Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm

Multi-Objective Optimization of Die-Sinking EDM Process on AISI P-20 Tool Steel Using Cuckoo Search and Genetic Algorithm

Goutam Kumar Bose (Haldia Institute of Technology, India & West Bengal University of Technology, India), Pritam Pain (Haldia Institute of Technology, India) and Sayak Mukhopadhyay (Haldia Institute of Technology, India)
DOI: 10.4018/978-1-5225-1639-2.ch006
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Abstract

Electrical Discharge Machining (EDM) is nontraditional machining processes applied for precise machining and developing intricate geometries on work materials which are difficult to machine by conventional process. The present research work emphases on the die sinking EDM of AISI P20 tool steel, to study the effect of machining parameters such as pulse on time (POT), pulse off time (POF), discharge current (GI) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) and Overcut (OC) using square-shaped Cu tool with Lateral flushing. The experimentation is performed using L27 orthogonal array and significant process parameters are ascertained using Regression analysis. The influence of the important process parameters on individual responses are detected using Cuckoo search algorithm. The present chapter is aimed at multi-response optimization i.e. higher MRR, lower Ra and minimum OC, which is conceded out using Genetic Algorithm.
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Introduction

The intention of secondary manufacturing processes or material removal processes is to impart desired shape and size to the work material with acceptable tolerances, surface finish and surface integrity. Electric Discharge Machining (EDM) is anextensively used non-conventional machining process in the manufacturing of complex shaped dies, molds and critical parts of plastic molding, automobile, aerospace and other industrial applications. The process uses thermal energy of the sparks to machine electrically conductive parts regardless of the hardness of the work materials. This unique feature of EDM provides a noticeable advantage in the manufacture of complex shaped die and molds made up of hard materials which are complicated to machine by traditional machining processes (El-Hofy, 2005). The process involves removal of material through action of series of repeated electrical discharges of short duration and high current density between the tool–electrode and the workpiece under the effect of die–electric medium. The electrode never touches the workpiece but spark gets discharge through the dielectric across a very small gap. Thermal energy generates a plasma channel between the electrode and workpiece at a temperature range between 80000C to 120000C. EDM has proven to be applicable to machine electrically conductive materials such as stainless steels, tool steels, carbides, super alloys, ceramics etc., regardless of their other physical and metallurgical properties. The major factors influence in EDM machining are:

  • Spark gap,

  • Electrical parameters (pulse frequency, duty cycle, current and voltage) and

  • Physical properties of electrode, workpiece and dielectric fluid.

AISI P-20 tool steel is being employed for making plastic injection molding dies for various polymer products. The plastic injection molding process requires hardened and precision dies to meet stringent surface quality. This material is difficult to cut by conventional process hence EDM technique is adapted to process the material. The knowledge of cutting parameters and their responses is essential to manufacture precision components. Hence an investigation has been focused on EDM machining of AISI P-20 Tool steel with squared copper electrode.

Presently, problem solving optimization algorithms are mostly metaheuristics and all of them are nature motivated. The moment they originate, they are acknowledged by researchers and applied widely. The potency of all modern metaheuristic algorithms is the fact that they imitate the paramount properties in nature, particularly biological systems materialize from natural selection over millions of years. Cuckoo Search (CS) is a novel metaheuristic algorithm. It is being used for solving optimization problem. It was exploited in 2009 by Xin- She Yang and Suash Deb. Exceptionality of this algorithm is the compulsory brood parasitism behavior of several cuckoo species besides the Levy Flight behavior of some birds and fruit flies. A Lévy flight is a haphazard move in which the movement-lengths are spread according to a heavy-tailed probability distribution. After a considerable amount of steps, the distance from the origin of the random walk has a tendency to a stable distribution.

Genetic Algorithm (GA) is a search heuristic that emulate the process of natural selection. This heuristic process is used to generate useful solutions to optimization problems. GA belong to the larger class of Evolutionary Algorithms (EA), which yield solutions to optimization problems by means of techniques inspired by the process of natural evolution, such as birthright, metamorphosis, selection, and crossover (Rao, 2011). It simulates the survival of the fittest amongst individuals over successive generation for elucidating a problem. Each generation comprises of a population of character strings that are similar to the chromosome that we perceive in our DNA. Each individual represents a point in a search space and a probable solution. The entities in the population are then made to proceed through a system of evolution.

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