Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays

Pontes, Fabrício José; Paiva, Anderson Paulo de; Balestrassi, Pedro Paulo; Ferreira, João Roberto; Silva, Messias Borges da

doi:10.1016/j.eswa.2012.01.058

Cited by 86 publications

(41 citation statements)

References 34 publications

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“…Drozda [10] and ASM [11] proved that honing is currently the only process that is able to achieve the double requirement for surface finish and cross-hatch pattern that are necessary for manufacturing cylinder liners. best configuration for neural networks used to predict roughness in turning processes [17]. Ortiz-Rodriguez et al also used Taguchi DOE for designing neural networks [18].…”

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confidence: 99%

Indirect model for roughness in rough honing processes based on artificial neural networks

Sivatte-Adroer

Parra

Buj-Corral

et al. 2016

Precision Engineering

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In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were tested with different strategies, number of layers, number of neurons and transfer function. Best configuration for the network was searched by means of two different methods, trial and error and Taguchi design of experiments (DOE). In both cases, best configuration corresponds to a single network with two hidden layers. Once best configuration was found, a network was defined for obtaining honing parameters as a function of required roughness parameters related to Abbott-Firestone curve, Rk, Rpk and Rvk.

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confidence: 99%

Indirect model for roughness in rough honing processes based on artificial neural networks

Sivatte-Adroer

Parra

Buj-Corral

et al. 2016

Precision Engineering

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“…Their results showed that the RBFANN-based models achieved superior performance compared to the MLPANN. However, other studies have highlighted the advantages of using the RBFANN (Pontes, Paiva, Balestrassi, & Ferreira, 2012;Sideratos & Hatziargyriou, 2012;Wu & Liu, 2012;Yu, Xie, Paszczynski, & Wilamowski, 2011;Zhou, Ma, Li, & Li, 2012).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

Kayrı¹

2015

EDUC SCI-THEOR PRACT

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The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The participants' transcript scores were used as the target variables and the two methods were employed to test the predictors that affected these variables. The results show that the multilayer perceptron artificial neural network outperformed the radial basis artificial neural network in terms of predictive ability. Although the findings suggest that research in quantitative educational science should be conducted by using the former artificial neural network method, additional supporting evidence needs to be collected in related studies.

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“…The Taguchi process capability index can be considered an extension of the mean square error. The optimal level combination of each response value to the target is obtained in the case where the response variance is small; this approach is preferred for situations with target values and upper and lower tolerance lines [25][26][27]. Therefore, the evaluation index of the physical and mechanical properties of the cold roll-beating spline surface is constructed using the Taguchi process capability index to construct the optimization function, as shown in…”

Section: Optimization Of the Traceability Function Of Taguchi Processmentioning

confidence: 99%

Optimization of the Physical and Mechanical Properties of a Spline Surface Fabricated by High-Speed Cold Roll Beating Based on Taguchi Theory

Cui

et al. 2018

Mathematical Problems in Engineering

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The aim of this work is to control the physical and mechanical properties of a spline surface and achieve a reasonable choice of high-speed cold roll-beating processing parameters. The surface residual stress and surface work hardening at the indexing circle serve as the main evaluation indices of the physical and mechanical properties of the spline surface. The influence degree of the processing parameters on each evaluation index is analyzed using Taguchi theory. An optimized model for improving the Taguchi process capability index that combines Taguchi theory with entropy theory is established, and the integral process capacity index is optimized via the generalized price reduction gradient method. The results of the optimization and the verification test are implemented in a high-speed cold roll forming test for comparison. The results show that the influence of processing parameters on the physical and mechanical properties of the splash surface of the cold roll can be ordered as follows: feed rate > roll round radius > cold roll-beating speed. In addition, the spline surface physical and mechanical properties of the optimal processing parameters were obtained for the combination of a cold rolling speed of 1581 r/mm, feed rate of 42 mm/min, and roll round radius of 2 mm.

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Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays

Cited by 86 publications

References 34 publications

Indirect model for roughness in rough honing processes based on artificial neural networks

Indirect model for roughness in rough honing processes based on artificial neural networks

An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

Optimization of the Physical and Mechanical Properties of a Spline Surface Fabricated by High-Speed Cold Roll Beating Based on Taguchi Theory

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