首页> 外文期刊>Journal of Mechanical Science and Technology >Multi-objective optimization of powder mixed electric discharge machining parameters for fabrication of biocompatible layer on beta-Ti alloy using NSGA-II coupled with Taguchi based response surface methodology
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Multi-objective optimization of powder mixed electric discharge machining parameters for fabrication of biocompatible layer on beta-Ti alloy using NSGA-II coupled with Taguchi based response surface methodology

机译:NSGA-II结合基于Taguchi的响应面方法在β-Ti合金上制备生物相容层的粉末混合放电加工参数的多目标优化

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摘要

The success of an implant depends upon surface characteristics like roughness, topography, chemistry and hardness. The fabrication of a hard surface in combination with micron-, submicron- and nano-scale surface roughness is a great challenge for biomanufacturing industries. The surface microhardness (MH) needs to be maximized while controlling the Surface roughness (SR). The present research is the first study in which the application of Non-dominated sorting genetic algorithm (NSGA)-II coupled with Taguchi based Response surface methodology (RSM) is used to predict the optimal conditions of Powder mixed electric discharge machining (PMEDM) parameters to fabricate the biocompatible surface on beta-phase Ti alloy. Batch vial tests were first carried out in accordance with the L-25 orthogonal array. ANOVA analysis gave the significant influencing factors and then mathematical models were developed between input parameters and output responses like SR and MH using Taguchi based RSM technique. These models were then optimized using NSGA-II to obtain a set of Pareto-optimal solutions. From the series of multiple solutions, the best optimal condition to achieve required low SR and high MH was determined, which are 13 A peak current, 5 mu s pulse duration, 8% duty cycle (longer pulse-interval) and 8 g/l silicon powder concentration for achieving a required low SR and high MH. The MH considerably increased about 184% compared to the base material, and about 1.02 mu m SR can be achieved in combination with micron-, submicron- and nano-scale surface features.
机译:植入物的成功取决于表面特性,例如粗糙度,形貌,化学性质和硬度。结合微米级,亚微米级和纳米级的表面粗糙度来制造坚硬的表面对于生物制造行业来说是一个巨大的挑战。需要在控制表面粗糙度(SR)的同时最大化表面显微硬度(MH)。本研究是首次将非支配排序遗传算法(NSGA)-II与基于Taguchi的响应面方法(RSM)结合使用来预测粉末混合放电加工(PMEDM)参数的最佳条件的研究在β相Ti合金上制造生物相容性表面。首先根据L-25正交阵列进行批量样品瓶测试。 ANOVA分析给出了显着的影响因素,然后使用基于Taguchi的RSM技术在输入参数和输出响应(如SR和MH)之间建立了数学模型。然后使用NSGA-II对这些模型进行优化,以获得一组帕累托最优解。从一系列的解决方案中,确定了实现所需的低SR和高MH的最佳最佳条件,即13 A峰值电流,5μs脉冲持续时间,8%占空比(较长的脉冲间隔)和8 g / l硅粉浓度以实现所需的低SR和高MH。与基础材料相比,MH大大提高了约184%,与微米级,亚微米级和纳米级表面特征相结合,可实现约1.02μmSR。

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