Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant

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@Article{HEGAB:2021:ASC,

• author = "H. Hegab and A. Salem and S. Rahnamayan and H. A. Kishawy",
• title = "Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant",
• journal = "Applied Soft Computing",
• volume = "108",
• pages = "107416",
• year = "2021",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2021.107416",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494621003392",
• keywords = "genetic algorithms, genetic programming, Inconel 718, Minimum quantity lubrication, Nano-additives, Tool wear, Surface roughness, Energy consumption, Modeling and multi-objective optimization",
• abstract = "In the current study, analysis, modeling, and optimization of machining with nano-additives based minimum quantity lubrication (MQL) during turning Inconel 718 are presented and discussed. Multi-walled carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3) gamma nanoparticles were used as used nano-additives. The studied design variables include cutting speed, feed rate, and nano-additives percentage (wt. percent). Three machining outputs were considered namely: flank wear, surface roughness, and energy consumption. The novelty here focuses on improving the MQL heat capacity by employing two different nano-fluids. The analysis of variance (ANOVA) technique was employed to investigate the influence of the design variables on the studied machining outputs. The results demonstrated that the usage of MQL-nanofluids improved the cutting process performance compared to the classical approach of MQL. It was found that 4 wt. percent of added MWCNTs decreased the flank wear by 45.6percent compared to the pure MQL. Similarly, it was found that 4 wt. percent of added Al2O3 nanoparticles improved the tool wear by 37.2percent. Besides, the nanotubes additives showed more improvements than Al2O3 nanoparticles in terms of tool wear, surface quality, and energy consumption. Regarding the modeling stage, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) are employed to model the measured outputs in terms of the studied parameters. These soft computing approaches provide various advantages through their self-learning capabilities, fuzzy principles, and evolutionary computational concept. In addition, a comparison among the developed models has been conducted to select the most accurate approach to present the machining characteristics. Finally, the non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the studied cutting processes. Moreover, a comparison between the optimized results from different approaches is presented. The proposed methodology presented in this work can be further implemented in other machining cases to model, analyze as well as optimize the machining performance, especially for the hard-to-cut materials which are commonly used in different industries",

}

Genetic Programming entries for Hussien Hegab A Salem Shahryar Rahnamayan Hossam A Kishawy

Citations