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首页> 外文期刊>Journal of Manufacturing Processes >Statistical and computational analysis of an environment-friendly MWCNT/ NiSO4 composite materials
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Statistical and computational analysis of an environment-friendly MWCNT/ NiSO4 composite materials

机译：环境友好型MWCNT / NISO4复合材料的统计和计算分析

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摘要

A brake pad material has been developed with multiwall carbon nanotube (MWCNT) and Nickel sulfate (NiSO4) to increase the heat transfer rate and better manufacturability. The maximum hardness of brake pad sample is found at 15 Ton compacting load and 150 degrees C sintering temperature. The performance of developed brake pad is compared with conventional brake pads. Scanning Electron Microscope (SEM) with Energy Dispersive X-Ray (EDX), high resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), gas sorption analyser (GSA) and thermal property analyser (TPA) are used to measure the distribution of elements, compound analysis, porosity distribution and thermal properties analysis respectively. The wear particles (mass loss) are less in the developed brake pad. The results show that developed brake pad material is superior compared to market available brake pads.An artificial neural network (ANN) model has been developed to predict the mass loss and coefficient of friction (CoF). The input parameters of 300 rpm, 50 N load and 125 degrees C temperature are the near optimal parameters combination for minimum wear and maximum CoF as observed by the design of experiment (DOE). The ANN and DOE results are verified with experimental findings. An ANSYS model has also been developed to study the temperature variation with time. The model has also compared with experimental results and found the 1% error.
机译：制动垫材料已经用多壁碳纳米管（MWCNT）和硫酸镍（NISO 4）开发，以增加传热速率和更好的可制造性。制动垫样品的最大硬度在15吨压实负载和150摄氏度的烧结温度下找到。与传统的制动衬块相比，开发的制动衬块的性能。扫描电子显微镜（SEM）具有能量分散X射线（EDX），高分辨率透射电子显微镜（HRTEM），X射线衍射（XRD），气体吸附分析仪（GSA）和热属性分析仪（TPA）测量分别分布元素，复合分析，孔隙率分布和热性能分析。磨损颗粒（质量损耗）在开发的制动衬块中较少。结果表明，与市场可用制动垫相比，开发的制动垫材料优越。已经开发了人工神经网络（ANN）模型来预测摩擦（COF）的质量损失和系数。输入参数为300rpm，50 n负载和125℃的温度是通过实验设计（DOE）设计的最小磨损和最大COF的近最佳参数组合。 ANN和DOE结果通过实验结果进行了验证。还开发了ANSYS模型以研究随时间的温度变化。该模型也与实验结果相比，发现了1％的错误。

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