Title: Optimisation of spark erosion machining process parameters using hybrid grey relational analysis and artificial neural network model
Authors: N. Manikandan; Ramesh Raju; D. Palanisamy; J.S. Binoj
Addresses: Micro Machining Research Centre, Department of Mechanical Engineering, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, Andhra Pradesh, India ' Department of Mechanical Engineering, Santhiram Engineering College, Nandyal, Kurnool, Andhra Pradesh, India ' Dr. Abdul Kalam Research Centre, Department of Mechanical Engineering, Adhi College of Engineering and Technology, Chennai, Tamil Nadu 631605, India ' Micro Machining Research Centre, Department of Mechanical Engineering, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, Andhra Pradesh, India
Abstract: Hastealloy C276 is hard to machine superalloy and extensively used in various engineering applications. It possess good strength and lower thermal conductivity which results in decreased tool life and poor machinability by conventional machining. Advanced machining processes have developed to overcome these difficulties and claimed as an alternative methods. Electrical Discharge Machining (EDM) is one of the advanced method used for machining of hard materials. This article details an investigation on EDM process and development of hybrid Grey ANN model. Taguchi method and ANOVA are used for designing the experiments and statistical analysis respectively. Grey Relational Analysis is adopted for determining the Grey Relational Grade (GRG) to represent the multi aspect optimization model and a neural network has been evolved to predict GRG by feeding the Grey Relational Co-efficient (GRC) values as input to developed neural network model. A comparison has been done between the experimental values and predicted values.
Keywords: electrical discharge machining; EDM; hard materials; haste alloy; Taguchi's methodology; form and orientation tolerances; grey relational analysis; GRA; artificial neural network; ANN.
International Journal of Machining and Machinability of Materials, 2020 Vol.22 No.1, pp.1 - 23
Received: 14 Jun 2018
Accepted: 03 Nov 2018
Published online: 05 Dec 2019 *