MULTI OBJECTIVE MACHINING ESTIMATION MODEL USING ORTHOGONAL AND NEURAL NETWORK

Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi orthogonal and NN modeling approach is tested on two types of electrical discharge machining (EDM) operations; Cobalt Bonded Tungsten Carbide (WC-Co) and Inconel 718 to observe the efficiency of proposed approach on different numbers of objectives. WC-Co EDM considered two objective functions and Inconel 718 EDM considered four objective functions. It is found that one hidden layer 4-8-2 layer recurrent neural network (LRNN) is the best estimation model for WC-Co machining and one hidden layer 5-14-4 cascade feed forward back propagation (CFBP) is the best estimation model for Inconel 718 EDM. The results are compared with trial-error approach and it is proven that the proposed modeling approach is able to improve the machining performances and works efficiently on two-objective problems.

[1]  R. Parveen,et al.  A Review and Survey of Artificial Neural Network in Medical Science , 2016 .

[2]  Mohan Kumar Pradhan,et al.  Recurrent neural network estimation of material removal rate in electrical discharge machining of AISI D2 tool steel , 2011 .

[3]  Azlan Mohd Zain,et al.  Computational Approach for Multi Performances Optimization of EDM , 2016 .

[4]  H. Juhr,et al.  Generation of parameter technologies for EDM die sinking with artificial neural networks (ANN) and nonlinear regression functi- ons (NRF) , 2004 .

[5]  Mao Ye,et al.  Fast crowd density estimation with convolutional neural networks , 2015, Eng. Appl. Artif. Intell..

[6]  S. S. Pande,et al.  Intelligent process modeling and optimization of die-sinking electric discharge machining , 2011, Appl. Soft Comput..

[7]  Habibollah Haron,et al.  Regression and ANN models for estimating minimum value of machining performance , 2012 .

[8]  Shahin Rahimifard,et al.  STATE OF THE ART IN WIRE ELECTRICAL DISCHARGE MACHINING (WEDM) , 2004 .

[9]  P. Saha,et al.  Artificial neural network model in surface modification by EDM using tungsten–copper powder metallurgy sintered electrodes , 2010 .

[10]  Yunn-Shiuan Liao,et al.  Optimization of machining parameters of Wire-EDM based on Grey relational and statistical analyses , 2003 .

[11]  Mahmudur Rahman,et al.  Neural network modeling and analysis for surface characteristics in electrical discharge machining , 2014 .

[12]  Mohan Kumar Pradhan,et al.  ANN modelling for surface roughness in electrical discharge machining: a comparative study , 2013 .

[13]  R. Mahdavinejad.  Modeling and Optimization of Electrical Discharge Machining of SiC Parameters, Using Neural Network and Non-Dominating Sorting Genetic Algorithm (NSGA II) , 2011 .

[14]  Habibollah Haron,et al.  Neural network model for prediction of facial caricature landmark configuration using modified procrustes superimposition method , 2015, SOCO 2015.

[15]  M. S. Shunmugam,et al.  Multi-objective optimization of wire-electro discharge machining process by Non-Dominated Sorting Genetic Algorithm , 2005 .

[16]  Pei-Jen Wang,et al.  Predictions on surface finish in electrical discharge machining based upon neural network models , 2001 .

[17]  K. Palanikumar,et al.  Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II) , 2009 .

[18]  H. Haron,et al.  Akademia Baru Experimental Study of Genetic Algorithm Optimization on WC / Co Material Machining , 2016 .

[19]  SOUMYAKANT PADHEE,et al.  Multi-objective parametric optimization of powder mixed electro-discharge machining using response surface methodology and non-dominated sorting genetic algorithm , 2012, Sadhana.

[20]  Angelos P. Markopoulos,et al.  Artificial neural network models for the prediction of surface roughness in electrical discharge machining , 2008, J. Intell. Manuf..

[21]  Habibollah Haron,et al.  Fuzzy logic for modeling machining process: a review , 2013, Artificial Intelligence Review.

[22]  Chitra Sharma,et al.  Multi-Objective Optimization of Die-Sinking Electric Discharge Machining , 2011 .

[23]  K. Palanikumar,et al.  Optimization of electrical discharge machining characteristics of WC/Co composites using non-dominated sorting genetic algorithm (NSGA-II) , 2008 .

[24]  K. Maity,et al.  ANN modelling and Elitist teaching learning approach for multi-objective optimization of μ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt , 2016, Journal of Intelligent Manufacturing.

[25]  Zainal Abidin,et al.  Comparison of Diverse Ensemble Neural Network for Large Data Classification , 2015 .

[26]  Pei-Jen Wang,et al.  Comparisons of neural network models on material removal rate in electrical discharge machining , 2001 .

[27]  Osman Taylan,et al.  A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process , 2015, Comput. Ind. Eng..

[28]  M. Ghoreishi,et al.  Neural-network-based modeling and optimization of the electro-discharge machining process , 2008 .

[29]  Azlan Mohd Zain,et al.  Overview of Support Vector Machine in Modeling Machining Performances , 2011 .

[30]  Habibollah Haron,et al.  Review of ANN Technique for Modeling Surface Roughness Performance Measure in Machining Process , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

[31]  Stephen T. Newman,et al.  State of the art electrical discharge machining (EDM) , 2003 .

[32]  R. K. Bhoi,et al.  Artificial Neural Network Prediction of Material Removal Rate in Electro Discharge Machining , 2005 .