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Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition

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Abstract

An effective method of fluid application such as high-pressure coolant (HPC) augments the performance characteristics by producing quality products. Effective control of parameters, prior to actual machining, prevents the loss of resources which in turn maximize the productivity. Thus, an adequate prediction model of surface roughness and an optimization model of control parameters must be determined that can be efficiently used for HPC employed machining. In this regard, this article presents the formulation of two predictive models of surface roughness, one by using artificial intelligence-based technique, namely support vector regression (SVR), and another by applying conventional technique called response surface methodology, in turning of hardened and tempered AISI 1060 steel in dry cutting and under the application of pressurized oil jet. The cutting speed, feed rate and material hardness were considered as input variables for model formulation, and based on these factors, the full factorial experimental design plan was conducted. The performance of the predictive models was evaluated on the basis of root mean square error. Additionally, the effects of control factors were evaluated by using analysis of variance. Furthermore, separate optimization models were created using composite desirability function and genetic algorithm (GA) to determine the control factor setting corresponding to minimal surface roughness. Both of the optimization models suggested an optimal parameter setting at 0.10 mm/rev feed rate, 161 m/min cutting speed and ~43 HRC material hardness. The adequacy of the optimization models was evaluated by a confirmation test. The predictive model by SVR and optimization model by GA provided the highest accuracy.

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Abbreviations

\(\alpha\) :

Lagrange multiplier

v c :

Cutting speed

f :

Feed rate

a p :

Depth of cut

H :

Material hardness

E :

Machining environment

R a :

Average surface roughness parameter

γ :

Regularization parameter

σ :

Kernel width parameter

b :

Bias term

w :

Weight vector

:R, R 2 :

Coefficient of determination

d :

Desirability

\(\Delta\) :

Deviation of actual and experimental result

AISI:

American Iron and Steel Institute

ANN:

Artificial neural network

ANOVA:

Analysis of variance

CBN:

Cubic boron nitride

CDF:

Composite desirability function

CGA:

Conventional genetic algorithm

DF:

Degree of freedom

DOE:

Design of experiment

ED:

External diameter

FFD:

Full factorial design

GA:

Genetic algorithm

HPC:

High-pressure coolant

HRC:

Rockwell hardness scale C

ID:

Internal diameter

IGA:

Improved genetic algorithm

KM:

Kernel method

LS:

Least square

MSE:

Mean square error

PC:

Percentage contribution

PCD:

Polycrystalline diamond

PSO:

Particle swarm optimization

RBF:

Radial basis function

RMSE:

Root mean square error

RSM:

Response surface methodology

SAE:

Society of automotive engineers

SVM:

Support vector machine

SVR:

Support vector regression

VG:

Viscosity grade

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Acknowledgements

The authors are grateful to Directorate of Advisory Extension and Research Services (DAERS), BUET, Bangladesh for providing research fund, Sanction No. DAERS/CASR/R-01/2013/DR-2103 (92) dated 23/08/2014, and the Department of Industrial and Production Engineering, BUET, Dhaka, Bangladesh, for allowing laboratory facility to carry out the research work.

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Authors and Affiliations

  1. Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh

    Mozammel Mia

  2. Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

    Nikhil Ranjan Dhar

Authors
  1. Mozammel Mia
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Correspondence to Mozammel Mia.

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Appendices

Appendix 1

See Table 13.

Table 13 Different parameters of LS-SVR during training and error findings of testing data on dry environment
Full size table

Appendix 2

See Table 14.

Table 14 Different parameters of LS-SVR during training and error findings of testing data on HPC environment
Full size table

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Mia, M., Dhar, N.R. Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Comput & Applic 31, 2349–2370 (2019). https://doi.org/10.1007/s00521-017-3192-4

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