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
Multi-response Optimization using TGRA for End Milling of AISI H11 Steel Alloy Using Carbide End Mill
TL;DR: In this article, the influence of the cutting speed, feed rate and depth of cut in end milling onto the surface roughness (SR) and metal removal rate (MRR) was investigated.
Journal ArticleDOI
Robust design optimisation via surrogate network model and soft outer array design
TL;DR: This study presents a soft computing-based robust optimisation that merges control and noise factors into a combined experimental design to establish a surrogate using artificial neural network and provides a superior robust optimum using a much smaller sample and less controlling cost compared with Taguchi method and a conventional response surface method.
Journal ArticleDOI
Boosting Projections to improve surface roughness prediction in high-torque milling operations
TL;DR: Boosting Projections is used to predict surface roughness in high-torque, high-power face milling operations and demonstrates a higher prediction accuracy when compared with single multilayer perceptrons, decision trees and classical ensemble methods.
Journal ArticleDOI
Multivariate global index and multivariate mean square error optimization of AISI 1045 end milling
Robson Bruno Dutra Pereira,Carlos Andrés Arango Hincapié,Paulo Henrique da Silva Campos,Anderson Paulo de Paiva,João Roberto Ferreira +4 more
TL;DR: In this paper, a sequential methodology on roughness surface multivariate modelling and optimization is presented, where a complete factorial design was used with center points, and the hardness of machined surfaces as a covariate was taken into account.
Quality function deployment analysis based on neural network and statistical results
TL;DR: In this paper, the authors focused on the development of general QFD for machine specification selection so that it later can be used for any kind of machine evaluation prior to purchasing the machines.
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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