Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm

https://doi.org/10.1016/j.jmatprotec.2008.04.003Get rights and content

Abstract

The present work is aimed at optimizing the surface roughness of die sinking electric discharge machining (EDM) by considering the simultaneous affect of various input parameters. The experiments are carried out on Ti6Al4V, HE15, 15CDV6 and M-250. Experiments were conducted by varying the peak current and voltage and the corresponding values of surface roughness (SR) were measured. Multiperceptron neural network models were developed using Neuro Solutions package. Genetic algorithm concept is used to optimize the weighting factors of the network. It is observed that the developed model is within the limits of the agreeable error when experimental and network model results are compared. It is further observed that the error when the network is optimized by genetic algorithm has come down to less than 2% from more than 5%. Sensitivity analysis is also done to find the relative influence of factors on the performance measures. It is observed that type of material effectively influences the performance measures.

Introduction

The selection of appropriate machining conditions for minimum surface roughness during the electric discharge machining (EDM) process is based on the analysis relating the various process parameters to surface roughness (SR). Traditionally this is carried out by relying heavily on the operator's experience or conservative technological data provided by the EDM equipment manufacturers, which produced inconsistent machining performance. The parameter settings given by the manufacturers are only applicable for the common steel grades. The settings for new materials such as titanium alloys, aluminium alloys, special steels, advanced ceramics and metal matrix composites (MMCs) have to be further optimized experimentally. Optimization of the EDM process often proves to be difficult task owing to the many regulating machining variables. A single parameter change will influence the process in a complex way. Thus the various factors affecting the process have to be understood in order to determine the trends of the process variation. The selection of best combination of the process parameters for an optimal surface roughness involves analytical and statistical methods. In addition, the modeling of the process is also an effective way of solving the tedious problem of relating the process parameters to the surface roughness.

The settings for new materials such as titanium alloys, aluminium alloys and special steels have to be further optimized experimentally. It is also aimed to select appropriate machining conditions for the EDM process based on the analysis relating the various process parameters to SR. It is aimed to develop a methodology using an input–output pattern of data from an EDM process to solve both the modeling and optimization problems. The main objective of this research is to model EDM process for optimum operation representing a particular problem in the manufacturing environment where, it is not possible to define the optimization objective function using a smooth and continuous mathematical formula. It has been hard to establish models that accurately correlate the process variables and performance of EDM process. Improving the surface quality is still a challenging problem that constrains the expanding application of the technology. When new and advanced materials appear in the field, it is not possible to use existing models and hence experimental investigations are always required. Undertaking frequent tests or many experimental runs is also not economically justified. In the light of this, the present work describes the development and application of a hybrid artificial neural network (ANN) and genetic algorithm (GA) methodology to model and optimize the EDM process.

At first, experiments involving discharge machining of Ti6Al4V, HE15, 15CDV6 and M250 at various levels of input parameters namely current, voltage and machining time are conducted to find their effect on the surface roughness. The second phase involves the establishment of the model using multi-layered feed forward neural network architecture. GA finds the optimum values of the weights that minimize the error between the measured and the evaluated (output from the network) performance parameters, where genetic evolution establishes a strong intercommunication between the neural network pattern identification and the GA optimization tasks. The developed hybrid model is validated with some of the experimental data, which was not used for developing the model.

Section snippets

Literature survey

In the past few decades, a few EDM modeling tools correlating the process variables and surface finish have been developed. Tsai and Wang, 2001a, Tsai and Wang, 2001b, Tsai and Wang, 2001c established several surface models based on various neural networks taking the effects of electrode polarity in to account. They subsequently developed a semi-empirical model, which is dependent on the thermal, physical and electrical properties of the work piece and electrode together with pertinent process

Experimental setup

A number of experiments were conducted to study the effects of various machining parameters on EDM process. These studies have been undertaken to investigate the effects of current, voltage, machining time and type of material on surface roughness. All the four materials were discharge machined with copper tool electrode. Kerosene was used as dielectric medium. The experiments were conducted on Elektra 5535 *PS Eznc Die Sinking Electric Discharge Machine.

Experimental procedure

Work pieces were cut into specimens by

Hybrid model

In manufacturing there are certain processes that are not possible to describe using analytical models for GA optimization. It has been hard to establish models that accurately correlate the process variables and performance of EDM process. Improving surface quality is still challenging problem that constrain the expanding application of the technology. When new and advanced materials appear in the field, it is not possible to use existing models and hence experimental investigations are always

Conclusions

From the experiments that were conducted on the die sinking EDM and the ANN models developed, the following interesting conclusions were drawn.

  • 1.

    When current increases at constant voltage surface finish reduces tremendously.

  • 2.

    For titanium machining at currents less than/equal to 15 A is more suitable.

  • 3.

    Special conclusion for titanium alloy is that it has good surface finish at voltage 40 V and at constant current of 16 A.

  • 4.

    Aluminium alloy has good erosion properties than titanium alloy due to the high

References (26)

  • O. Yilmaz

    A user friendly fuzzy based system for the selection of electro discharge machining process parameters

    J. Mater. Process. Technol.

    (2006)
  • J.H. Zhang et al.

    Study on the electro-discharge machining of a hot pressed alluminium oxide based ceramic

    J. Mater. Process. Technol.

    (1997)
  • M. Ghoreishi et al.

    Vibro-rotary electrode: a new technique in EDM drilling, performance evaluation by statistical modeling and optimization

  • Cited by (0)

    View full text