A new artificial neural network approach to modeling ball-end milling

El-Mounayri, Hazim; Briceno, J.; Gadallah, Mohamed H.

doi:10.1007/s00170-009-2217-2

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…The neural network was trained by a random training data set and a feed-forward back-propagation algorithm was used for the train the network. The back propagation algorithm 18,19 is works based on the method of gradient descent and it updates the mean square error values with respect to the target and training values by the iteration of weights. In this ANN model, the hidden layer and output layer activation functions were fixed as logsig and tansig, respectively, for the output response of the surface roughness.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

Madhesan¹,

Venkatesan²,

Kaviarasan³

2018

Mater. Tehnol.

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Magnesium alloys are advanced, light materials used widely in industries and milling is one of the material removal processes that are extensively used. In this present study, the experimental work has been carried out based on a Box-Behnken design by mainly considering three factors, i.e., cutting speed, feed, and depth of cut. The first part of in this study, the effects of Response Surface methodology (RSM) and Artificial Neural Network (ANN) models were evaluated and compared. The RSM and ANN models provide the average error of 2.40 % and 1.52 %, respectively, it recommends that ANN is a more efficient methodology for the prediction of the optimal output response than RSM. The predicted model has been coupled with evolutionary optimization technique genetic algorithm (GA) to determine the optimum cutting parameters to attain the minimal surface roughness with respect to the wide ranges of machining parameters and GA suggest the surface roughness of 1.0926 μm with the optimized machining parameters. The evaluation of these observations proves that the proposed methods are capable of determining the optimum machining parameters for modern materials. Keywords: artificial neural network, genetic algorithm, magnesium alloy, milling, optimization Zlitine na osnovi magnezija (Mg) so napreden lahek material, ki se pogosto uporablja v razli~nih vejah industrije. Njegova mehanska obdelava z rezkanjem je ena od najpogostej{ih metod odstranjevanja materiala za dokon~no oblikovanje razli~nih industrijskih izdelkov. V predstavljeni {tudiji avtorji opisujejo eksperimentalno delo izvedeno na osnovi Box-Behnkenovega dizajna z upo{tevanjem treh faktorjev: rezalne hitrosti, pomika in globine reza. V prvem delu te {tudije so avtorji ovrednotili in primerjali u~inke dveh uporabljenih modelov: metodologije reakcije povr{ine (RSM; angl.: Response Surface Methodology) in umetne nevronske mre`e (ANN, angl: Artificial Neural Network). RSM in ANN modela sta predvidela povpre~no 2,40 % oz. 1,52 % napako. To pomeni, da je ANN u~inkovitej{a methodologija za napoved optimalne (reakcije) storilnosti. Model za prognozo so avtorji zdru`ili z evolucijsko optimizacijsko tehniko genetskih algoritmov (GA) in na ta na~in dolo~ili optimalne rezalne parametre za dosego minimalne povr{inske hrapavosti v obmo~ju dokaj {iroko izbranih parametrov mehanske obdelave. GA napoveduje povr{insko hrapavost 1.0926 μm pri optimiziranih parametrih mehanske obdelave. Ovrednotenje teh opazovanj je potrdilo da, je s predlaganimi metodami mo`no dolo~iti optimalne parametre mehanske obdelave modernih materialov. Klju~ne besede: umetna nevronska mre`a, genetski algoritem, zlitina na osnovi Mg, rezkanje, optimizacija

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

Madhesan¹,

Venkatesan²,

Kaviarasan³

2018

Mater. Tehnol.

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“…El-Mounayri et al [39] developed an ANN model for prediction of cutting forces in a ball end milling process. Radial basis was applied for its advantages from the view of convergence speed.…”

Section: Cutting Forcesmentioning

confidence: 99%

Application of ANN in Milling Process: A Review

Al-Zubaidi

Ghani

Haron

2011

Modelling and Simulation in Engineering

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In recent years the trends were towards modeling of machining using artificial intelligence. ANN is considered one of the important methods of artificial intelligence in the modeling of nonlinear problems like machining processes. Artificial neural networks show good capability in prediction and optimization of machining processes compared with traditional methods. In view of the importance of artificial neural networks in machining, this paper is an attempt to review the previous studies and investigations on the application of artificial neural networks in the milling process for the last decade.

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“…The RBF model was proven to be faster than an MLP model, but it was inferior in terms of accuracy. Elmounayri et al [19] employed an RBF model for the prediction of cutting forces during ball-end milling. The model had four inputs, related to the process parameters and four outputs, namely the maximum, minimum, mean, and standard deviation of instantaneous cutting force.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

et al. 2019

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Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

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A new artificial neural network approach to modeling ball-end milling

Cited by 20 publications

References 9 publications

Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

Evaluation of the surface integrity in the milling of a magnesium alloy using an artificial neural network and a genetic algorithm

Application of ANN in Milling Process: A Review

A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

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