The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of SYSTEMS ENGINEERING, APPLICATIONS AND DEVELOPMENT
Transactions ID Number: 20-709
Full Name: Md. Mustafizur Rahman
Position: Associate Professor
Age: ON
Sex: Male
Address: Automotive Engineering Centre, Universiti Malaysia Pahang, Tun Razak Highway, 26300 Gambang, Kuantan, Pahang, Malaysia
Country: MALAYSIA
Tel: +6094242246
Tel prefix:
Fax: +6094242202
E-mail address: mustafizur@ump.edu.my
Other E-mails: mustafiz12@gmail.com
Title of the Paper: Neural Network Approach for Evaluation of Material Removal Rate for Ti-5Al-2.5Sn in Electrical Discharge Machining
Authors as they appear in the Paper: M.M. Rahman, Md. Ashikur Rahman Khan, K. Kadirgama, M.A. Maleque and Rosli A. Bakar
Email addresses of all the authors: mustafizur@ump.edu.my, ashik.nstu@yahoo.com, kumaran@ump.edu.my, maleque@iiu.edu.my, rosli@ump.edu.my
Number of paper pages: 8
Abstract: Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5!
Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance
Keywords: Ti-5Al-2.5Sn, material removal rate, copper tungsten, positive polarity, artificial neural network, multi-layer perceptron
EXTENSION of the file: .doc
Special (Invited) Session: Prediction of Material Removal Rate for Ti-5Al-2.5Sn in EDM using Multi-Layered Perceptron Neural Network Technique
Organizer of the Session: 650-133
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