利用响应面法和粒子群优化对哈氏合金 C276 超级合金的微放电加工进行多目标参数优化

M. Parthiban, M. Harinath, V.S.S. Krishaanth, B. Logesh, Ahamed N.J. Musthak
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引用次数: 0

摘要

近年来,由于超级合金在高温下具有广泛的承载能力等优点,航空航天工业对超级合金的应用需求不断增加。哈氏合金就是这样一种超级合金,由于在其他镍基超级合金中具有良好的耐腐蚀性和耐热性,因此被广泛应用于航空航天领域。本研究旨在了解哈氏合金 C276 的微电火花加工过程中,电压、脉冲关闭时间(Toff)和脉冲开启时间(Ton)等输入工艺参数对材料去除率(MRR)、刀具磨损率(TWR)、过切削率(OC)和锥度比(TR)等响应变量的影响。在对哈氏合金 C276 材料进行微钻孔时,使用了直径为 0.5 毫米的铜电极。为了研究输入和输出特性之间的联系,应用了一种称为响应面方法(RSM)的技术,这是一种数学和统计方法的集合。实验运行是在基于 RSM 的箱-贝肯设计(BBD)的帮助下进行的。对实验结果进行计算,并使用方差分析来确定影响最大的变量。此外,还利用粒子群优化(PSO)来优化结果,并与响应面方法进行比较。PSO 优化结果显示,预期结果与实验结果之间的相关性强于 RSM 方法。
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Multi-Objective Parametric Optimization of Micro-Electro Discharge Machining of Hastelloy C276 Super Alloy Using Response Surface Methodology and Particle Swarm Optimization
The need for the application of superalloys in aerospace industries in recent years has increased owing to its benefits such as extensive load-bearing capability under high temperatures. Hastelloy is one such superalloy that is extensively utilized in the aerospace sector because of its good corrosion and heat resistance among the other nickel-based superalloys. In this work, the investigation is conducted to understand the effects of input process parameters such as voltage, pulse off time (Toff), and pulse on time (Ton) on the response variables, namely Material removal rate (MRR), Tool wear rate (TWR), Overcut (OC), and Taper Ratio (TR) during micro-EDM of Hastelloy C276. For micro drilling the Hastelloy C276 material, a copper electrode with a diameter of 0.5 mm is utilized. To investigate the connections between the input and output characteristics, a technique known as the Response Surface Methodology (RSM), which is a collection of mathematical and statistical methodologies, is applied. The experimental runs are carried out with the help of the RSM-based Box-Behnken design (BBD). The experimental outcomes were computed, and ANOVA was used to identify the most influential variables. In addition, particle swarm optimization (PSO) was utilized to optimize the results, which were compared to the Response surface methodology approach. The outcomes of the PSO-optimized results revealed a strong correlation between expected and experimental outcomes over the RSM approach.
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