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Experimental Study on Micro Electrochemical Machining of SS 316L using Teaching Learning based Optimization

Journal: International Journal of Mechanical and Production Engineering Research and Development (IJMPERD ) (Vol.10, No. 3)

Publication Date:

Authors : ;

Page : 1051-1060

Keywords : Metal Removal Rate (MMR); Analysis of Variance (ANOVA); Response Surface Methodology (RSM) & Teaching Learning-Based Optimization (TLBO);

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Electrochemical machining is a radical machine, which is widely used for machining various intrinsic shapes in alloys; optimal usage of ECM process parameters can lessen machine operation such as tooling, and maintenance cost. Attempts are made to investigate the optimum values of Metal Removal Rate in micro-Electro chemical machining (micro-ECM) on Stainless Steel alloy voltage, Electrolyte concentration, Duty cycle and Temperature that are the input parameters for the experiment, while output responses are metal removal rate. Taguchi approach is used to evaluate the best possible criteria of the method and the number of experiments necessary to design the answers. The approach of the surface response (RSM) is related to maximize the material removal rate (MRR) in the machining of SS316L. The mathematical model from RSM is used to exercise function for single-objective optimization [7], using teachinglearning-based optimization. Results demonstrate that the best developed mathematical model varies less than 5% of the experiment.

Last modified: 2021-02-06 20:06:34