Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique

doi:10.1007/s11465-013-0269-3

Front Mech Eng

0, Vol.

Issue () : 319-332 https://doi.org/10.1007/s11465-013-0269-3

RESEARCH ARTICLE

Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique

Ravindra Nath YADAV¹(

), Vinod YADAVA¹, G.K. SINGH^1,2

1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad-211004, India; 2. Department of Mechanical Engineering, Galgotias University, Gr. Noida, India

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Abstract

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R_a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.

Keywords hybrid machining processes (HMPs) electrical discharge diamond grinding (EDDG) artificial neural network (ANN) genetic algorithm modeling and optimization

Corresponding Author(s): YADAV Ravindra Nath,Email:rnymnnit@yahoo.com

Issue Date: 05 September 2013

Cite this article:

Ravindra Nath YADAV,Vinod YADAVA,G.K. SINGH. Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique[J]. Front Mech Eng, 0, (): 319-332.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-013-0269-3
https://academic.hep.com.cn/fme/EN/Y0/V/I/319

Fig.1 Different configurations of EDDG process

Fig.2 EDDFG as combination of EDFG and DFG

Fig.3 Photographic view of EDDFG setup

Fig.4 Schematic diagram of EDDFG process

Tab.1 Detail of metal bonded grinding wheel

Tab.2 Input parameters and their values

Tab.3 Experimental results at corresponding inputs

Tab.4 Experimental results with corresponding inputs for testing the network

Fig.5 Performance of single layer network with varying neurons

Fig.6 ANN (4-8-2) architecture

Fig.7 Training performance of (4-8-2) ANN architecture

Tab.5 Experimental and predicted values of MRR and

Fig.8 Comparison between experimental and predicted MRR

Fig.9 Comparison between experimental and predicted

Fig.10 Flow chart of ANN-NSGA-II approach

Fig.11 Effect of wheel speed on MRR and

Fig.12 Effect of pulse current on MRR and

Fig.13 Effect of pulse on-time on MRR and

Fig.14 Effect of duty factor on MRR and

Fig.15 Pareto optimal front

Tab.6 Optimal solution set and corresponding inputs-outputs

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