Optimization of Processing Parameters in ECM of Die Tool Steel Using Nanofluid by Multiobjective Genetic Algorithm

Abstract

Formation of spikes prevents achievement of the better material removal rate (MRR) and surface finish while using plain NaNO3 aqueous electrolyte in electrochemical machining (ECM) of die tool steel. Hence this research work attempts to minimize the formation of spikes in the selected workpiece of high carbon high chromium die tool steel using copper nanoparticles suspended in NaNO3 aqueous electrolyte, that is, nanofluid. The selected influencing parameters are applied voltage and electrolyte discharge rate with three levels and tool feed rate with four levels. Thirty-six experiments were designed using Design Expert 7.0 software and optimization was done using multiobjective genetic algorithm (MOGA). This tool identified the best possible combination for achieving the better MRR and surface roughness. The results reveal that voltage of 18 V, tool feed rate of 0.54 mm/min, and nanofluid discharge rate of 12 lit/min would be the optimum values in ECM of HCHCr die tool steel. For checking the optimality obtained from the MOGA in MATLAB software, the maximum MRR of 375.78277 mm3/min and respective surface roughness Ra of 2.339779 μm were predicted at applied voltage of 17.688986 V, tool feed rate of 0.5399705 mm/min, and nanofluid discharge rate of 11.998816 lit/min. Confirmatory tests showed that the actual performance at the optimum conditions was 361.214 mm3/min and 2.41 μm; the deviation from the predicted performance is less than 4% which proves the composite desirability of the developed models.

Analysis of technological indicators in electrochemical machining (ECM) of details with deep holes

“…Dimensional electrochemical machining (DECM) [3,17,18,19] It is used mainly in the formation of details in difficult-toprocess materials with expressed high requirements for the accuracy of the shape and the mutual position of the surfaces, such as:  Molding of deep holes with special configuration (snow sockets, weapons barrels) [3,17,18,19] .  Creation of working spaces in tools for injectable molding of polymers and metals [20,21,22] .…”

Prediction of surface quality and optimization of process parameters in drilling of Delrin using neural network

“…Many researches have also been conducted in the past using above methodologies in relation to metals, metal matrix composites, and nanocomposites. 10 -24 Elango et al 25 attempted to find the optimum control parameters for turning Delrin through TLBO algorithm. But the drilling characteristics of Delrin have not yet been addressed, which look to be a high requirement for industrial applications.…”

Genetic algorithm parameters optimization for electrochemical machining using response surface methodology

“…The optimum results given by the experiment are as follows 8V voltage and 0.3mm/min feed rate and 217A current are most appropriate and momentous to achieve utmost surface finish with NaNO3 electrolyte solution. V. Sathiyamoorthy et al (2015) attempted to diminish the formation of spikes in the work piece of high carbon and high chromium die tool steel using nano particles of copper floating in plain NaNO3 electrolyte. They used Design expert 7.0 software to developed the mathematical models for getting the response like MRR and SR and after that multi-objective genetic algorithm in MATLAB was used for the best probable combination for accomplishing the maximum MRR and minimum SR. After comparing the result from design expert 7.0 to the optimum results obtained by using multi-objective genetic algorithm (MOGA) the conclusion come that the distinction from the predicted performance is less than 4% which verify the composite popularity of the developed models.…”