| Abstrakt: |
The present research examines the impact of Si3N4–BN hybrid reinforcement on the machinability of a magnesium hybrid composite in wire electrical discharge machine (WEDM). The composite is fabricated through inert gas assisted stir casting route with silicon nitride and boron nitride as reinforcement with varying weight percentages of 0%, 5%, and 10%. Subsequently, the fabricated composites were machined through WEDM according to Taguchi 27 orthogonal array with varying pulse on time (PON), pulse off time (POFF), wire feed rate (WFR), and wire tension (WT). The machinability of the composite was evaluated by measuring Surface Roughness (SR), Kerf Width (KW), and Cutting Velocity (CV) during WEDM. Results reveals that the % of Si3N4 has greater influence over kerf width and cutting velocity whereas BN % has higher influence over surface roughness. The optimization of process parameters using the Taguchi method resulted in different combinations of parameters for each output response. Therefore, Grey Relational Analysis was applied to determine the common optimal process parameters for all three considered output responses. The identified input parameters that yielded the higher CV, minimal SR and KW were as follows: 0% Si3N4 and BN, 6 µs PON, 14 µs POFF, 6 m/min WFR, and 10 g WT. Artificial Neural Network model has been developed to predict the output response CV, SR and KW. The 6–8–3 network model predicts the output responses with better accuracy with overall R2 value of 99.3%. [ABSTRACT FROM AUTHOR] |
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