Surface roughness modeling using response surface methodology and a variant of multiquadric radial basis function

Sánchez-López, Orquídea; Hernández-Castillo, Ignacio; Castañeda-Roldan, C. H.; Santiago-Alvarado, A.; Cruz-Félix, Angel-Sinue

doi:10.1007/s00170-020-06035-w

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Therefore, Eq.12 can be transformed into the regression between the measured value of surface roughness and the subsection theoretical model, it is shown in Eq. (20).…”

Section: Prediction Results and Parameters Influence Analysis Of The ...mentioning

confidence: 99%

“…The predictive accuracy of data-driven model is generally better than that of the theoretical model. The data-driven modeling method for surface roughness prediction mainly includes multiple regression modeling method [17,18], response surface methodology (RSM) [19][20][21] and intelligence algorithm modeling method (such as neural network [22][23][24][25] and support vector machine (SVM) [26][27][28]). However, since the data-driven model is based on data, and the more data is, the higher the accuracy of the model is, so it needs to spend the large experimental cost.…”

Section: Introductionmentioning

confidence: 99%

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Surface roughness prediction for turning based on the corrected subsection theoretical model

Lü

Wang

Chen

et al. 2022

Int J Adv Manuf Technol

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In order to obtain the accurate prediction model of surface roughness in precision machining processes, this paper researches the subsection theoretical model for surface roughness in turning built by our research group, which considers feed rate, nose radius and tool minor cutting edge angle, and proposes two error correction models of the subsection theoretical model for improving theoretical model accuracy. The prediction performance of two error correction models is evaluated by 25 groups of turning data. The experimental results show that two error correction models have excellent prediction performance and significantly improve the prediction accuracy and stability of the subsection theoretical model. Moreover, the influence of turning parameters and tool geometry on surface roughness based on the three models, and the advantages and disadvantages of three models in prediction performance and test cost are analyzed, which provide an effective guidance for selection of parameters and the prediction model of surface roughness in the actual turning.

show abstract

“…Therefore, Eq.12 can be transformed into the regression between the measured value of surface roughness and the subsection theoretical model, it is shown in Eq. (20).…”

Section: Prediction Results and Parameters Influence Analysis Of The ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surface roughness prediction for turning based on the corrected subsection theoretical model

Lü

Wang

Chen

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

Study on surface morphology and residual stress in inclined milling of titanium alloy TC11

Song

Chen

Tang

et al. 2022

Int J Adv Manuf Technol

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Predictive modelling of residual stress in turning of hard materials using radial basis function network enhanced with principal component analysis

Farias,

Paschoalinoto,

Bordinassi

et al. 2024

Engineering Science and Technology, an International Journal

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Surface roughness modeling using response surface methodology and a variant of multiquadric radial basis function

Cited by 6 publications

References 37 publications

Surface roughness prediction for turning based on the corrected subsection theoretical model

Surface roughness prediction for turning based on the corrected subsection theoretical model

Study on surface morphology and residual stress in inclined milling of titanium alloy TC11

Predictive modelling of residual stress in turning of hard materials using radial basis function network enhanced with principal component analysis

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