Guillot +1 more
TL;DR: In this paper, the authors examined the feasibility of an intelligent sensor fusion technique to estimate on-line surface finish (Ra) and dimensional deviations (DD) during machining and presented a systematic method for sensor selection and fusion using neural networks.
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