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Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS

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Abstract

Inconel 690 is one of the most comprehensively used heat-resistive superalloys, exclusively applied in aerospace or aircraft engineering. Due to its implausible strength and rigidity, it possesses dull machinability. Hence, the machinability of Inconel alloys has turned out to be an extremely significant topic for study. Minimum quantity lubrication–vegetable oil synergy already made a reliable venture into the challenging facets of Inconel machining. However, for the effective controlling of end milling parameters, it is an imperative idea to imply Pareto-based hybrid multi-objective optimization strategy in machining domain. Thus, for the first time, a three-stage computational approach combining the theory of gene expression programming (GEP), non-dominated sorting genetic algorithm-II (NSGA-II) and technique for order preference by similarity to ideal solution model (TOPSIS) were utilized. Here, GEP-generated explicit equations are applied in NSGA-II to search the different solutions, and TOPSIS method is applied to choose the best compromise solution from non-dominated Pareto optimal solutions. Furthermore, a comparative study showed that the average error obtained between the experimental and predicted response is 3.13%, which determines the modesty of the proposed optimization model. So, the results of this study enlighten the possibility of adopting Pareto-based hybrid algorithms in the domains of the metal cutting operation.

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Abbreviations

MQL:

Minimum quantity lubrication

CNC:

Computer numerical control

GEP:

Gene expression programming

NSGA-II:

Non-dominated sorting genetic algorithm-II

TOPSIS:

Technique for order preference by similarity to ideal solution

NTOPSIS:

NSGA-II coupled TOPSIS

PIS:

Positive ideal solution

NIS:

Negative ideal solution

GA:

Genetic algorithm

ANN:

Artificial neural network

RSM:

Response surface methodology

ANFIS:

Adaptive network-based fuzzy inference system

MCDM:

Multi-criteria decision-making

AHP:

Analytic hierarchy process

ANOVA:

Analysis of variance

DF:

Degree of freedom

SS:

Sum of squares

MS:

Mean square

ETs:

Expression trees

RNC:

Random numerical constant

RMSE:

Root mean square error

MAPE:

Mean absolute percentage error

v c :

Cutting speed

f :

Feed rate

a p :

Depth of cut

Q :

MQL flow rate

Θ :

Nozzle inclination angle

R a :

Average surface roughness

F r :

Resultant cutting force

T :

Cutting temperature

V B :

Tool wear

R 2 :

Coefficient of determination

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Authors and Affiliations

  1. Department of Production Engineering, National Institute of Technology, Agartala, 799046, India

    Binayak Sen & Uttam Kumar Mandal

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

    Mozammel Mia

  3. The Logistic Institute-Asia Pacific, National University of Singapore, 21 Heng Mui Keng Terrace, Singapore, 119613, Singapore

    Bapi Dutta

  4. Department of Applied Science, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, 741249, West Bengal, India

    Sankar Prasad Mondal

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  1. Binayak Sen
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Correspondence to Mozammel Mia.

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Appendix

See Table 13.

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Sen, B., Mia, M., Mandal, U.K. et al. Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS. Neural Comput & Applic 31, 8693–8717 (2019). https://doi.org/10.1007/s00521-019-04450-z

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