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 and Prediction of Cutting Force and Surface Roughness in End Milling Process of AISI 304 Stainless Steel
TL;DR: In this article, the effects of spindle speed, feed rate and depth of cut have been studied on the cutting force and surface roughness using Taguchi's 27 orthogonal arrays.
Journal ArticleDOI
Calculation of fractal dimension based on artificial neural network and its application for machined surfaces
TL;DR: It is found that the [Formula: see text] values of machined surfaces could be influenced significantly by the feed rate, while the cutting speed and depth are relatively irrelevant.
Journal ArticleDOI
Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy
Song Zhang,Jianfeng Li +1 more
TL;DR: In this article, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process using a large number of milling experiments on Ti-6Al-4V alloy using the uncoated carbide tools.
Journal ArticleDOI
Prediction of thrust forces and hole diameters using artificial neural networks in drilling of AISI D2 tool steel with cemented carbide tools
TL;DR: In this article, the effects of cutting speed, feed rate and different types of coating materials on thrust force and hole diameter were investigated in drilling of AISI D2 cold work tool steel.
Journal ArticleDOI
Surface roughness analysis and optimization for the CNC milling process by the desirability function combined with the response surface methodology
TL;DR: In this article, an optimization strategy for the CNC pocket milling process based on the desirability function approach (DFA) combined with the response surface methodology (RSM) was proposed.
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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