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
CNC Turning Parameter Optimization for Surface Roughness of Aluminium-2014 Alloy Using Taguchi Methodology
TL;DR: In this paper, the surface roughness of aluminum-2014 alloy was optimized by adjusting the machining parameters of computer numerical control (CNC) turning, including, cutting speed, depth of cut and feed rate.
Journal Article
Statistical Analysis Of Wire Electrical Discharge Machining On Surface Finish
S V Subrahmanyam,M. M. M. Sarcar +1 more
TL;DR: In this paper, the effects of eight input process parameters on surface finish during the machining of EN-31 using Taguchi L36(2 1 3 7 ) orthogonal array (OA) as design of experiments (DOE).
Journal ArticleDOI
Evaluación Experimental del Desempeño del Proceso de Fresado Frontal del Acero ABNT 1045 para Herramientas con Diferentes Números de Aristas
TL;DR: In this paper, a diseno experimental factorial, with tres replicas and 95% of confianza, was used to evaluate the fresado frontal asimetrico of acero ABNT 1045.
Journal ArticleDOI
Predicting of Open Source Software Component Reusability Level Using Object-Oriented Metrics by Taguchi Approach
TL;DR: A mathematical model is proposed to establish the relationship between the reusability of CK-metrics and the results indicate that the OSS component reUSability level is 0.698194.
Journal Article
Designing an Artificial Neural Network Based Model for Online Prediction of Tool Life in Turning
TL;DR: In this paper, an artificial neural network model was developed to predict the tool wear and tool life in turning process using cutting parameters and cutting forces as input and tool flank wear rates were regarded as target data for creating the online prediction system.
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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