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


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.


Inconel 690 MQL End milling GEP NSGA-II TOPSIS 



Minimum quantity lubrication


Computer numerical control


Gene expression programming


Non-dominated sorting genetic algorithm-II


Technique for order preference by similarity to ideal solution




Positive ideal solution


Negative ideal solution


Genetic algorithm


Artificial neural network


Response surface methodology


Adaptive network-based fuzzy inference system


Multi-criteria decision-making


Analytic hierarchy process


Analysis of variance


Degree of freedom


Sum of squares


Mean square


Expression trees


Random numerical constant


Root mean square error


Mean absolute percentage error

List of symbols


Cutting speed


Feed rate


Depth of cut


MQL flow rate


Nozzle inclination angle


Average surface roughness


Resultant cutting force


Cutting temperature


Tool wear


Coefficient of determination


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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