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Studies on Some Aspects of Multi-objective Optimization: A Case Study of Electrical Discharge Machining Process

机译:多目标优化某些方面的研究:以电火花加工工艺为例

摘要

Electrical Discharge Machining (EDM) finds extensive application in manufacturing of dies, molds and critical parts used in the automobile and other industries. The present study investigates the effects of different electrodes, deep cryogenic treatment of tools subjected to different soaking duration and a hybrid approach of powder mixed EDM of cryogenically treated electrodes on machinability of Inconel 718 super alloy. Inconel 718 has been used as the work material owing to its extensive application in aerospace industries. A Box– Behnken design of response surface methodology (RSM) has been adopted to estimate the effect of machining parameters on the performance measures. The machining efficiency of the process is evaluated in terms of material removal rate (MRR), electrode wear ratio (EWR), surface roughness, radial overcut and white layer thickness which are function of process variables viz. open circuit voltage, discharge current, pulse-on-time, duty factor and flushing pressure. In this work, a novel multi-objective particle swarm optimization algorithmud(MOPSO) has been proposed to get the Pareto-optimal solution. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence and to improve the solution quality. To avoid subjectiveness and impreciseness in the decision making, the Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory (MDT). Finally, a thermal model based on finite element method has been proposed to predict the MRR and tool wear rate (TWR) when work piece is machined with variety of electrode materials. A coupled thermo-structural model has been also proposed to estimate the residual stresses. The numerical models were validated through experimentations. Parametric study is carried out on the proposed model to understand the influence of important process parameters on the performance measures. The study offers useful insight into controlling the machining parameters to improve the machining efficiency of the EDMed components.ud
机译:放电加工(EDM)在汽车和其他行业中使用的模具,模具和关键零件的制造中得到了广泛的应用。本研究研究了不同电极,不同浸泡时间的工具的深冷处理以及深冷处理过的电极的粉末混合EDM混合方法对Inconel 718超级合金可加工性的影响。 Inconel 718由于在航空航天工业中的广泛应用而被用作工作材料。已采用Box-Behnken设计的响应面方法(RSM)来估计加工参数对性能指标的影响。根据材料去除率(MRR),电极磨损率(EWR),表面粗糙度,径向过切和白层厚度来评估工艺的加工效率,这些是工艺变量的函数。开路电压,放电电流,脉冲接通时间,占空比和冲洗压力。在这项工作中,提出了一种新颖的多目标粒子群优化算法 ud(MOPSO)来获得帕累托最优解。 MOPSO算法中引入了主要在遗传算法中使用的变异算子,以避免过早收敛并提高求解质量。为了避免决策中的主观性和不精确性,通过MOPSO获得的帕累托最优解已通过通过最大偏差理论(MDT)获得的综合评分进行了排名。最后,提出了一种基于有限元方法的热模型,以预测在用各种电极材料加工工件时的MRR和工具磨损率(TWR)。还提出了耦合的热结构模型来估计残余应力。通过实验验证了数值模型。对提出的模型进行了参数研究,以了解重要过程参数对性能指标的影响。该研究为控制加工参数以提高电火花加工零件的加工效率提供了有用的见识。 ud

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    Mohanty Chinmaya Prasad;

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  • 年度 2015
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