The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling

  • Ali Yeganefar
  • Seyed Ali NiknamEmail author
  • Reza Asadi


In the present study, prediction and optimization of the surface roughness and cutting forces in slot milling of aluminum alloy 7075-T6 were pursued by taking advantage of regression analysis, support vector regression (SVR), artificial neural network (ANN), and multi-objective genetic algorithm. The effects of process parameters, including cutting speed, feed per tooth, depth of cut, and tool type, on the responses were investigated by the analysis of variance (ANOVA). Grid search and cross-validation methods were used for hyperparameter tuning and to find the best ANN and SVR models. The training algorithm of developed NNs was one of the hyperparameters which was chosen from Levenberg-Marquardt and RMSprop algorithms. The performance of regression, SVR, and ANN models were compared with each other corresponding to each machining response studied. The ANN models were integrated with the non-dominated sorting genetic algorithm (NSGA-II) to find the optimum solutions by means of minimizing the surface roughness and cutting forces. In addition, the desirability function approach was utilized to select proper solutions from the statistical tools.


ANN SVR Regression analysis Dummy variable NSGA-II Cutting forces Surface roughness Milling 



Artificial neural network


Support vector machine


Support vector regression


Genetic algorithm


Non-dominated sorting genetic algorithm


Analysis of variance


Aluminum alloy




Multi-objective evolutionary algorithm




Mean squared error


Mean absolute percentage error


Mean absolute error


Correlation coefficient


Levenberg-Marquardt algorithm


Radial basis function


Infeed force (N)


Cross feed force (N)


Thrust force (N)


Surface roughness (μm)


Cutting speed (m/min)


Feed per tooth (mm/tooth)


Depth of cut (mm)


Tool type

Tool.1 & Tool.2

Dummy variables


Epsilon, predefined parameter in SVR


Regularization parameter in SVR

γ , r

Kernel hyperparameters in SVR

μ , μ update factor

LMA hyperparameter


Global learning rate in RMSprop algorithm


Funding information

This study was financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de Recherche du Québec – Nature et technologies (FRQNT).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringIran University of Science and TechnologyTehranIran

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