Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process

Briceno, J.; El-Mounayri, Hazim; Mukhopadhyay, Snehasis

doi:10.1016/s0890-6955(02)00008-1

Cited by 62 publications

(29 citation statements)

References 4 publications

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“…The efficiency of a neural network model was estimated by conducting the numerical simulations and experiments. Several studies had been focussed on the optimization of important cutting process parameters using intelligent optimization techniques like genetic algorithms and radial basis neural networks (Briceno et al 2002;Mounayri et al 2010;Mounayri et al 2005). Using, different neural network models and optimization algorithms the impact of the surface roughness on the process parameters are extensively studied (Palanisamy et al 2007;Palanisamy and Kalidass 2014;Zain et al 2011;Saffar and Razfar 2010).…”

Section: Introductionmentioning

confidence: 99%

Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach

Shaik

Srinivas

2017

Mech Adv Mater Mod Process

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Background: Machining using vertical CNC end mill is popular in the modern material removal industries because of its ability to remove the material at a fast rate with a reasonably good surface quality. Methods: In this work, the influence of important common machining process variables like feed, cutting speed and axial depth of cut on the output parameters such as surface roughness and amplitude of tool vibration levels in Al-6061 workpieces has been studied. With the use of experimental result analysis and mathematical modelling, correlations between the cutting process conditions and process outputs are studied in detail. The cutting experiments are planned with response surface methodology (RSM) using Box-Behnken design (BBD).

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Section: Introductionmentioning

confidence: 99%

Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach

Shaik

Srinivas

2017

Mech Adv Mater Mod Process

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“…He reported that in most cases, these four statistical parameters tend to reach a constant value for a discrete time history segment of at least 6000 points of data. Briceno et al [8] used maximum, minimum, mean and standard deviation value of resultant force which represented the important characteristic of a continuous force pattern in selecting an artificial neural network for efficient modeling and accurate simulation of the milling process. Chungchoo and Saini [9] used skewness and kurtosis of force band as input to enhance the accuracy of tool wear prediction in turning operation.…”

Section: Introductionmentioning

confidence: 99%

Flank wear and I-kaz 3D correlation in ball end milling process of Inconel 718

Tahir¹,

Ghani²,

Nuawi³

et al. 2015

J. Mech. Eng. Sci.

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Tool wear may deteriorate the machine product quality due to high surface roughness, dimension exceeding tolerance and also to machine tool itself. Tool wear monitoring system is vital to be used in machining process to achieve high quality of the machined product and at the same time improve the productivity. Nowadays, many monitoring system developed using various sensor and statistical technique to analyze the signals being used. In this paper, I-kaz 3D method is used to analyze cutting force signal in milling process of Inconel 718 for monitoring the status of tool wear in milling process. The results from analyzing cutting force show that I-kaz 3D coefficient has a correlation with cutting tool condition. Tool wear will generate high value of I-kaz 3D coefficient than the sharp cutting tool. Furthermore, the three dimension graphical representation of I-kaz 3D for all cutting condition shown that the degree of scattering data increases with tool wear progression.

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“…Some researchers have concentrated on the development of thermal error models by using various modeling methodologies during the last few decades, which reveals the relationship between the thermal error and temperature from a couple of different perspectives. These methods include least square fitting model [2], finite element method [3], time series model [4][5][6], artificial neural network [7][8][9][10][11][12], grey model (GM) [13][14][15], system identification [16], and etc. Some successful applications of thermal error reduction based mainly on those modeling methods can be found in both research laboratories and industrial facilities [17].…”

Section: Introductionmentioning

confidence: 99%

Machine tool thermal error modeling and prediction by grey neural network

Zhang

Yang

Jiang

2011

Int J Adv Manuf Technol

100

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This paper proposes a novel modeling methodology for machine tool thermal error. This method combines the advantages of both grey model and artificial neural network (ANN) in terms of data processing. To enhance the robustness and the prediction accuracy, two kinds of grey neural network, namely serial grey neural network (SGNN) and parallel grey neural network (PGNN), are proposed to predict the thermal error. Experiments on the axial directional spindle deformation on a five-axis machining center are conducted to build and validate the proposed models. The results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness. So the new models are more suitable for complex working conditions in industrial applications.

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Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process

Cited by 62 publications

References 4 publications

Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach

Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach

Flank wear and I-kaz 3D correlation in ball end milling process of Inconel 718

Machine tool thermal error modeling and prediction by grey neural network

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