Artificial Neural Network Model to Predict Process Performance in Ultrasonic Drilling of GFRP

Authors

  • A. B. Pandey Department of Mechanical Engineering, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India
  • B. V. Kavad Dr. J. N. Mehta Government Polytechnic, Amreli
  • R. S. Agarwal Department of Mechanical Engineering, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India

Keywords:

Ultrasonic Machining, GFRP, ANN

Abstract

The prediction of process performance is essential to select the control parameters for obtaining the goals of
production. Ultrasonic machining is popular material removal process brittle materials like glass, ceramics etc. Glass
Fiber Reinforced Plastic (GFRP) is a widely used engineering material in number of engineering applications.
Experiments are conducted to obtain data regarding the effect of process parameters on ultrasonic drilling of GFRP.
Amplitude, pressure and thickness of the glass sheet are chosen as control parameters. Three levels of each of these
parameters are selected giving 33 = 27 trials. Material removal rate (MRR), overcut (OC), taper produced on the drilled
holes, delamination on top and bottom surfaces are determined as response parameters. Artificial Neural Network
(ANN) model is developed to capture relationship between control and response parameters as a predictive tool to
predict the performance of the process.

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Published

2015-12-25

How to Cite

A. B. Pandey, B. V. Kavad, & R. S. Agarwal. (2015). Artificial Neural Network Model to Predict Process Performance in Ultrasonic Drilling of GFRP. International Journal of Advance Engineering and Research Development (IJAERD), 2(12), 78–83. Retrieved from https://www.ijaerd.org/index.php/IJAERD/article/view/5266

Issue

Vol. 2 No. 12 (2015): Volume 2 Issue 12, December 2015

Section

Articles