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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Tribological performance of nanographite-based metalworking fluid and parametric investigation using artificial neural network
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Tribological performance of nanographite-based metalworking fluid and parametric investigation using artificial neural network

机译：基于纳米型金属加工的摩擦学性能和使用人工神经网络的参数调查

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摘要

This paper investigates the impact of palm oil methyl ester (POME) and graphite nanoflakes as additives on the thermophysical and tribological properties of naphthenic and groundnut oil blends. Different ratios of the two oil blends containing the selected additives were formulated and tested. The results show that both POME and graphite nanoflakes could be potential friction modifiers. Notably, the four-ball tribometer test reveals that the addition of 7 vol% of POME to naphthenic oil and 0.01 wt% graphite nanoflakes to naphthenic oil results in the reduction of wear scar diameter by 24% and 59%, respectively. Surface micrographs of aluminum workpiece subjected to simple turning operation in a CNC machine corroborated the wear test results. POME in naphthenic oil resulted in better surface finish relative to POME-formulated groundnut oil. The experimental data were analyzed using artificial neural network (ANN) to gain a better understanding of the entire work. The sensitivity analysis developed from the chosen ANN models exhibited the nature of influence of the input parameters (groundnut concentrations, POME, and naphthenic oil) on the outputs (Noack volatility, thermal conductivity, and wear).
机译：本文研究了棕榈油甲酯（Pome）和石墨纳米薄片的影响，作为环烷基和旱地油混合物热物理和摩擦学性质的添加剂。配制并测试含有所选添加剂的两种油混合物的不同比例。结果表明，玉马和石墨纳米薄片都可能是潜在的摩擦改性剂。值得注意的是，四球摩特计测试显示，向环烷体中加入7体积的Pome占环氧树脂和0.01重量％的石墨蛋白，导致耐磨疤痕直径减少24％和59％。铝制工件的表面显微照片在数控机床中经过简单的转动操作，证实了磨损试验结果。环氧萘油中的圆顶导致较好的表面光洁度相对于圆顶配制的地生油。使用人工神经网络（ANN）分析实验数据，以更好地了解整个工作。从所选的ANN模型中开发的敏感性分析表现出输入参数（Troundnut浓度，圆顶油）对输出的影响（Noack波动，导热率和磨损）的性质。

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