AN INTEGRATED OPTIMIZATION METHODOLOGY OF THE ELECTRICAL DISCHARGE MACHINING USING BOX BEHNKEN METHOD AND GENETIC AlGORITHMS

Abstract

Products used in critical industries like aerospace, automotive and power plant require specific and critical limits of the material properties. In aircraft a slight deviation from required specifications may result in the loss of both lives and equipment’s. Due to advancement in the industry, special material is used, therefore continuous study is required to optimize the properties of these materials. Nickel Chrome Alloy Steel and Powdered Metallurgical High-Speed Steel Alloy are important materials in industries, these alloys are used in different industries like automotive, aerospace, and electrical station industries, they are used in applications such as resistors, gears, shafts, tools. These component materials fail in the industry due to the high utilization rate and due to the lack of required specifications that are affected during the manufacturing process. Through the EDM manufacturing process, the temperature is very high, product properties and microstructure affected, the huge temperature in EDM lead to the material phase transformation, affect material properties, and surface defects are generated, and may cause the product to fail in the industry. Another issue, the post-treatment process should be performed after the manufacturing process, to restore the microstructure and to improve material properties, which costs a lot of money. In this research, two types of electrodes are used, silver-tungsten and copper-tungsten to optimize the EDM performance. The optimum combinations of the input parameters are important, which affects the EDM responses and material surface properties. In this study, an investigation of the effect of a new input parameter called pulse cycle time (Tc), in addition to dielectric liquid pressure (P), Voltage (V), and electrode material on the EDM responses was done. Box Behnken design methodology and Genetic Algorithm (GA) optimization methodology are used as an integrated approach to model and optimize the electrical discharge process. Two softwares, Minitab and Matlab are utilized for this purpose. Outputs are Machining Time (MT), Material Removal Rate (MRR), Tool Wear Rate (TWR), Over Cut (OC), Surface Roughness (SR), Hardness, Micro crack width (MCW), and Recast Layer thickness (RLT). 3D Laser Microscope is used to measure and inspect the surface defects. An interface expert system was built and used as a response predictive tool and used as a historical database. Based on result analysis and ANOVA tables, it is concluded that (Tc) as investigated new input parameter is significant, because its effect probability (p-value) is less than 0.05 for the most responses. From the results of Genetic Algorithm optimization, for the material Nickle Chrome Alloy Steel, it is concluded that the copper-tungsten electrode is the best for minimum MT, TWR, SR, MCS, RLT, and maximum MRR. While the silver-tungsten is the best for the minimum OC, and maximum Hardness, so the tool material is a significant parameter. The integrated methodology used in this study has proven as an effective tool, where the values of some outputs are improved up to 68.42% better than experimental values.