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Salonica Sravani, P.
- Multi Response Optimization of Proces Parameters in Electrical Discharge Machining Using TOPSIS
Authors
1 Dept of Mechanical Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, IN
Source
Manufacturing Technology Today, Vol 15, No 8 (2016), Pagination: 3-9Abstract
Electrical Discharge Machining (EDM) is identified for machining difficult to machine materials. This paper proposes an approach to study the influence of electrical discharge machining process parameters on Taper, Radial over cut (ROC), Material removal rate (MRR) in machining of the AA 6061-TiB2 in-situ metal matrix composite fabricated by flux assisted synthesis. Pulse on time, pulse off time and current are considered as process parameters. AA 6061-TiB2 composite was machined by EDM process using a copper electrode and kerosene is used as a dielectric fluid. Multi criteria decision making technique named Technique Ordered Preference by Similarity to the Ideal Solution (TOPSIS) was used to find out the optimum process parameters. The optimum levels of machining parameters are found to be pulse on time 90μs, pulse off time 3μs and discharge current 30 Amps.Keywords
In-Situ Composite, EDM, Taper, Radial Over Cut, Material Removal Rate, TOPSIS.
- Study on Multi Responses in Turning of EN19 Steel Using Fuzzy Taguchi Method
Authors
1 Department of Mechanical Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, IN
Source
Manufacturing Technology Today, Vol 15, No 4 (2016), Pagination: 31-38Abstract
In this work Fuzzy-Taguchi method has been used to identify the optimal combination of influential factors in turning of EN 19 steel. Experiments are conducted according to Taguchi orthogonal array considering various machining parameters: depth of cut, feed rate, spindle speed. The experimental responses: Surface Roughness and Material Removal Rate were recorded for each experimental run.
Fuzzy logic is applied for the analysis of experimental responses data of Surface Roughness and Material Removal Rate. The Fuzzy grade is calculated from this data and Fuzzy grade is optimized using Taguchi method in order to get the optimal parameter values. The confirmation test were conducted for the optimum levels.