Taguchi Optimization of Multiple Performance Characteristics in the Electrical Discharge Machining of the Tigr2

Abstract

Electrical discharge machining (EDM) provides many advantages for the shaping of metallic materials. It also provides better surface quality for Ti alloys used in the defense industry. In this study, experiments were carried out with different EDM parameters using the Titanium (Gr2) alloy. A number of novel industrial processes have been developed as a result of advances in technology. For a product to be developed, these novel approaches must be utilized to determine optimum parameters. The Taguchi method was applied in the experiments with EDM. The impact the test parameters had on the performance characteristics of tool wear rate, material removal rate, depth, and surface roughness were analyzed by the variance analysis (ANOVA). Quadratic regression analyses were carried out to reveal the correlation between the experimental results and the predicted values. According to the ANOVA results for material removal rate (MRR), tool wear rate (TWR), depth, and surface roughness, the most effective factor was amperage, at 99.66%, 99.56%, 87.95%, and 81.12%, respectively. The best value for average surface roughness was determined to be 3.29 µm obtained at 120 μs time-on, 8 A, and 40 μs time-off.

A grey-fuzzy based multi-response optimisation study on the friction and wear characteristics of titanium diboride reinforced aluminium matrix composite

“…'smaller-the-better', 'nominal-the-better' and 'largerthe-better'. [41][42][43] Analysis of variance (ANOVA) is also performed for determining the statistically significance of each tribological parameter.…”

Experimental Research on Surface Quality of Titanium Rod Turned by Wire Electrical Discharge Turning Process

“…They found a 3A discharge current, 8µs spark duration, and 6 µs spark-off duration as the optimum turning parameters, by a GRA analysis, to simultaneously maximize the material removal rate (MRR) and minimize the surface roughness (R a ) and tool wear rate (TWR). The effect of the electrical discharge machining parameters on the tool wear rate, material removal rate, and surface roughness was analyzed using an analysis of variance [20]. A surface roughness of 3.29 µm was obtained at a machining combination of a 120 µs spark duration, an 8 A current, and a 40 µs spark-off duration.…”

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining

“…Experimental results validated that GRA effectively optimized the overall situation, with GA fine-tuning individual process parameters while considering the impact on other response variables. Akıncıoğlu [ 31 ] explored the machining of titanium alloy (Gr2) using EDM, investigating parameters such as EWR, depth, SR, and MRR under various process settings. Notably, amperage emerged as the most critical factor affecting the response measurements, with an amperage of 8A producing the most favourable average SR outcomes.…”