Artificial neural network modelling and multi objective optimisation of hole drilling electro discharge micro machining of invar. (1st January 2012)

• Record Type: Journal Article
• Authors: Porwal, Rajesh Kumar; Yadava, Vinod; Ramkumar, J.
• Is Part Of: International journal of mechatronics and manufacturing systems. Volume 5:Number 5/6(2012)
• Page Start: 470
• Page End: 494
• Publication Date: 2012-01-01
• Subjects: hole drilling electro discharge micro machining -- HD-EDMM -- modelling -- optimisation -- Taguchi methodology -- ANN -- GRA -- PCA; Mechatronics -- Periodicals; 629.89
• Languages: English
• ISSNs: 1753-1039

Abstract

Hole drilling electro discharge micro machining (HD-EDMM) is one of the potential method for creation of micro-holes in difficult to machine electrically conductive workpiece materials. Maintaining quality and accuracy of the drilled micro-holes along with better performance characteristics have always been a challenge for the researchers and manufacturers. Keeping cost and time of manufacturing into consideration, modelling and optimisation of EDMM is required. In this paper, attempts have been made to model the HD-EDMM process using feed forward back propagation neural network (BPNN) and further combined with GRA-based PCA for its optimisation. The developed ANN model and finally optimised results are validated with our own experimentally obtained results. The approach used in the present paper would be extendable to other configuration of EDMM such as milling-EDMM, wire-EDMM and grinding-EDMM.