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
Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks
TL;DR: The cutting tool stresses (normal, shear and von Mises) in machining of nickel-based super alloy Inconel 718 have been investigated in respect of the variations in the cutting parameters (cutting speed, feed rate and depth of cut).
Journal ArticleDOI
Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351
TL;DR: In this article, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R ) values in Al alloy 7075-T7351 after face milling machining process.
Journal ArticleDOI
The influence of component inclination on surface finish evaluation using digital image processing
P. Priya,B. Ramamoorthy +1 more
TL;DR: In this paper, an artificial neural network (ANN) is trained and tested to estimate the optical roughness parameters using the input obtained from the digital images of inclined surfaces which include optical roughs parameters estimated and angle of inclination of test parts.
Journal ArticleDOI
Experimental Investigation of Surface Roughness and Power Consumption in Turning Operation of EN 31 Alloy Steel
TL;DR: In this article, an experimental study of power consumption and roughness characteristics of surface generated in turning operation of EN-31 alloy steel with TiN+Al2O3+TiCN coated tungsten carbide tool under different cutting parameters is presented.
Journal ArticleDOI
Surface roughness prediction using Taguchi-fuzzy logic-neural network analysis for CNT nanofluids based grinding process
S. Prabhu,M. Uma,B. K. Vinayagam +2 more
TL;DR: The present study highlights the Taguchi design of experiment techniques proved to be an efficient tool for the design of neural networks’ surface roughness to predict in the grinding process, where CNT mixed nanofluids are used as dielectric for machining AISI D3 Tool steel material.
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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