Mathematical modeling and multi-criteria optimization of rotary electrical discharge machining process

International Conference on Vibration Problems

Balraj, U. Shrinivas^1

Department of Mechanical Engineering, Kakatiya Institute of Technology and Science, Warangal, India^1

关键词: Correlation coefficient;    Electrical discharge machining;    Electrical discharge machining process;    Grey relational grade;    Grey-based Taguchi method;    Material removal rate;    Multicriteria optimization;    Performance characteristics;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/662/1/012023/pdf
DOI  :  10.1088/1742-6596/662/1/012023

来源: IOP

【 摘 要 】

In this paper, mathematical modeling of three performance characteristics namely material removal rate, surface roughness and electrode wear rate in rotary electrical discharge machining RENE80 nickel super alloy is done using regression approach. The parameters considered are peak current, pulse on time, pulse off time and electrode rotational speed. The regression approach is very much effective in mathematical modeling when the performance characteristic is influenced by many variables. The modeling of these characteristics is helpful in predicting the performance under a given set of combination of input process parameters. The adequacy of developed models is tested by correlation coefficient and Analysis of Variance. It is observed that the developed models are adequate in establishing the relationship between input parameters and performance characteristics. Further, multi-criteria optimization of process parameter levels is carried using grey based Taguchi method. The experiments are planned based on Taguchi's L9 orthogonal array. The proposed method employs single grey relational grade as a performance index to obtain optimum levels of parameters. It is found that peak current and electrode rotational speed are influential on these characteristics. Confirmation experiments are conducted to validate optimal parameters and it reveals the improvements in material removal rate, surface roughness and electrode wear rate as 13.84%, 12.91% and 19.42% respectively.