Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling

Lela, Branimir; Bajić, Dražen; Jozić, Sonja

doi:10.1007/s00170-008-1678-z

Cited by 81 publications

(45 citation statements)

References 14 publications

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“…All datasets were then imported into a fuzzyset-based rule generation module to generate IF-THEN rules. Lela et al [27] examined the influence of cutting speed, feed, and depth of cut on surface roughness in face milling. Three different modeling methodologies (regression analysis, support vector machines, and Bayesian neural network) were applied to predict surface roughness.…”

Section: Review Of Literaturementioning

confidence: 99%

Pre-evaluation on surface profile in turning process based on cutting parameters

Chen

et al. 2009

Int J Adv Manuf Technol

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile preevaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.

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Section: Review Of Literaturementioning

confidence: 99%

Pre-evaluation on surface profile in turning process based on cutting parameters

Chen

et al. 2009

Int J Adv Manuf Technol

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“…D. Baijic et al studied the effect of speed, feed and depth of cut on the surface roughness during face milling. Regression analysis and neural networks had been applied on the experimentally determined data to predict surface roughness [3][4]. Sheth et al have analysed flashing process for better MRR and spread during manufacturing of precision steel ball, as here along with the MRR the geometry of ball is also important for better functioning of ball bearing [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation and Optimization of Flatness and Surface Roughness using Grey Relational Analysis for WCB Material during Face Milling Operation

Sheth¹,

George²

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Abstract:The right selection of the machining process parameters may help to achieve the desired dimensional and geometric tolerances, to meet the functional requirements. In the present study to know the effect of machining parameters, during face milling operation, on surface roughness and flatness various experiments are performed by varying spindle speed, feed rate and depth of cut. The work piece material is Wrought Cast Steel grade B (WCB), as it widely used in manufacturing of valves due to its less cost. Experiments are performed using 2 3 full factorial designs with four centre points. The values of flatness and surface roughness are critical in the case of dual plate check valve, as it affects a lot during leakage testing. To achieve the desire value of flatness and surface roughness simultaneously machining parameters need to be controlled; hence the research is carried out and Grey Relational Analysis (GRA) of the data is carried out. The flatness and surface roughness is measured using CNC Coordinate Measuring Machine (CMM) and surface roughness tester respectively. The GRA gives the second run order as the optimized one. The second run order is having spindle speed of 1200 rpm, feed rate of 150 mm/min and dept of cut 0.1 mm. This run order gives the optimum value of surface roughness is 2.1035 µm and flatness is 0.019 mm.

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“…The results of all three methods are satisfactory but the test performance of ANFIS is better than ANN and MRA. In paper [17] the authors analyse the influence of cutting speed, feed, and depth of cut on surface roughness in face milling process. For roughness modelling, based on the data collected by the planned experiment, they apply three methodologies: regression analysis, support vector machines and Bayesian neural network (BNN).…”

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Surface Roughness in Face Milling

Šimunović¹,

Šimunović²,

Šarić³

2013

Int. j. simul. model.

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The paper presents a research on machined surface roughness in face milling of aluminium alloy on a low power cutting machine. Based on the results of the central-composite plan of experiment with varying machining parameters (number of revolutions -spindle speed n, feed rate f and depth of cut a) and the roughness observed as output variable, two models have been developed: a regression model and a model based on the application of neural networks (NN model). The regression model (coefficient of determination of 0.965 or 0.952 adjusted) with insignificant lack of fit, provides a very good fit and can be used to predict roughness throughout the region of experimentation. Likewise, the model based on the application of neural networks approximates well the experimental results with the level of RMS (Root Mean Square) error in the phase of validation of 4.01 %.

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Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling

Cited by 81 publications

References 14 publications

Pre-evaluation on surface profile in turning process based on cutting parameters

Pre-evaluation on surface profile in turning process based on cutting parameters

Experimental Investigation and Optimization of Flatness and Surface Roughness using Grey Relational Analysis for WCB Material during Face Milling Operation

Modelling and Simulation of Surface Roughness in Face Milling

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