Optimization of Correlated and Conflicting Responses of ECM Process Using Flower Pollination Algorithm

Optimization of Correlated and Conflicting Responses of ECM Process Using Flower Pollination Algorithm

Bappa Acherjee, Debanjan Maity, Arunanshu S. Kuar
Copyright: © 2020 |Pages: 15
DOI: 10.4018/IJAMC.2020100101
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The electrochemical machining (ECM) process has been investigated in this article to achieve the desired process performances by optimizing the machining parameters using the flower pollination algorithm (FPA). Two major process performances namely: material removal rate (MRR) and surface roughness (Ra), which are correlated and conflicting in nature, are optimized with respect to the key process parameters. The regression equations developed by using experimental data are used as objective functions in the flower pollination algorithm. Objectives are set to find the optimal set of process parameters to fulfil a single objective as well as multiple objectives. The performance of the algorithm is checked in terms of accuracy, convergence speed, number of optimized populations, and computational time. The mean values of functional evaluations for MRR and Ra obtained are close to their respective optimal results, which ensures the quality of the convergence. It is further seen that FPA can predict the true overall parametric trends as it does not require keeping any parameter as constant during the analysis.
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1. Introduction

Electro chemical machining (ECM) is one of the most potential modern machining processes which uses electrical current to remove the metal and relies on the principle of electrolysis for material removal. If two electrodes are placed in a bath containing a conducting liquid, and a DC potential is applied across them, then metal can be depleted from the anode and plated on the cathode. Electrolysis principle has been used for electroplating where the objective is to deposit metal on work piece. Since in ECM the objective is to remove the metal, the work piece is made as anode. When current is passed, a controlled anodic electrochemical dissolution of the work piece occurs (Kozak, 1998). During the process the tool is provided with a downward feed motion at a controlled rate. The shape to be produced on the work piece depends on the form of the tool. The electrolyte is pumped through the gap between the tool and the work piece, and the electrolyte is so chosen that the anode is dissolved but no deposition take place at cathode (Ghosh and Mallik, 2010). Figure 1 shows the schematic view of a typical electrochemical machining set up. The electrochemical machining system has the following modules: power supply, electrolyte filtration and delivery system, tool feed system and working tank.

Figure 1.

Scheme of an electrochemical machining set up


ECM has been used in industry for cutting, deburring, drilling and shaping of metals (Bhattacharya and Sorkhel, 1999; Lee et al., 2002; Ebeid et al., 2004; Bhattacharyya et al., 2006; Rao et al., 2008). ECM process is employed for generating intricate shapes in components in the defense and aerospace industries, automotive industries, forging die manufacturing, and recently, in micro-manufacturing (Bhattacharya and Sorkhel, 1999; Ebeid et al., 2004; Bhattacharyya et al., 2006; Rao et al., 2008; Asokan et al., 2008). Bhattacharya and Sorkhel (1999) developed a microprocessor-based electrochemical machining setup with controlled electrolyte flow and automatic tool feed for performing machining operation on EN 8 steel using an insulated cylindrical solid brass tool. The effect of machining parameters on the process performances namely material removal rate and over cut is investigated. Bhattacharyya et al. (2006) also carried out an experimental investigation to study the influence of tool vibration on machine performances in electrochemical micro-machining of copper. As the process performance depends on several machining parameters, optimization of the ECM parameters is thus creating much attention among the researchers. Rao et al. (2008) made an effort to optimize the ECM process performances including dimensional accuracy, tool life, material removal rate and machining cost with respect to the important machining parameters such as the tool feed rate, electrolyte flow velocity and applied voltage. Asokan et al. (2008) have developed regression and artificial neural network models for optimizing of process performances namely material removal rate and surface roughness in terms of control parameters like current, voltage, flow rate and inter electrode gap in ECM of hardened steel. Samanta and Chakraborty (2011) obtained optimal settings of machining parameters for some nontraditional machining processes including ECM, using artificial bee colony algorithm. Rao and Kalyankar (2011) obtained combination of optimal process parameters of ECM using a teaching learning-based optimization algorithm. Das et al. (2014) investigated on electrochemical machining of EN 31 steel for optimization of material removal rate and surface roughness using artificial bee colony algorithm.

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