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Microstructural measurement and artificial neural network analysis for adhesion of tribolayer during sliding wear of powder-chip reinforcement based composites
Measurement ( IF 5.2 ) Pub Date : 2020-09-08 , DOI: 10.1016/j.measurement.2020.108417
Mayank Agarwal , Manvandra Kumar Singh , Rajeev Srivastava , Rakesh Kumar Gautam

The influence of powder-chip based reinforced LM6 aluminum alloy fabricated by a consolidated effect of stirring and squeeze process in the semi-solid stage is reported for wear properties. Effect of oxide formation on the worn surfaces due to the processing was noticed and experimental results showing that powder-chip based reinforcement with semi-solid slurry affects and gave excellent resistance against the adhesive wear. Evidences of protective tribo-layers observed from the worn surface investigation, profilometer analysis, EDS and XRD results which provides an appropriate explanation for the drop in the wear rate in alloys. Specific wear rate reduction due to the effect of oxides in mixed tribolayer has been studied by artificial neural network (ANN) with two stage nested analysis which reflects only 1.11% Mean Square Error as compared to experimental values. This model provides better understanding to identify influencing parameter for huge variable set of processing and validate with sufficient accuracy.



中文翻译:

粉末屑增强基复合材料滑动磨损过程中摩擦层附着力的显微组织测量和人工神经网络分析

据报道,在半固态阶段,通过搅拌和挤压过程的综合效应制造的粉末屑基增强LM6铝合金的磨损性能受到影响。注意到由于加工而在磨损表面上形成氧化物的影响,并且实验结果表明,具有半固态浆料的粉末碎片基增强材料会产生影响,并具有出色的抗粘着磨损性。从磨损表面调查,轮廓仪分析,EDS和XRD结果观察到的保护性摩擦层证据,为合金磨损率的下降提供了适当的解释。已通过人工神经网络(ANN)研究了氧化物在混合摩擦层中的特定磨损率降低,并进行了两阶段嵌套分析,仅反映了1。与实验值相比,均方误差为11%。该模型提供了更好的理解,可以识别巨大的处理变量集的影响参数并以足够的精度进行验证。

更新日期:2020-09-08
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