Artificial Intelligence Modeling for Prediction of Electrode Wear Rate of Ti-5Al-2.5Sn through Electrical Discharge Machining
This paper is presented Artificial intelligence modeling for prediction of electrode wear rate of Ti-5AI-2.5Sn material in Electrical Discharge Machining (EDM). The electrical discharge machining is carried out employing the positive polarity and copper as an electrode. Investigation has been focused using five levels of each parameter as peak current, pulse on time, pulse off time and servo voltage to correlate these parameters with EDM characteristics as electrode wear rate. The parameter combination is worked out using central composite design of experiment methods. The developed model is validated with the experimental results, which was not utilized for developing the model. It is observed that the developed model is within the limits of the agreeable error when experimental and network model results are compared. Sensitivity analysis is carried out to investigate the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures. The reported results indicate that the proposed AI models can satisfactorily evaluate the electrode wear rate in EDM. Moreover, it can be considered as valuable tools for the process planning for EDM.
EDM Artificial neural network Ti-5Al-2.5Sn Central composite design Electrode wear rate
M. M. Rahman Md. Ashikur Rahman Khan K. Kadirgama Rosli A. Bakar
Faculty of Mechanical Engineering Universiti Malaysia Pahang Kuantan, Malaysia
国际会议
成都
英文
975-979
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）