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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2019

27.08.2019 | Original Article

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

Erschienen in: Neural Computing and Applications | Ausgabe 12/2019

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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.
Vorheriger Artikel A random-weighted plane-Gaussian artificial neural network
Nächster Artikel Delayed impulsive control for exponential synchronization of stochastic reaction–diffusion neural networks with time-varying delays using general integral inequalities

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Metadaten
Titel
Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS
Publikationsdatum
27.08.2019
Erschienen in
Neural Computing and Applications / Ausgabe 12/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04450-z

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