Electro-chemical machining is a non-conventional machining method that is used for machining of very complicated shape. In this chapter an attempt has been made to carry out multi-objective optimization of the surface roughness (SR) and material removal rate (MRR) for the ECM process of EN 19 on a CNC ECM machine using copper electrode through evolutionary optimization techniques like teaching-learning-based optimization (TLBO) technique and biogeography-based optimization (BBO) technique. The input parameters considered are electrolyte concentration, voltage, feed rate, inter-electrode gap. TLBO and BBO techniques were used to obtain maximum MRR and minimum SR. In addition, obtained optimized values were validated for testing the significance of the TLBO and BBO techniques, and a very small error value of MRR and SR was found. BBO outperformed TLBO in every aspect like less percentage error and better-optimized values; however, TLBO took less computation time than the BBO.
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Conventional machining processes employ tools that are tougher than the workpieces to take the work material to its plastic state (beyond the yield stress) until it gets removed from the parent work material. Alloys consisting of alloying elements such as wolfram, chromium, metallic elements, vanadium etc possess high hardness value, high strength to weight ratio and high heat resistance which makes it impossible for conventional machining processes to work efficiently, effectively and with ease of cutting due to the physical interaction between the tool and the work material(Aggarwal et al., (2015)). These drawbacks can be overcome by implementing non-conventional machining processes which employs other sources of energy for metal cutting rather than by physical interaction between the work and the tool. These sources of energy can be light, heat, electricity, water pressure and kinetic energy of the abrasive particles, based on which non conventional machining processes can be divided into various types. Electro chemical machining (ECM) is one such type which employs electrolytes between the electrode work and the tool to remove the material from the surface.
ECM process works on the principle that when electric voltage is applied across the anode and cathode electrodes under the presence of an electrolytic medium, material starts to get remove material from the anode surface due to electron exchange resulting in removal of material. During ECM process, work piece is treated an anode, while the tool as a cathode and a voltage of 5-30 V is applied with a current density of 10-200 A/cm. The electrolytic aqueous solution commonly used is of NaCl or NaNO3 and is selected to prevent from any alteration in the shape of the tool. A pump speed (3 to 60m/s) of the electrolyte is maintained to expel the removed material from the gap between the cathode tool and the anode work surface. A constant feed (about 0.02mm/s) of the tool towards the work surface is maintained and a steady state gap is reached which generates a negative profile of the cathode tool on the work surface and gradually deepening for complete cut. The components of a general ECM machine are shown in figure 1.
In the current chapter ECM machining of EN19 was performed for finding the optimal values of the input parameters using two different optimization technique which are the Biogeography based optimization (BBO) and Teaching learning based optimization techniques (TLPO). Results from these techniques are validated by conducting experiments considering the optimal input parameters and finding the error percentages of the output parameter values with the theoretical one as obtained from the two techniques. A thorough literature review, experimental setups, input and output parameters, the BBO and TLBO techniques, the results and discussions are all described in the subsequent sections.
Figure 1. Schematic diagram of ECM process
One of the most significant parts of industries associated with manufacturing is the machining process. The intricate shapes are machined using the non-traditional machining process. Engineers are required to give their best and produce products with higher performance and better effectiveness. As such ample effort have been made to identify optimal solution for the machining processes using different optimization techniques as such Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Simulated Annealing (SA), Genetic Algorithm etc. (Anitha et al. (2016). Owing to the importance of optimization techniques, researchers have developed several optimization techniques that depict the nature in their operation which resulted in reduced cycle time and better machining.
Biogeography based optimization (BBO) comes under the category of heuristic optimization technique which was first introduced by Dan Simon (professor at Cleveland State University in the Department of Electrical and Computer Engineering) in the year 2008 and is based on an iterative technique to optimize stochastic functions by improving the fitness function. This technique is motivated by the nature of the biological species in their locomotion, distribution, evolution, emergence and extinction patterns. The suitability of the habitat is computed and is given a habitat suitability index (HSI), the definition of which complies with the nature of the objective function of the optimization technique and is basically a fitness value as in the case of TLBO.