Back propagation neural network based modeling of multi-responses of an electrical discharge machining process

Article type: Research Article

Authors: Mandal, Debabrata | Pal, Surjya K.^{; *} | Saha, Partha

Affiliations: Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur-721302, India

Correspondence: [*] Corresponding author. Tel.: +91 3222 282996; Fax: +91 3222 255303; E-mail: [email protected]

Abstract: This work attempts to model the electrical discharge machining (EDM) process using artificial neural network (ANN) with back propagation as the learning algorithm. The three most important parameters, namely, material removal rate (MRR), tool wear and surface roughness have been considered as a measure of the process performance. A large number of experiments have been carried out over a wide range of machining conditions to study the effect of input parameters on the machining performance. The experimental data is used for the training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters accurately.

Keywords: Electrical discharge machining (EDM), back propagation neural network (BPNN), process modeling, surface roughness

DOI: 10.3233/KES-2007-11603

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 11, no. 6, pp. 381-390, 2007