Modeling and optimization of WEDM machining of armour steel using modified crow search algorithm approach
Springer Science and Business Media LLC - Trang 1-21 - 2024
Tóm tắt
Armour steel is a type of steel that is often used in armoured vehicles, military equipment, and structural components that require a high level of resistance to penetration. Because of its high strength and hardness, cutting armour steel presents various obstacles. To overcome these challenges in armour steel cutting, innovative cutting methods, specialized equipment, and careful process planning are required. The current work discusses an experimental investigation that focuses on input process parameters of wire electrical discharge machining and on multi-objective optimization to obtain the best cutting rate (CR), surface roughness (SR), wire electrode temperature (TE) and material removal rate (MRR) for armour steel. The fractional factorial approach has been used in the investigation, with pulse off time (B), pulse on time (A), spark voltage (D), peak current (C), wire feed (E) as machining parameters and workpiece thickness (F) as a material parameter. The main and interaction impacts of the input parameters on the response parameters have been examined using the main effect plot, interaction plot, and ANOVA analysis, followed by the development of regression modeling. The research revealed that the pulse on time and workpiece thickness have the most significant contributions to CR and SR, with 55.25% and 21.77% contributions for CR and 67.87% and 8.72% contributions for SR, respectively. Toff and spark voltage are the major contributors for TE with 33.58% and 26.30% respectively and Ton is a major contributor with 70.04% for MRR. The ideal input parameters for CR [0.71 (mm/min)], SR [2.46 (microns)], TE [52 (°C)] and MRR [23.85 (mm3/min)] have been found to be A2B1C2D1E1F1, A1B2C2D2E1F2, A1B2C1D1E1F2 and A1B2C1D1E2F1 respectively. The modified crow search algorithm (MCSA) has been used in this study for single and multi-objective optimization, and their results contrast with other methods such as Rao-1 and Shuffled Frog Leaping Algorithm (SFLA). According to the conclusions of the present investigation, this study demonstrates that the MCSA technique exceeds the Rao-1 and SFLA techniques in terms of producing globally optimal outcomes for the specific problem under examination.
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