An integrated approach for prediction of radial overcut in electro discharge machining using fuzzy graph recurrent neural network. (23rd September 2021)

• Record Type: Journal Article
• Authors: Jena, Amrut Ranjan; Das, Raja; Acharjya, D.P.
• Is Part Of: International journal of embedded systems. Volume 14:Number 4(2021)
• Page Start: 345
• Page End: 354
• Publication Date: 2021-09-23
• Subjects: recurrent neural network -- RNN -- fuzzy graph -- mean square error -- MSE -- radial overcut -- electro discharge machining -- EDM; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

Manufacturing of goods relies on the design methodology and the process parameters. The parameters used in manufacturing process play an important role to build a quality product. Initially heuristic techniques are used for parameter selection. Much research has been conducted to predict the radial overcut using neural networks. Besides, fuzzy neural network gains more popularity due to presence of fuzzyness in machining process. In this paper fuzzy graph recurrent neural network architecture is used for modelling and predicting the radial overcut in electro discharge machining. The proposed model is analysed over the information system obtained from VIT, Vellore, India. Moreover, it is also compared with fuzzy graph neural network and traditional neural network and found to be better in terms of accuracy.