含有纳米 HAP 填充物的单质和混合 CF/Ep 复合材料的粘着磨损特性

Divya GURKAR SOMASHEKAR, Naveena BETTAHALLI ESWAREGOWDA, Suresha Bheemappa
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摘要

含有两种以上增强材料的复合材料被称为混合复合材料。 通过混合填料、纤维和基体来定制复合材料,会比单一复合材料产生更好的性能。因此,目前的研究工作致力于开发碳纤维环氧混合纳米复合材料,其中包括不同重量百分比的羟基磷灰石(HAP),并使用双体滑动磨损法评估其对摩擦学特性的潜在影响。采用田口技术(L27 阵列)研究了填料含量(0%、1.5% 和 3%)、载荷(30、45 和 60 N)、滑动速度(1、2 和 3 m-s-1)和滑动距离(1000、2000 和 3000 m-s-1)等参数对所开发复合材料磨损损失的影响。结果表明,1.5 wt% HAP 复合材料组合的 Ks 和 COF 最低。1.5 wt% HAP 填料、1 m-s-1 滑动速度、45 N 负载和 3000 m 滑动距离的组合显示出较低的 Ks 和 COF,分别为 0.44652 × 10-14 (m3-Nm-1) 和 0.136。利用方差分析对参数的显著性进行了评估,结果表明填料对耐磨性的影响很大。利用回归分析建立的数学模型和 K-Nearest Neighbors (KNN) 预测值与实验值显示出良好的一致性。采集的显微照片图像分析了磨损表面上明显的磨损机制,揭示了广泛的基体损伤、基体去除导致的纤维暴露和纤维断裂等失效机制。
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Adhesive wear characteristics of mono and hybrid CF/Ep composite with nano-HAP filler
Composites materials with more than two reinforcing materials are called hybrid composites.  Tailoring the composites by hybridizing fillers, fibers and matrix will yield better properties compared to mono-composites. Hence, an effort has been made in the current research work to develop carbon fiber epoxy hybrid nanocomposites, comprising different weight percentage of Hydroxyapatite (HAP) to evaluate the potential effects on tribological properties using two body sliding wear method. Taguchi technique (L27 array) has been adopted to investigate the impact of parameters such as filler inclusion (0%, 1.5%, and 3%), load (30, 45, and 60 N), sliding velocity (1, 2, and 3 m·s‒1) and distance (1000, 2000, and 3000 m·s‒1) on wear loss of developed composite. It was observed that the combination of 1.5 wt% HAP composite showed the lowest Ks and the COF. The combination of  1.5 wt% HAP filler, 1 m·s‒1 sliding velocity, 45 N load and 3000 m sliding distance exhibited the lower Ks and COF of 0.44652 × 10‒14 (m3·Nm‒1) and 0.136 respectively. The significance of the parameters was assessed using analysis of variance, revealing that the filler's contribution significantly impacted wear resistance. Developed mathematical model using Regression analysis and the predicted values from K-Nearest Neighbors (KNN) have showed good agreement with experimental values. Micrograph images were captured to analyze the wear mechanisms evident on worn surfaces, revealing failure mechanisms such as extensive matrix damage, fiber exposure resulting from matrix removal, and fiber breakage.
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