Modelling Approach for the Prediction of Machinability in Al6061 Composites by Electrical Discharge Machining

Applied Sciences

Kinga Korniejenko¹  Hariharan Sree Ram²  Sundaresan Thirumalai Kumaran²  Shanmugam Suresh Kumar²  Marimuthu Uthayakumar² 

[1] Faculty of Materials Engineering and Physics, Cracow University of Technology, al. Jana Pawła II 37, 31-864 Kraków, Poland;Faculty of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, India;

关键词: electric discharge machining (EDM);    composite property;    Al6061 composite;    mathematical modelling;    material removal rate (MRR);    surface roughness (R_a);   

DOI  :  10.3390/app12052673

来源: DOAJ

【 摘 要 】

This work aims to identify the pattern for the major output parameters, material removal rate (MRR) and surface roughness (R_a) of different combinations of Al6061-based composites. Based on the verification carried out on these patterns using analysis of variance (ANOVA) as the mathematical tool, the work predicts the mentioned output characteristics while machining Al6061 composites of different material compositions based on their hardness values. ANOVA was employed for the generation of equations of the particular composite. The equations were compared for the coefficients of each parameter employed in ANOVA. The work was carried out comparing the characteristic equation of different combinations of Al6061-based composite. The results indicate that the coefficients of the current show a drastic variation when compared to other coefficients for both the output parameters. It was observed that the current and its coefficients contribute to the output parameters based on the variation in hardness. For surface roughness, the constant of the characteristic equation was also found to influence the parameter for the change in hardness. The equation derived for both material removal rate (MRR) and surface roughness (R_a) were identified to be matching with the experimental result carried out for validation. The average variation observed was 9.3% for MRR and 7.2% for surface roughness.

【 授权许可】

Unknown