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Optimization of Cutting Parameters and Result Predictions with Response Surface Methodology, Individual and Ensemble Machine Learning Algorithms in End Milling of AISI 321

  • Research Article-Mechanical Engineering
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Abstract

Optimizing the parameters in the milling method is important in terms of cost, energy, and time. The forces that arise during milling cause undesirable results, such as tool wear and energy loss. In this study, cutting parameters were optimized during the milling of AISI 321 material. Cutting speed (60, 70, 80 m/min), feed per tooth (0.04, 0.05, 0.06 mm/tooth), and depth of cut (0.25, 0.5, 0.75 mm) were selected as input parameters. Cutting force in the X and Y axes and the surface roughness were selected as the output parameters. Optimum parameters (60.80 m/min for cutting speed, 0.04 mm/tooth for feed per tooth, and 0.25 mm for depth of cut) were found using response surface methodology. The effect of cutting parameters was calculated by analysis of variance. The most influential parameters were found, depth of cut as 87.49% for cutting force on the X-axis, 86.48% on the Y-axis, and for surface roughness, the cutting speed with 36.48%. Prediction models are compared to choose the best model. Individual (Neural network, decision tree, and k-nearest neighbor algorithms) and ensemble methods (vote) from machine learning and response surface methodology from statistical methods were used for models. The error rates of the models were compared according to the mean absolute percentage error performance criterion. The lowest MAPE values were obtained with the vote method 11.163% in the X-axis force, the artificial neural network algorithm with 7.749% in the Y-axis force, and RSM with 0.93% in the surface roughness.

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Abbreviations

RSM:

Response surface methodology

ANOVA:

Analysis of variance

MAPE:

Mean absolute percentage error

ANN:

Artificial neural network

SVR:

Support vector regression

DT:

Decision tree

K-NN:

K-nearest neighbor

F x :

X-axis force (N)

F y :

Y-axis force (N)

Ra:

Surface roughness (µm)

Vc:

Cutting speed (m/min)

fz:

Feed per tooth (mm/tooth)

Ap:

Depth of cut (mm)

HB:

Brinell hardness

MLP:

Multilayer perceptron neural network

DF:

Degree of freedom

Adj SS:

Adjusted sum of squares

Adj MS:

Adjusted mean squares

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Authors and Affiliations

  1. Industrial Engineering Department, Faculty of Engineering, Sakarya University, Sakarya, Turkey

    Deniz Demircioglu Diren

  2. Mechanical Engineering Department, Faculty of Engineering, Sakarya University, Sakarya, Turkey

    Neslihan Ozsoy, Murat Ozsoy & Huseyin Pehlivan

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  1. Deniz Demircioglu Diren
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Demircioglu Diren, D., Ozsoy, N., Ozsoy, M. et al. Optimization of Cutting Parameters and Result Predictions with Response Surface Methodology, Individual and Ensemble Machine Learning Algorithms in End Milling of AISI 321. Arab J Sci Eng 48, 12075–12089 (2023). https://doi.org/10.1007/s13369-023-07642-x

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