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
Optimization of CNC End Milling Process Parameters of Low-Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis
R. Suresh Kumar,S. Senthil Kumar,K. Murugan,B. Guruprasad,Sreekanth Manavalla,S. Madhu,M. Hariprabhu,S. Balamuralitharan,S. Venkatesa Prabhu +8 more
TL;DR: In this article, the authors focused on improving the machining economy by enhancing the surface integrity and tool life with minimum resources using Box-Behnken design and grey regression analysis.
Proceedings ArticleDOI
A prediction model based on neural network and fuzzy Markov chain
TL;DR: The addition of fuzzy Markov chain to ANN method can prominently improve the prediction quality and was applied to analysis the properties of nano-composite materials and showed it was effective and better than neural network model.
Journal ArticleDOI
A study of parameters relationship to backcutting phenomena during high speed end milling of AISI H13
Erry Yulian Triblas Adesta,Afifah Mohd Ali,Delvis Agusman,Muhammad Riza,Mohammad Yuhan Suprianto +4 more
TL;DR: The result has shown that the pattern of surface roughness was not sufficient enough to compare between the surface with backcutting and without backcutting, and the backcutting phenomena were seen mostly in combination of medium to high cutting speed andmedium to high feedrate.
Artificial Intelligence Techniques for Machining Performance: a Review
TL;DR: In this article, the authors reviewed the approaches of artificial neural network (ANN) on machining performance and found that ANN has the ability in generalizing the system characteristics by predicting values close to the actual measured ones.
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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