Analyzing the influence of electrical parameters on EDM process of Ti6Al4V alloy using Adaptive Neuro-fuzzy Inference System (ANFIS)

• Main Authors: Al Hazza, Muataz Hazza Faizi, Mohammed, Baba Ndaliman, Mohammad, Yeakub Ali, Khan, Ahsan Ali
• Format: Article
• Language: English; English
• Published: Praise Worthy Prize 2015
• Subjects: TS200 Metal manufactures. Metalworking
• Online Access: http://irep.iium.edu.my/43939/1/Baba_2015-1.pdf; http://irep.iium.edu.my/43939/4/43939_Analyzing%20the%20influence%20of%20electrical%20parameters%20on%20EDM%20process%20of%20Ti6Al4V_SCOPUS.pdf; http://irep.iium.edu.my/43939/; http://www.praiseworthyprize.org/jsm/index.php?journal=ireme&page=article&op=view&path%5B%5D=17179

Electrical discharge machining (EDM) is machining process that is suitable for machining very hard materials that are electrically conductive. In this process the material is removed by series of repeated electrical discharges, produced by electric pulse generators at short intervals in dielectric fluid medium. Thus, the electrical parameters are the main process parameters. However, the complexity of this cutting process will not permit pure analytical physical investigating. Therefore, the conventional mathematical models may not be suitable for analysing the output responses. The aim of this research is to investigate and predict the influence of the electrical parameters: peak current (PC), pulse duration (PD) and duty factor (DF) on the surface roughness (SR), Material Removal Rate (MRR) and Tool Wear Rate (TWR) using Adaptive Neuro-Fuzzy Inference System (ANFIS) as one of the effective soft computing methods. In this research, a set of experimental data was obtained with different levels. The measured values have been used to train the ANFIS system to find minimum error. The results indicate that even with the complexity of the EDM process, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was found to be adequate in predicting the SR, MRR and TWR with high accuracy.