Wire-EDM is a highly complex process, which is characterized by non-linear behavior. Due to a large number of input parameters, a data mining approach based on machine learning is followed in this paper to model this process. The model was trained on experimental data collected from carefully conducted experiments. The model was also tested on additional data. It is found that the model built using data mining provides results with desired accuracy. The important parameters have been identified and reported. © 2003 Elsevier Science B.V. All rights reserved.

Shunmugam M S

Department of Mechanical Engineering

Shajan Kuriakose

Cutting

data mining

Learning systems

CUTTING PARAMETERS

Electric discharge machining

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Data mining applied to wire-EDM process

Journal of Materials Processing Technology

Surface topography of cylinder liners plays an important role in terms of functionality of the engine. The surface is finished by honing process in various stages namely rough, finish and finally plateau honing to have proper plateau portion (bearing area) at the top to resist wear and grooves at desired honing angle to retain lubrication during engine operation. Selection of parameters and their levels in plateau honing operations to achieve required surface topography is found to be very difficult. For the first time, data mining approach is used in this investigation to select significant honing parameters and their levels considering two important bearing area parameters namely core roughness depth (R k) and material portion in the valley zone (M r2) together. Data mining model was trained and tested using experimental data. The significant input plateau honing parameters have been identified and graphical representation of the decision tree has been also presented for easier understanding. Copyright © 2010 Inderscience Enterprises Ltd.

International Journal of Machining and Machinability of Materials

Data mining approach for selection of plateau honing parameters with dual objectives

Wire-electro discharge machining (Wire-EDM or WEDM) is one of the important non-traditional machining processes, which is used for machining of difficult-to-machine materials and intricate profiles. Being a complex process, it is very difficult to determine optimal parameters for improving cutting performance. Cutting velocity and surface finish are most important output parameters, which decide the cutting performance. There is no single optimal combination of cutting parameters, as their influences on the cutting velocity and the surface finish are quite the opposite. In the present work, a multiple regression model is used to represent relationship between input and output variables and a multi-objective optimization method based on a Non-Dominated Sorting Genetic Algorithm (NSGA) is used to optimize Wire-EDM process. A non-dominated solution set has been obtained and reported. © 2005 Elsevier B.V. All rights reserved.

Multi-objective optimization of wire-electro discharge machining process by Non-Dominated Sorting Genetic Algorithm

Nickel alloys have been popular for their strength and good resistance to corrosion and heat. Especially due to these properties of nickel alloys, they can be used in the manufacture of various aerospace and aeronautical components. Wire electrical discharge machining [WEDM] is widely used for machining of various alloys since is reliable in terms of precision and accuracy. In this paper, three different aerospace alloys of nickel viz. Inconel, Monel and Incoloy have been taken for experimentation with WEDM machining. The process optimization is done by using Taguchi method considering four parameters viz. Pulse ON time [TON], Pulse OFF time [TOFF], Wire feed rate and Current at three different levels each. The machining rate and surface roughness for each sample is measured separately. Then the results are compared and then based on the comparison, the optimum parameters for machining of each alloy are determined as well as the alloy most suited for machining using WEDM is also for aerospace applications are inferred. © 2017 Elsevier Ltd.

Materials Today: Proceedings

WEDM machining on Aerospace Materials for improving Material Properties

Intelligent control of any manufacturing process requires an appropriate modeling of the process. Wire-cut EDM, being a highly complex process with non-linear behavior, is modeled by fuzzy logic approach in this paper. The knowledge base for the fuzzy model has been developed using the experimental results. Membership functions are defined for each parameter and a total of 10 input and 2 output parameters are considered to build the model. The results of the fuzzy model are compared with experimental values and a statistical analysis is presented.

Proceedings of SPIE - The International Society for Optical Engineering

Fuzzy logic modeling of wire-cut EDM process

Design Automation for Embedded Systems

ESWEEK 2009 special issue introduction

International Journal of Electrical Machining

“ Delivering the Difference ” CHARMILLES Ultra-high Precision Wire EDM

International Journal of Machine Tools and Manufacture

Observations on chip formation and acoustic emission in machining Ti–6Al–4V alloy

Experimental investigation of effects of cutting parameters on surface roughness in the WEDM process

Journal	Journal of Materials Processing Technology
ISSN	09240136
Open Access	No