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Neural Computing and Applications

, Volume 31, Issue 12, pp 8693–8717 | Cite as

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

  • Binayak Sen
  • Mozammel MiaEmail author
  • Uttam Kumar Mandal
  • Bapi Dutta
  • Sankar Prasad Mondal
Original Article

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.

Keywords

Inconel 690 MQL End milling GEP NSGA-II TOPSIS 

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

List of symbols

vc

Cutting speed

f

Feed rate

ap

Depth of cut

Q

MQL flow rate

Θ

Nozzle inclination angle

Ra

Average surface roughness

Fr

Resultant cutting force

T

Cutting temperature

VB

Tool wear

R2

Coefficient of determination

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

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

Authors and Affiliations

  1. 1.Department of Production EngineeringNational Institute of TechnologyAgartalaIndia
  2. 2.Department of Mechanical and Production EngineeringAhsanullah University of Science and TechnologyDhakaBangladesh
  3. 3.The Logistic Institute-Asia PacificNational University of SingaporeSingaporeSingapore
  4. 4.Department of Applied ScienceMaulana Abul Kalam Azad University of Technology, West BengalHaringhataIndia

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