Modeling and analysis of MRR, EWR and surface roughness in EDM milling through response surface methodology

• Main Authors: Asif Iqbal, A.K.M., Khan, Ahsan Ali
• Format: Article
• Language: English
• Published: Science Publications 2010
• Subjects: TS200 Metal manufactures. Metalworking
• Online Access: http://irep.iium.edu.my/1881/1/Asif_2.pdf; http://irep.iium.edu.my/1881/; http://thescipub.com/abstract/10.3844/ajeassp.2010.611.619
• Institution: Universiti Islam Antarabangsa Malaysia

Electrical Discharge Machining (EDM) has grown over the last few decades from a novelty to a mainstream anufacturing process. Though, EDM process is very demanding but the mechanism of the process is complex and far from completely understood. It is difficult to establish a model that can accurately predict the performance by correlating the process parameters. The optimum processing parameters are essential to increase the production rate and decrease the machining time, since the materials, which are processed by EDM and even the process is very costly. This research establishes empirical relations regarding machining parameters and the responses in analyzing the machinability of the stainless steel. Approach: The machining factors used are voltage, rotational speed of electrode and feed rate over the responses MRR, EWR and Ra. Response surface methodology was used to investigate the relationships and parametric interactions between the three controllable variables on the MRR, EWR and Ra. Central composite experimental design was used to estimate the model coefficients of the three factors. The responses were modeled using a response surface model based on experimental results. The significant coefficients were obtained by performing Analysis Of Variance (ANOVA) at 95% level of significance. Results: The variation in percentage errors for developed models was found within 5%. Conclusion: The developed models show that voltage and rotary motion of electrode are the most significant machining parameters influencing MRR, EWR and Ra. These models can be used to get the desired responses within the experimental range.